Georgia Tech researchers are making AI easier to understand through their work on Transformer Explainer. The free, online tool shows non-experts how ChatGPT, Claude, and other large language models (LLMs) process language. 

Transformer Explainer is easy to use and runs on any web browser. It quickly went viral after its debut, reaching 150,000 users in its first three months. More than 563,000 people worldwide have used the tool so far.

Global interest in Transformer Explainer continues when the team presents the tool at the 2026 Conference on Human Factors in Computing Systems (CHI 2026). CHI, the world’s most prestigious conference on human-computer interaction, will take place in Barcelona, April 13-17.

[Related: GT @ CHI 2026]

“There are moments when LLMs can seem almost like a person with their own will and personality, and that misperception has real consequences. For example, there have been cases where teenagers have made poor decisions based on conversations with LLMs,” said Ph.D. student Aeree Cho.

“Understanding that an LLM is fundamentally a model that predicts the probability distribution of the next token helps users avoid taking its outputs as absolute. What you put in shapes what comes out, and that understanding helps people engage with AI more carefully and critically.”

A transformer is a neural network architecture that changes data input sequence into an output. Text, audio, and images are forms of processed data, which is why transformers are common in generative AI models. They do this by learning context and tracking mathematical relationships between sequence components.

Transformer Explainer demystifies how transformers work. The platform uses visualization and interaction to show, step by step, how text flows through a model and produces predictions.

Using this approach, Transformer Explainer impacts the AI landscape in four main ways:

• It counters hype and misconceptions surrounding AI by showing how transformers work.
• It improves AI literacy among users by removing technical barriers and lowering the entry for learning about AI.
• It expands AI education by helping instructors teach AI mechanisms without extensive setup or computing resources.
• It influences future development of AI tools and educational techniques by providing a blueprint for interpretable AI systems.

“When I first learned about transformers, I felt overwhelmed. A transformer model has many parts, each with its own complex math. Existing resources typically present all this information at once, making it difficult to see how everything fits together,” said Grace Kim, a dual B.S./M.S. computer science student. 

“By leveraging interactive visualization, we use levels of abstraction to first show the big picture of the entire model. Then users click into individual parts to reveal the underlying details and math. This way, Transformer Explainer makes learning far less intimidating.”

Many users don’t know what transformers are or how they work. The Georgia Tech team found that people often misunderstand AI. Some label AI with human-like characteristics, such as creativity. Others even describe it as working like magic.

Furthermore, barriers make it hard for students interested in transformers to start learning. Tutorials tend to be too technical and overwhelm beginners with math and code. While visualization tools exist, these often target more advanced AI experts.

Transformer Explainer overcomes these obstacles through its interactive, user-focused platform. It runs a familiar GPT model directly in any web browser, requiring no installation or special hardware. 

Users can enter their own text and watch the model predict the next word in real time. Sankey-style diagrams show how information moves through embeddings, attention heads, and transformer blocks.

The platform also lets users switch between high-level concepts and detailed math. By adjusting temperature settings, users can see how randomness affects predictions. This reveals how probabilities drive AI outputs, rather than creativity.

“Millions of people around the world interact with transformer-driven AI. We believe that it is crucial to bridge the gap between day-to-day user experience and the models' technical reality, ensuring these tools are not misinterpreted as human-like or seen as sentient,” said Ph.D. student Alex Karpekov. 

“Explaining the architecture helps users recognize that language generated by models is a product of computation, leading to a more grounded engagement with the technology.” 

Cho, Karpekov, and Kim led the development of Transformer Explainer. Ph.D. students Alec Helbling, Seongmin Lee, Ben Hoover, and alumni Zijie (Jay) Wang (Ph.D. ML-CSE 2024) and Minsuk Kahng (Ph.D. CS-CSE 2019) assisted on the project. 

Professor Polo Chau supervised the group and their work. His lab focuses on data science, human-centered AI, and visualization for social good.

Acceptance at CHI 2026 stems from the team winning the best poster award at the 2024 IEEE Visualization Conference. This recognition from one of the top venues in visualization research highlights Transformer Explainer’s effectiveness in teaching how transformers work.

“Transformer Explainer has reached over half a million learners worldwide,” said Chau, a faculty member in the School of Computational Science and Engineering. 

“I'm thrilled to see it extend Georgia Tech's mission of expanding access to higher education, now to anyone with a web browser.”

The Georgia Scientific Computing Symposium (GSCS) held its annual meeting on February 21 in Athens. Since 2009, the event has hosted researchers from across the Peach State to showcase homegrown advances in scientific computing.

The symposium highlighted Georgia’s reputation as a computing innovation hub. People from around the world come to Georgia universities to lead computing research. By advancing science, engineering, medicine, and technology, their work improves communities at home and abroad.

Faculty and students from Georgia Tech, UGA, Georgia State University, and Emory University presented at the symposium. Georgia Tech participants came from the colleges of Computing, Engineering, and Sciences.

This year’s organizers agreed to meet in Atlanta for the 2027 symposium. Georgia Tech’s School of Computational Science and Engineering (CSE) will host the 19th GSCS.

“From healthcare to computer chip design, scientific computing underpins many of the technological advances we see in our lives,” said Professor Edmond Chow, associate chair of the School of CSE.

“Scientific computing provides the mathematical models, simulations, and data‑driven tools that make modern innovation possible. It allows people to analyze complex systems, test ideas virtually before building them, and make faster, more accurate decisions across nearly every sector of society.”

Professor Haomin Zhou and Assistant Professor Helen Xu delivered two of the symposium’s five plenary talks. 

Zhou presented a new method for solving the Schrödinger equation, a landmark equation in quantum mechanics. Drawing inspiration from the mathematics used in generative artificial intelligence models, his approach develops an algorithm that more effectively simulates waves, particle motion, and other physical systems.

Xu focused on improving how computers move and organize data during complex calculations. Her work uses “cache-friendly” layouts that help computers access data more efficiently, boosting performance for scientific and engineering applications.

“Speaking at GSCS was a great opportunity,” Xu said. “The symposium fostered connections within the scientific computing community and gave us a chance to share exciting research.”

The symposium showcased student work through a poster blitz and a poster session. During the blitz, 36 students each had one minute to introduce their research to the full audience. They then shared more details about their research during the poster session.

The student projects showed the range of fields supported by scientific computing. The session also provided attendees with an opportunity to connect and expand their professional networks, helping grow the field’s future impact.

“As an aerospace engineer by training and aspiring computational scientist, GSCS gave me the platform to network with other researchers in the field while showcasing my own research,” said M.S. student Kashvi Mundra. 

“I was able to connect with scientists across different disciplines whose work intersects with my own in unexpected ways. Those conversations pushed my thinking beyond my own lab's perspective, helping me see my work on physics-informed machine learning for inverse problems in a broader scientific computing context.”

Georgia Tech students who presented posters included:

Abir Haque (CSE), Massively Parallel Random Phase Approximation Correlation Energy via Lanczos Quadrature

Antonio Varagnolo (CSE), Physics-Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation

Ben Burns (CSE), Infinite-Dimensional Stein Variational Inference with Derivative-Informed Neural Operators

Ben Wilfong (CSE), Shocks without Shock Capturing; Compressible Flow at 1 quadrillion Degrees of Freedom without Loss of Accuracy

Daniel Vickers (CSE), Highly-Parallel Fluid-Solid Interactions for Compressible Flows

Eric Fowler (CSE), High-Performance Tensor Contractions in Computational Chemistry

Haoran Yan (Math), Understanding Denoising Autoencoders through the Manifold Hypothesis: A Geometric Perspective

Kashvi Mundra (CSE), Autoregressive Multifidelity Neural Surrogate Modeling under Scarce Data Regimes

Sebastián Gutiérrez Hernández (Math/CSE), PDPO: Parametric Density Path Optimization

Vivian Zhang (AE), Multifidelity Operator Inference: Non-Intrusive Reduced Order Modeling from Scarce Data

Xian Mae Hadia (CSE), Data Efficiency of Surrogate Models: Learning Physics Data from Full Field Data vs. Inductive Bias from Approximate PDE Solvers

Xiangming Huang (CSE), Neural Operator Accelerated Evolutionary Strategies for PDE-Constraint Optimization

Zhaiming Shen (Math), Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods

Zhongjie Shi (Math), Towards Understanding Generalization in DP-GD: A Case Study in Training Two-Layer CNNs

Georgia Tech’s Qi Tang is building machine learning (ML) models to accelerate nuclear fusion research, making it more affordable and more accurate. Backed by a grant from the U.S. Department of Energy (DOE), Tang’s work brings clean, sustainable energy closer to reality.

Tang has received an Early Career Research Program (ECRP) award from the DOE Office of Science. The grant supports Tang with $875,000 disbursed over five years to craft ML and data processing tools that help scientists analyze massive datasets from nuclear experiments and simulations.

Tang is the first faculty member from Georgia Tech’s College of Computing and School of Computational Science and Engineering (CSE) to receive the ECRP. He is the seventh Georgia Tech researcher to earn the award and the only GT awardee among this year’s 99 recipients.

More than a milestone, the award reflects a shift in how nuclear research is done. Today, progress depends on computing and data science as much as on physics and engineering.

“I am honored and excited to receive the ECRP award through DOE’s Advanced Scientific Computing Research program, an organization I care about deeply,” said Tang, an assistant professor in the School of CSE. 

“I am grateful to my former colleagues at Los Alamos National Laboratory and collaborators at other national laboratories, including Lawrence Livermore, Sandia, and Argonne. I am also thankful for my Ph.D. students at Georgia Tech, whose dedication and creativity make this award possible.”

[Related: New Faculty Applies High-Performance Computing, Scientific Machine Learning Interests to Studies in Plasma Physics]

A problem in nuclear research is that fusion simulations are challenging to understand and use. These simulations generate enormous datasets that are too large to store, move, and analyze efficiently.

In his ECRP proposal to DOE, Tang introduced new ML methods to improve the analysis and storage of particle data.

Tang’s approach balances shrinking data so it is easier to store and transfer while preserving the most important scientific features. His multiscale ML models are informed by physics, so the reduced data still reflects how fusion systems really behave.

With Tang’s research, scientists can run larger, more realistic fusion models and analyze results more quickly. This accelerates progress toward practical fusion energy.

“In contrast to generic black-box-type compression tools, we aim at preserving the intrinsic structures of the particle dataset during the data reduction processes,” Tang said. 

“Taking this approach, we can meet our goal of achieving high-fidelity preservation of critical physics with minimum loss of information.”

Computing is essential in modern research because of the amount of data produced and captured from experiments and simulations. In the era of exascale supercomputers, data movement is a greater bottleneck than actual computation.

DOE operates three of the world’s four exascale supercomputers. These machines can calculate one quintillion (a billion billion) operations per second.

The exascale era began in 2022 with the launch of Frontier at Oak Ridge National Laboratory. Aurora followed in 2023 at Argonne National Laboratory. El Capitan arrived in 2024 at Lawrence Livermore National Laboratory.

With Tang’s data reduction approaches, all of DOE’s supercomputers spend more time on science and less time waiting for data transfers.

“Qi’s work in computational plasma physics and nuclear fusion modeling has been groundbreaking,” said Haesun Park, Regents’ Professor and Chair of the School of CSE. 

“We are proud of Qi and what this award means for him, Georgia Tech, and the Department of Energy toward leveraging computation to solve challenges in science and engineering, such as sustainable energy."

Previous Georgia Tech recipients of DOE Early Career Research Program awards include:

Itamar Kimchi, assistant professor, School of Physics

Sourabh Saha, assistant professor, George W. Woodruff School of Mechanical Engineering

Wenjing Lao, associate professor, School of Mathematics

Ryan Lively, Thomas C. DeLoach Professor, School of Chemical & Biomolecular Engineering

Josh Kacher, associate professor, School of Materials Science and Engineering

Devesh Ranjan, Eugene C. Gwaltney Jr. School Chair and professor, Woodruff School of Mechanical Engineering

Georgia Tech researchers say HPC and artificial intelligence (AI) advances this year are poised to improve how people power their homes, design safer buildings, and travel through cities.

According to Qi Tang, scientists will take progressive steps toward cleaner, sustainable energy through nuclear fusion in 2026. 

“I am very hopeful about the role of advanced computing and AI in making fusion a clean energy source,” said Tang, an assistant professor in the School of Computational Science and Engineering (CSE). 

“Fusion systems involve many interconnected processes happening across different scales. Modern simulations, combined with data-driven methods, allow us to bring these pieces together into a unified picture.”

Tang’s research connects HPC and machine learning with fusion energy and plasma physics. This year, Tang is continuing work on large-scale nuclear fusion models.

Only a few experimental fusion reactors exist worldwide compared to more than 400 nuclear fission reactors. Tang’s work supports a broader effort to turn fusion from a promising idea into a practical energy source.

Nuclear fusion occurs in plasma, the fourth state of matter, where gas is heated to millions of degrees. In this extreme state, electrons are stripped from atoms, creating a hot soup of fast-moving ions and free electrons. In plasma, hydrogen atoms overcome their natural electrical repulsion, collide, and fuse together. This releases energy that can power cities and homes.

Computers interpret extreme temperatures, densities, pressures, and plasma particle motion as massive datasets. Tang works to assimilate these data types from computer models and real-world experiments.

To do this, he and other researchers rely on machine learning approaches to analyze data across models and experiments more quickly and to produce more accurate predictions. Over time, this will allow scientists to test and improve fusion reactor designs toward commercial use. 

Beyond energy and nuclear engineering, Umar Khayaz sees broader impacts for HPC in 2026.

“HPC is the need of the day in every field of engineering sciences, physics, biology, and economics,” said Khayaz, a CSE Ph.D. student in the School of Civil and Environmental Engineering. 

“HPC is important enough to say that we need to employ resources to also solve social problems.”

Khayaz studies dynamic fracture and phase-field modeling. These areas explore how materials break under sudden, rapid loads. 

Like nuclear fusion, Khayaz says dynamic fracture problems are complex and data-intensive. In 2026, he expects to see more computing resources and computational capabilities devoted to understanding these problems and other emerging civil engineering challenges.

CSE Ph.D. student Yiqiao (Ahren) Jin sees a similar relationship between infrastructure and self-driving vehicles. He believes AI will innovate this area in 2026.

At Georgia Tech, Jin develops efficient multimodal AI systems. An autonomous vehicle is a multimodal system that uses camera video, laser sensors, language instructions, and other inputs to navigate city streets under changing scenarios like traffic and weather patterns.

Jin says multimodal research will move beyond performance benchmarks this year. This shift will lead to computer systems that can reason despite uncertainty and explain their decisions. In result, engineers will redefine how they evaluate and deploy autonomous systems in safety-critical settings.

“Many foundational problems in perception, multimodal reasoning, and agent coordination are being actively addressed in 2026. These advances enable a transition from isolated autonomous systems to safer, coordinated autonomous vehicle fleets,” Jin said. 

“As these systems scale, they have the potential to fundamentally improve transportation safety and efficiency.”

The College of Computing named Professor Rich Vuduc as director of the Center for Scientific Software Engineering (CSSE). The Georgia Tech hub is dedicated to building reliable, high-performance software for scientists.  

Under Vuduc’s leadership, CSSE strives to accelerate the pace and increase the quality of scientific discovery by developing custom software tools and best practices tailored to researchers’ needs.

“There is a reproducibility and reliability problem right now with scientific software,” Vuduc said. “The promise of CSSE is to leverage capabilities shared between Georgia Tech, Schmidt Sciences, and industry experts to address this problem.” 

Issues arise because scientists often need to develop their own software for experiments or data analysis. However, troubleshooting coding issues and other bugs can slow down research.

To assist these scientists, CSSE receives their input to create custom software tools and best practices. The center employs professional software engineers who build and deliver products tailor-made to the needs of researchers at Georgia Tech and broader scientific communities.

Beyond its research focus, CSSE helps Georgia Tech fulfill its educational mission. The center provides students with direct access and exposure to real-world software engineering.

As the center enters its third year, Vuduc wants to better prepare students for employment by enhancing their hands-on experience while learning from CSSE engineers.

To achieve this goal, Vuduc is working to establish a Ph.D. fellowship program in which CSSE engineers mentor students. This program would connect academic inquiry with industry expertise, creating the next generation of dynamic leaders in computational science.  

Vuduc also envisions pairing CSSE with Georgia Tech’s Vertically Integrated Projects (VIP) program. This approach would allow undergraduate students to earn class credit while working with CSSE engineers on large software engineering projects spanning multiple semesters.

“The center gives our students access to something that is very unique to find in a university environment,” Vuduc said. 

“The software engineers in CSSE mostly come from industry. They have over 65 years of combined experience doing real-world software engineering that students can learn from.”

Vuduc is a 2010 recipient of the Gordon Bell Prize and a leading expert in high-performance computing (HPC). He was a finalist for the award in 2020 and 2022.

The Gordon Bell Prize, often referred to as the Nobel Prize in supercomputing due to the scope and magnitude of research it recognizes, celebrates achievement in HPC research and application. 

Vuduc joined Georgia Tech in 2007 as one of the first faculty hired for the new Division of Computational Science and Engineering (CSE). Not a stranger of leading new units, he saw CSE begin offering M.S. and Ph.D. degrees in 2008 and attain school status in 2010.  

Since 2021, Vuduc has served as co-director of the Center for Research into Novel Computing Hierarchies (CRNCH). 

CRNCH is an interdisciplinary research center at Georgia Tech that explores technologies and approaches that will usher the next generation of computing. Areas CRNCH studies include quantum computing, brain-inspired computing, and approximate computing. 

Vuduc will step down as CRNCH co-director to fulfill his role as CSSE director. The College of Computing will lead a search for CRNCH’s next co-director.

“In a sense, the CRNCH to CSSE transition was partly a natural one because one thing that contributes to software challenges is that hardware platforms are also changing and evolving very rapidly,” said Vuduc. 

“People are exploring radically new hardware systems and we will have to write software configured for those too. Centers, like CRNCH and CSSE, strongly position Georgia Tech to lead these endeavors.” 

Alessandro (Alex) Orso, the previous CSSE director, departed Georgia Tech earlier this year to become dean of the University of Georgia’s College of Engineering. Orso and Distinguished Professor Irfan Essa wrote the proposal to bring CSSE to Georgia Tech.

Georgia Tech formed CSSE in 2022 after securing an $11 million grant from Schmidt Sciences. Former Google CEO Eric Schmidt and his spouse, Wendy Schmidt, founded the philanthropic venture that funds science and technology research and talent networking programs. 

Georgia Tech’s CSSE is part of Schmidt Sciences’ Virtual Institute for Scientific Software (VISS) program. This network helps scientists obtain more robust, flexible, scalable open-source software. 

Schmidt Sciences is investing $40 million in VISS over five years at four universities: Georgia Tech, University of Washington, Johns Hopkins University, and University of Cambridge.

CSSE uses the funding to employ a software engineering lead, three senior and two junior software engineers. The Schmidt Sciences grant equips these engineers with computing resources to build scientific software. Along with the director, an advisory board guides the group’s work to meet the point of need for scientists in the field. 

“I am grateful to Schmidt Sciences for their support of CSSE. It aligns with our college’s strategic goals and expertise in scientific software, and I am delighted that Rich has agreed to take on this important role,” said Vivek Sarkar, Dean and John P. Imlay Jr. Chair of Computing.

“I know that Rich is committed to growing CSSE's internal and external visibility and long-term sustainability. I am confident that he will also help further socialize CSSE among internal stakeholders across Georgia Tech.”

Despite their importance, most of what scientists know about proteins only comes from a small sample size. This stands in the way of fully understanding how most proteins work and unlocking their full potential.

Georgia Tech’s Yunan Luo believes artificial intelligence (AI) could fill this knowledge gap. The National Science Foundation agrees. Luo is the recipient of an NSF Faculty Early Career Development (CAREER) award. 

“So much of biology depends on knowing what proteins do, but decades of research have concentrated on a relatively small set of well-studied proteins. This imbalance in scientific attention leads to a distorted view of the biological landscape that quietly shapes our data and our algorithms,” Luo said.

“My group’s goal is to build machine learning (ML) models that actively close this gap by generating trustworthy function predictions for the many proteins that remain understudied.”

[Related: Yunan Luo to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant]

In his proposal to NSF, Luo coined this rich-get-richer effect “annotation inequality.” 

One problem of annotation inequality is that it slows progress in disease prognosis, drug discovery, and other critical biomedical areas. It is challenging to innovate the few proteins that scientists already know so much about. 

A cascading effect of annotation inequality is that it diminishes the effectiveness of studying proteins with AI.  

AI methods learn from existing experimental data. Datasets skewed toward well-known proteins propagate and become entrenched in models. Over time, this makes it harder for computers to research understudied proteins. 

“Protein annotation inequality creates an effect analogous to a vast library where 95% of patrons only read the top 5% popular books, leaving the rest of the collection to gather dust,” Luo said.

“This has resulted in knowledge disparities across proteins in current literature and databases, biasing our understanding of protein functions.”

The NSF CAREER award will fund Luo with over $770,000 for the next five years to tackle head-on the problem of protein annotation inequality.

Luo will use the grant to build an accurate, unbiased protein function prediction framework at scale. His project aims to:

• Reveal how annotation inequality affects protein function prediction systems
• Create ML techniques suited for biological data, which is often noisy, incomplete, and imbalanced  
• Integrate data and ML models into a scalable framework to accelerate discoveries involving understudied proteins

More enduring than the ML framework, Luo will leverage the NSF award to support educational and outreach programs. His goal is to groom the next generation of researchers to study other challenges in computational biology, not just the annotation inequality problem.

Luo teaches graduate and undergraduate courses focused on computational biology and ML. Problems and methods developed through the CAREER project can be used as course material in his classes.

Luo also championed collaboration with Georgia Tech’s Center for Education Integrating Science, Mathematics, and Computing (CEISMC) in his proposal. 

Through this partnership, local high school teachers and students would gain access to his data and models. This promotes deeper learning of biology and data science through hands-on experience with real-world tools.  

Luo sees reaching students and the community as a way of paying forward the support he received from Georgia Tech colleagues. 

“I am incredibly grateful for this recognition from the NSF,” said Luo, an assistant professor in the School of Computational Science and Engineering (CSE). 

“This would not have been possible without my students and collaborators, whose hard work laid the groundwork for this proposal.”

Luo praised CSE faculty members B. Aditya Prakash, Xiuwei Zhang, and Chao Zhang for their guidance. All three study machine learning and computational bioscience, two of CSE’s five core research areas. 

Luo also thanked Haesun Park for her support and recommendation for the CAREER award. Park is a Regents’ Professor and the chair of the School of CSE.

Though decorated with awards and appearances in leading journals, Zhang will achieve his greatest accomplishment tonight at McCamish Pavilion. He will join the Class of 2025 in walking across the stage, receiving diplomas, and graduating from Georgia Tech.

Before he “gets out” of Georgia Tech, we interviewed Zhang to learn more about his Ph.D. journey and where his degree will take him next. 

Graduate: Ziqi Zhang

Research Interests: Machine learning, foundational models, cellular mechanisms, single-cell gene sequencing, gene regulatory networks

Education: Ph.D. in Computational Science and Engineering

Faculty Advisor: School of CSE J.Z. Liang Early-Career Associate Professor Xiuwei Zhang

What persuaded you to study at Georgia Tech? 

I chose Georgia Tech because it is one of the top engineering institutions in the United States, known for its strength in machine learning and data science. The university offers exceptional research resources and the opportunity to work with leading scholars in my field. Georgia Tech also has very good research infrastructure. The Coda Building is one of the most well-designed and productive research environments I have experienced. Having access to such a space has been a genuine privilege.

How has working on your CSE degree helped you so far in your career?

Working toward my CSE degree has been instrumental in my career development. As an interdisciplinary program, CSE has equipped me with strong computational skills while also deepening my understanding of key application domains. This breadth of training has opened more opportunities during my job and internship searches. In addition, CSE community events, such as HotCSE, the weekly coffee hour, and faculty recruiting activities, have helped me strengthen my scientific communication skills, which are essential for my long-term career growth.

What research project from Georgia Tech are you most proud of?

My favorite research project was scMoMaT, a matrix tri-factorization algorithm for single-cell data integration. I invested a significant amount of time and effort into this work, iterating on the model many times. I’m very proud that it ultimately evolved into a clean, robust, and elegant algorithm.

What advice would you give someone interested in graduate school?

It is important to find an advisor who is supportive and genuinely invested in your career development. A Ph.D. is not an easy journey, and you will inevitably encounter challenges along the way. Having an advisor who can provide thoughtful guidance and dedicated mentorship is one of the most crucial factors in helping you navigate those difficulties.

What is your most favorite memory from Georgia Tech?

CSE’s new student campus visit day every year was one of my favorite times of the year. It was always fun to meet new people, have good food, and enjoy the beautiful view from the Coda rooftop.

What are your plans after graduation?

I plan to keep working in academia after graduation. I’m on the job hunt, currently applying for positions and preparing for interviews.

The Yellow Jacket community swarmed campus for the final time of the fall semester to celebrate Commencement ceremonies held Dec. 11 to 13. Graduates from the School of Computational Science and Engineering (CSE) were among the 7,177 new alumni “getting out” of Tech.     

“We are immensely proud of School of CSE and CSE programs graduates in the Class of 2025,” said Haesun Park, Regents’ Professor and Chair of the School of CSE.

“Our collaborative approach to CSE education has prepared these graduates to attain roles in academia, national labs, industry, government, and beyond, where they will lead the next generation of interdisciplinary research.”

Along with administering its flagship CSE Ph.D. and M.S. CSE programs, the School of CSE offers doctoral degrees in computer science and machine learning. Ph.D. graduates who received their diplomas and doctoral hoods on Dec. 11 at McCamish Pavilion included:

• Grant Bruer (Ph.D. CSE-CSE 2025), advised by School of CSE Professor and Associate Chair Edmond Chow
• Dongjin Choi (Ph.D. CSE-CSE 2025), advised by School of CSE Regents’ Professor and Chair Haesun Park
• Hyungu Choi (Ph.D. CSE-AE 2025), advised by Daniel Guggenheim School of Aerospace Engineering Regents’ Professor Dimitri Mavris
• Maxfield Comstock (Ph.D. CSE-CSE 2025), advised by Elizabeth Cherry, College of Computing Associate Dean for Graduate Education and School of CSE Associate Professor
• Andrew Hornback (Ph.D. CS-CSE 2025), co-advised by School of CSE Assistant Professor Yunan Luo and Wallace H. Coulter Department of Biomedical Engineering Professor May Wang
• Ayush Jain (Ph.D. CSE-MSE 2025), advised by School of Materials Science and Engineering Regents’ Entrepreneur and Professor Rampi Ramprasad
• Anurendra Kumar (Ph.D. CS-CSE 2025), co-advised by School of CSE J.Z. Liang Early Career Associate Professor Xiuwei Zhang and Wallace H. Coulter Department of Biomedical Engineering Professor Saurabh Sinha
• Jiajia Xie (Ph.D. CSE-BME 2025), advised by Wallace H. Coulter Department of Biomedical Engineering Associate Professor Cassie Mitchell
• Yuchen Zhuang (Ph.D. ML-CSE 2025), advised by School of CSE Edenfield Early Career Associate Professor Chao Zhang
• Ziqi Zhang (Ph.D. CSE-CSE 2025), advised by School of CSE J.Z. Liang Early Career Associate Professor Xiuwei Zhang

Seven CSE Ph.D. students completed M.S. degrees this fall and will continue their studies at Georgia Tech. They are:

• Jesus Arias (M.S. CSE-CSE 2025), advised by School of CSE Assistant Professor Spencer Bryngelson
• Isabel Berry (M.S. CSE-CHEM 2025), advised by Regents’ Professor C. David Sherrill, who is jointly appointed with the School of Chemistry and Biochemistry and the School of CSE
• Max Hawkins (M.S. CSE-CSE 2025), co-advised by School of CSE Professor Rich Vuduc and Assistant Professor Spencer Bryngelson
• Xiao Jing (M.S. CSE-AE 2025), advised by Daniel Guggenheim School of Aerospace Engineering Regents’ Professor Dimitri Mavris
• Haoyun Li (M.S. CSE-CSE 2025), advised by Professor Felix Herrmann, who is jointly appointed with the Schools of Earth and Atmospheric Sciences, Electrical and Computer Engineering, and CSE
• Yuan Qiu (M.S. CSE-CSE 2025), advised by School of CSE Assistant Professor Peng Chen
• William Schertzer (M.S. CSE-MSE 2025), advised by School of Materials Science and Engineering Regents’ Entrepreneur and Professor Rampi Ramprasad

Georgia Tech’s CSE graduate program includes 12 schools and departments participating as home units. These home units represent the colleges of Computing, Engineering, and Sciences. This approach facilitates an immersive, interdisciplinary experience in which students study computational approaches within domain fields.

Georgia Tech jointly celebrated master’s graduates at a ceremony on Dec. 13 at Bobby Dodd Stadium. After the Institute celebration, graduates were recognized during ceremonies held by their respective colleges.

Mawutor Kofi Amanfu (M.S. CSE 2025)

Sunyoung An (M.S. CSE 2025)

Nischal Bandi (M.S. CSE 2025)

Elijah Bellamy (M.S. CSE 2025)

Meiwen Bi (M.S. CSE 2025)

Hao-Cheng Chang (M.S. CSE 2025)

Tianyu Chen (M.S. CSE 2025)

Yilong Chen (M.S. CSE 2025)

Zhiyu Chen (M.S. CSE 2025)

Seung Eun Choi (M.S. CSE 2025)

Vinodhini Comandur (M.S. CSE 2025)

Zhiyi Dai (M.S. CSE 2025)

Alejandro Danies-Lopez (M.S. CSE 2025)

Zixing Fan (M.S. CSE 2025)

Stefan Faulkner (M.S. CSE 2025)

Mihiri Fernando (M.S. CSE 2025)

Alexandra Freeman (M.S. CSE 2025)

Yuhan Fu (M.S. CSE 2025)

Jack Ganem (M.S. CSE 2025)

Omar Atef Garib (M.S. CSE 2025)

Martin Graffigna (M.S. CSE 2025)

Bochun Guo (M.S. CSE 2025)

Moyi Guo (M.S. CSE 2025)

Xinyu Guo (M.S. CSE 2025)

Yuqi Han (M.S. CSE 2025)

Tianyang Hu (M.S. CSE 2025)

Mingzheng Huang (M.S. CSE 2025)

Po-Han Huang (M.S. CSE 2025)

Wentao Jiang (M.S. CSE 2025)

Boxiao Jin (M.S. CSE 2025)

William-Michael Johnson (M.S. CSE 2025)

Garyoung Lee (M.S. CSE 2025)

Tzu Jung Lee (M.S. CSE 2025)

Congyan Li (M.S. CSE 2025)

Peiru Li (M.S. CSE 2025)

Yuhan Li (M.S. CSE 2025)

Zhiyun Liang (M.S. CSE 2025)

Yuexi Liao (M.S. CSE 2025)

Chenyu Liu (M.S. CSE 2025)

Honglin Liu (M.S. CSE 2025)

Shuojiang Liu (M.S. CSE 2025)

Xuanzhang Liu (M.S. CSE 2025)

Yue Lu (M.S. CSE 2025)

Fang Lunt (M.S. CSE 2025)

Jinrui Ma (M.S. CSE 2025)

Yu Miao (M.S. CSE 2025)

Hui-Chun Mo (M.S. CSE 2025)

Prajwal Kumar (M.S. CSE 2025)

Kavya Krishnan (M.S. CSE 2025)

Felicity Nielson (M.S. CSE 2025)

Jonathan Perng (M.S. CSE 2025)

Yinzhu Quan (M.S. CSE 2025)

Devanshi Shah (M.S. CSE 2025)

Yuxuan Shen (M.S. CSE 2025)

Steven Stewart (M.S. CSE 2025)

Linjun Su (M.S. CSE 2025)

Jingyun Sun (M.S. CSE 2025)

Abdul Rehman Tariq (M.S. CSE 2025)

Yu Chu Tsai (M.S. CSE 2025)

Xunzhi Wen (M.S. CSE 2025)

Jinghua Weng (M.S. CSE 2025)

Andi Xia (M.S. CSE 2025)

Zihao Xiao (M.S. CSE 2025)

Yunxiang Yan (M.S. CSE 2025)

Ziyuan Ye (M.S. CSE 2025)

Linyuan Yu (M.S. CSE 2025)

Bingqing Zhang (M.S. CSE 2025)

Tiankuo Zhang (M.S. CSE 2025)

Yu Zheng (M.S. CSE 2025)

Boye Zhou (M.S. CSE 2025)

Xinjie Zhu (M.S. CSE 2025)

Zilu Zhu (M.S. CSE 2025)

Raphaël Pestourie has earned back-to-back selections to the Institute’s Course Instructor Opinion Survey (CIOS) honor roll, placing him among the top-ranked teachers for Fall 2024 and Spring 2025.

By returning to the classroom this semester to teach two more courses, Pestourie continues to leverage proven experience to mentor the next generation of researchers in his field.

“Students played a very important part in the survey process, and I thank them for making the classes great,” said Pestourie, an assistant professor in the School of Computational Science and Engineering (CSE).

“I'm incredibly grateful that students shared their feedback so that I could go the extra mile to not only apply my expertise to teach in ways that I think work, but transform my instruction to reach students in the most impactful way I can.”

CIOS honor rolls recognize instructors for outstanding teaching and educational impact, based on student feedback provided through end-of-course surveys. 

Student praise of Pestourie’s CSE 8803: Scientific Machine Learning class placed him on the Fall 2024 CIOS honor roll. He earned selection to the Spring 2025 honor roll for his instruction of CX 4230: Computer Simulation. 

CSE 8803 is a graduate-level, special topics class that Pestourie created around his field of expertise. Scientific machine learning involves merging two traditionally distinct fields: scientific computing and machine learning.

In scientific computing, researchers build and use models based on established physical laws. Machine learning differs in that it employs data-driven models to find patterns without prior assumptions. Combining the two fields opens new ways to analyze data and solve challenging problems in science and engineering.

Pestourie organized student-focused scientific machine learning symposiums in Fall 2023 and 2024. CSE 8803 students work on projects throughout the course and present their work at these symposiums. Pestourie will use the same approach this semester. 

Compared to CSE 8803, CX 4230 is an undergraduate course that teaches students how to create computer models of complex systems. A complex system has many interacting entities that influence each other’s behaviors and patterns. Disease spread in a human network is one example of a complex system. 

CX 4230 is a required course for computer science students studying the Modeling & Simulation thread. It is also an elective course in the Scientific and Engineering Computing minor.  

“I see 8803 as my educational baby. Being acknowledged for it with a CIOS honor roll felt great,” Pestourie said. 

“In a way, I'm prouder of CX 4230 because it was a large, undergraduate regular offering that I was teaching for the first time. The honor roll selection came almost as a surprise.”

To be eligible for the honor roll recognition, instructors must have a minimum CIOS response rate of 70%. Composite scores for three CIOS items are then used to rank instructors. Those items are:

• Instructor’s respect and concern for students
• Instructor’s level of enthusiasm about the course
• Instructor’s ability to stimulate interest in the subject matter

Georgia Tech’s Center for Teaching and Learning (CTL) and the Office of Academic Effectiveness present the CIOS Honor Rolls. CTL recognizes honor roll recipients at its Celebrating Teaching Day events, held annually in March.

CTL offers the Class of 1969 Teaching Fellowship, in which Pestourie participated in the 2024-2025 cohort. The program aims to broaden perspectives with insight into evidence-based best practices and exposure to new and innovative teaching methods.

The fellowship offers one-on-one consultations with a teaching and learning specialist. Cohorts meet weekly in the fall semester and monthly in the spring semester for instruction seminars. 

The fellowship facilitates peer observations where instructors visit other classrooms, exchange feedback, and learn effective techniques to try in their own classes.

“I'm very grateful for the Class of 1969 fellowship program and to Karen Franklin, who coordinates it,” Pestourie said. “The honor roll is not just a one-person award. Support from the Institute and other people in the program made it happen.”

Like in Fall 2023 and 2024, Pestourie is teaching CSE 8803: Scientific Machine Learning again this semester. Additionally, he teaches CSE 8801: Linear Algebra, Probability, and Statistics.

Linear algebra and applied probability are among the fundamental subjects in modern data science. Like his scientific machine learning class, Pestourie created CSE 8801. This semester marks the second time Pestourie is teaching the course since Fall 2024.

Pestourie designed CSE 8801 as a refresher course for newer graduate students. This addresses a point of need to help students get off to a good start at Georgia Tech. By offering guidance early in their graduate careers, Pestourie’s work in the classroom also aims to cultivate future collaborators and serve his academic community.

“I see teaching as our one shot at making a good first impression as a research field and a community,” he said. 

“I see my work as a teacher as training my future colleagues, and I see it as my duty to our community to do my best in attracting the best talent toward our research field.”

In November 2024, JSC granted Assistant Professor Spencer Bryngelson exclusive access to the system through the JUPITER Research and Early Access Program (JUREAP).

By preparing Europe’s fastest supercomputer for launch, the joint project yielded valuable simulation data on the effects of shock waves in medicine and transportation.

“The shock-droplet problem has been a hallmark test problem in fluid dynamics for some decades now. It is sufficiently challenging to study that it keeps me scientifically interested, though the results are manifestly important,” Bryngelson said. 

“Understanding the droplet behavior in some extreme regimes remains an open scientific problem of high engineering value.”

Through JUREAP, JSC engineers tested Bryngelson’s Multi-Component Flow Code (MFC) on their computers. The project simulated how liquid droplets behave when struck by a large, high-velocity shock wave moving much faster than the speed of sound.

Tests produced visualizations of droplets deforming into pancake shapes before ejecting vortex rings as they broke apart from the shock wave. The experiments measured the swirls of air flow formed behind the droplets, known as vorticity.

Vorticity is one variable aerospace engineers consider when building aircraft designed to fly at supersonic and hypersonic speeds. Small droplets and vortices pose significant hazards for high-Mach vessels.

These computer models reduce the risk and cost associated with physical test runs. By simulating extreme scenarios, the JUREAP project demonstrated a safer and more efficient way to evaluate aerospace systems.

The human body is another fluid space where fast, high-energy flows can occur.

Simulations help medical researchers create less invasive shock wave treatments. This technology can be further applied for uses ranging from breaking up kidney stones to treating inflammation. 

MFC’s versatility for large- and small-scale applications made it suitable for testing JUPITER in its early stages. The project’s success even earned it a JUREAP certificate for scaling efficiency and node performance.

“The use of application codes to test supercomputers is common. We’ve participated in similar programs for OLCF Frontier and LLNL El Capitan,” said Bryngelson, a faculty member with Georgia Tech’s School of Computational Science and Engineering.

“Engineers at supercomputer sites usually find and sort most problems on their own. But deploying workloads characteristic of what the JUPITER will run in practice stresses it in new ways. In these instances, we usually end up identifying some failure modes.”

The JSC and Georgia Tech researchers named their joint project Exascale Multiphysics Flows (ExaMFlow).

ExaMFlow helps keep JUPITER on pace to become Europe’s first exascale supercomputer. This designation refers to any machine capable of computing one exaflop, or one quintillion (“1” followed by 18 zeros) calculations per second. 

All three systems that rank ahead of JUPITER are exascale supercomputers. They are El Capitan at Lawrence Livermore National Laboratory, Frontier at Oak Ridge National Laboratory, and Aurora at Argonne National Laboratory. 

JUPITER calculates more than 60 billion operations per watt. This makes the supercomputer the most energy-efficient system among the top five. 

ExaMFlow ran Bryngelson’s software on JSC’s JUWELS Booster and JUPITER Exascale Transition Instrument (JETI). The two modules form the backbone of JUPITER’s full design.

ExaMFlow’s report showed that MFC performed with near-ideal scaling behavior on JUWELS and JETI compared to similar systems based on NVIDIA A100 GPUs.

Access to NVIDIA hardware at Georgia Tech played a key role in ExaMFlow’s success.

The Institute hosts the Phoenix Research Computing Cluster, which includes A100 GPUs among its arsenal of components. Bryngelson’s lab owns NVIDIA A100 GPUs and four GH200 Grace Hopper Superchips. 

Since JUPITER is equipped with around 24,000 Grace Hopper Superchips, Bryngelson’s work with the hardware proved especially insightful for the ExaMFlow project.   

“The Grace Hopper chip is interesting. It’s not challenging to use like a regular GPU device when one is familiar with running NVIDIA hardware. The more fun part is using its tightly coupled CPU to GPU interconnect to make use of the CPU as well,” Bryngelson said. 

“It’s not immediately obvious how to best do this, though we used a few tricks to tune its use to our application. They appear to work nicely.”

JSC researchers Luis Cifuentes, Rakesh Sarma, Seong Koh, and Sohel Herff played important roles in running Bryngelson’s MFC software on early JUPITER modules. 

The ExaMFlow team included NVIDIA scientists Nikolaos Tselepidis and Benedikt Dorschner. 

The pair observed their company’s hardware used in the field. They return to NVIDIA with notes that help the corporation build the next devices tailored to the need of scientific computing researchers. 

“We try to be prepared for the latest, biggest computers. Being able to take immediate advantage of the largest systems is a valuable capability,” Bryngelson said. 

“When the early access systems arrive, it’s a great opportunity for the teams involved to test the machines, demonstrate and tune scientific software, and meet very capable new collaborators.”

One of these scientists is Nabil Imam, an assistant professor at Georgia Tech’s School of Computational Science and Engineering (CSE). Not only does Imam study the possibilities of brain-inspired computing, he also thinks the answer to this engineering question lies in the same parts of the brain that let us see our world.

Imam posits that by modeling the human visual cortex in computer hardware and software, computers can become both more efficient and more powerful. If achieved, this idea could transform the future of computer manufacturing and programing.

Imam presented his research observations Feb. 3 at a summit hosted by Georgia Tech’s Center for Research in Novel Compute Hierarchies (CRNCH). Imam’s presentation included an overview of the neural circuits within the brain, simulations that have modeled the visual cortex, and designs for silicon chips that emulate brain architecture.

The highlight of Imam’s seminar was his presentation of a microchip architecture he and other researchers have designed to function like the brain’s visual cortex. The system Imam presented has simulated hundreds of millions of neurons and tens of billions of synapses, a step toward making brain-inspired computing a reality. 

“Multi-chip systems have been built using these chips to simulate 100 million neurons and 25 billion synapses in real time,” Imam said. “These chips are very efficient platforms for simulating biological neural networks.” 

To open his seminar, Imam showed circuit connectivity and neural response properties of the primary visual cortex – an area of the brain that is involved in seeing the world.

The primary visual cortex is one of the most extensively studied areas of the brain. It consists of six layers of brain cells totaling about 300 million neurons and 300 billion synapses in primates. Even though this is one of the most well studied areas of the brain, the sheer numbers and complexity involved still make it difficult to understand.  

Imam then discussed computer simulations of the visual system during his presentation. The example Imam used simulates a visual cortex segment of 230,000 neurons, a small sliver of the structure but one that shows promise.

“This is a very small simulation, but it is a very detailed one. The models are based on extensive data curated from years of measurements,” said Imam. “With the right kind of computing platform, we can scale this up and simulate larger portions of the circuit and its interactions with other areas of the brain.”

Due to the sheer computing power required for brain simulations, scaling is a significant obstacle that researchers, like Imam, are studying to overcome.

Simulating one second of a small neural circuit requires hours of computing time. With current computer architectures, it would require speeds measured in exaflops and memory spanning petabytes to achieve a simulation of the human brain.

Presently, the max speed of the world’s fastest supercomputer, called Frontier, is 1.102 exaflops. It is the first, and currently, the only computer to reach exascale speeds. To do this, Frontier requires 7,300 square feet of space, consumes 21 megawatts of power, and pumps 6,000 gallons of water a minute to keep itself cool. This shows how far computers still must go before being able to simulate the brain.

However, progress is being made. The chip Imam discussed at CRNCH Summit 2023 uses specialized integrated circuits to model neurons and their networks found in the brain and visual cortex.

To address the scaling challenge, the computer chips were scaled up via multi-chip platforms to simulate hundreds of millions of neurons and tens of billions of synapses, approaching scales of complex cortical circuitry.

Simulations in this chip are orders of magnitude larger than the 230,000-neuron example simulation Imam presented earlier. 

These chips operate in real time, so one second of brain activity equals one second of computing. These chips are also smaller than a square inch and consume less than one watt of power. The chips also can be integrated with sensors and actuators to interact with the environment. 

Much of Imam’s research remains theoretical and ongoing work continues. But as Imam showed in his CRNCH Summit 2023 seminar, the intersection of computing and neuroscience is rapidly growing and the future for this technology appears bright.

“The goal is to develop computational and analytical methods that will help us understand the behavior of these models” Imam said. “These insights can then be used to develop new classes of computer systems and new models of computation.”

We caught up with Holly and Andrew to learn more about what makes Georgia Tech so appealing to students and employees alike, and the unique perspective their mother-son relationship brings to campus. 

How long have you worked at Georgia Tech? What do you do as a financial operations assistant director?

[Holly] I have been at Georgia Tech since 2000 and joined the College of Computing in early 2005. Ironically and very memorably, I found out I was expecting Andrew right after I started in Computing. I still recall being very nervous about telling my new boss that I was expecting, but she was happy for me and very accommodating. I went to the bookstore right after our meeting and bought Andrew his first Georgia Tech t-shirt, which I plan to pass down to him one day. 

Being part of the College for this long, I have watched it grow from having divisions to schools. I began as a financial administrator and steadily grew into my current role as assistant director of financial operations for the School of Computational Science and Engineering (CSE). In my current role, I oversee financial operations for the School of CSE, including budgeting, forecasting, financial reporting, and ensuring compliance with Institute and sponsor guidelines. I work closely with faculty, staff, and leadership to support our financial programs and other initiatives. It is a rewarding role that allows me to contribute to the success of the college while watching it grow, just like my own journey here. 

How much influence did your mom’s work at Georgia Tech have on your interest in coming here for college?

[Andrew] It definitely played a role. I grew up a fan of all things Georgia Tech, so this was always my dream school. When I got in, all the stars aligned. Tech was my dream school, my mom worked there, and I was close to home. It was perfect.  

What is it about Georgia Tech that has kept you here for more than two decades?

[Holly] From the beginning, I have always felt that my work contributes to something bigger – supporting faculty who offer world-class education and innovation. I have had the opportunity to grow professionally and work alongside very talented colleagues. 

How has your first year gone? What do you like best about attending Georgia Tech?

[Andrew] Overwhelming. I wasn’t sure what to expect from attending college. The college lifestyle was a big change for me, and I had to learn how to navigate it. I knew Georgia Tech would be challenging, and I thought I was ready for it, but it was still harder than I expected. However, I managed to get through my first year with great resilience and finish stronger than I started.  

What do you each like best about being on campus together?

[Holly] I enjoy being nearby and getting to share this chapter of his life. There are many times that Andrew is focused on his studies or his fraternity commitments, so he doesn’t get to come home as often as I would like. But with me being on campus, we can grab a quick lunch or have a short visit. I also like knowing he is a short walk from my office if I want to drop off a homemade meal. Even when we are both too busy for a visit, sometimes I find myself looking out the window across campus. Just seeing the top of a building where he is attending class gives me comfort knowing he is there.

[Andrew] Bouncing off of what my mom said, it has been great. It is comforting that I can go to her office just to say “hey,” and not drive all the way home. It’s nice that when I’m having a rough patch with school, she drops by to offer some reassurance.

What has been the key to reaching the milestones of a fulfilling career and witnessing your children achieve their goals?

[Holly] The key to reaching these milestones has really been maintaining a healthy work-life balance. Georgia Tech has given me the space and support to grow professionally while also being present as a parent. That balance has been everything. It allowed me to build a fulfilling career that I’m proud of, while also being there to watch my children grow and pursue their dreams, including Andrew becoming a Georgia Tech student himself. Being able to do both, without having to choose one over the other, has truly been the foundation of my success and happiness.  

What do you look forward to in the next few years studying computer engineering at Georgia Tech? And after graduating?

[Andrew] I am most excited for my threads and starting my career. I have learned so much that I am already applying my studies. I recently used Raspberry Pi to build a circuit that enabled a motion detector to work and set off an LED indicating motion. This was a part of my discovery class for my major, and it was the most fun I have had at Georgia Tech so far. It was very new and exciting to learn about, and it motivates me to put my skills to work.

After graduating, I want to work in cybersecurity, possibly as a hardware security engineer for the government or even my own startup. The project I mentioned opened my eyes to my threads and really motivated me to continue in this field. 

Rush is one of more than 150 College of Computing staff members who support the College and its five schools. Staff members are the backbone of the College. From managing operations to providing essential services, their dedication ensures the seamless delivery of education, research, and community support, making them integral to the College's success.

Ph.D. candidate Md Abdur Rahaman’s dissertation studies brain data to understand how changes in brain activity shape behavior. 

Computational tools Rahaman developed for his dissertation look for informative patterns between the brain and behavior. Successful tests of his algorithms show promise to help doctors diagnose mental health disorders and design individualized treatment plans for patients.

“I've always been fascinated by the human brain and how it defines who we are,” Rahaman said. 

“The fact that so many people silently suffer from neuropsychiatric disorders, while our understanding of the brain remains limited, inspired me to develop tools that bring greater clarity to this complexity and offer hope through more compassionate, data-driven care.”

Rahaman’s dissertation introduces a framework focusing on granular factoring. This computing technique stratifies brain data into smaller, localized subgroups, making it easier for computers and researchers to study data and find meaningful patterns.

Granular factoring overcomes the challenges of size and heterogeneity in neurological data science. Brain data is obtained from neuroimaging, genomics, behavioral datasets, and other sources. The large size of each source makes it a challenge to study them individually, let alone analyze them simultaneously, to find hidden inferences. 

Rahaman’s research allows researchers and physicians to move past one-size-fits-all approaches. Instead of manually reviewing tests and scans, algorithms look for patterns and biomarkers in the subgroups that otherwise go undetected, especially ones that indicate neuropsychiatric disorders.

“My dissertation advances the frontiers of computational neuroscience by introducing scalable and interpretable models that navigate brain heterogeneity to reveal how neural dynamics shape behavior,” Rahaman said. 

“By uncovering subgroup-specific patterns, this work opens new directions for understanding brain function and enables more precise, personalized approaches to mental health care.”

Rahaman defended his dissertation on April 14, the final step in completing his Ph.D. in computational science and engineering. He will graduate on May 1 at Georgia Tech’s Ph.D. Commencement. 

After walking across the stage at McCamish Pavilion, Rahaman’s next step in his career is to go to Amazon, where he will work in the generative artificial intelligence (AI) field. 

Graduating from Georgia Tech is the summit of an educational trek spanning over a decade. Rahaman hails from Bangladesh where he graduated from Chittagong University of Engineering and Technology in 2013. He attained his master’s from the University of New Mexico in 2019 before starting at Georgia Tech. 

“Munna is an amazingly creative researcher,” said Vince Calhoun, Rahman’s advisor. Calhoun is the founding director of the Translational Research in Neuroimaging and Data Science Center (TReNDS).

TReNDS is a tri-institutional center spanning Georgia Tech, Georgia State University, and Emory University that develops analytic approaches and neuroinformatic tools. The center aims to translate the approaches into biomarkers that address areas of brain health and disease.    

“His work is moving the needle in our ability to leverage multiple sources of complex biological data to improve understanding of neuropsychiatric disorders that have a huge impact on an individual’s livelihood,” said Calhoun.

Driskill came to Georgia Tech in 2023 as a graduate transfer to run long-distance for the women’s cross country and track teams. In doing so, she became the first-ever student-athlete in the School of Computational Science and Engineering (CSE). 

While a pioneer in her own right, the trails that Driskill blazed will be her legacy at Georgia Tech and the School of CSE.

“We are so proud of Grace and so happy she came our way for graduate school,” said Alan Drosky, head coach of Georgia Tech’s cross country and track and field teams.

“She is obviously very talented academically and athletically, and she has contributed in a major way to our teams in competition.” 

[Related: Day in the Life of a CSE Student-Athlete]

This year, Driskill recorded the fourth fastest time in history of the Georgia Tech Women's Indoor Track program. She clocked a 9:22.21 in the 3000-meter race at the David Hemery Valentine Invitational, held on Feb. 15 at Boston University.

Driskill’s record-setting performance at the Valentine Invitational came days after news of her latest academic achievement. She was among 19 Yellow Jackets selected to the 2024 All-ACC Cross Country Academic Team.

This listing was Driskill’s third after previous selections in 2024 for indoor track and 2023 for cross country. She is on pace for a 2025 indoor track selection, putting her on four All-ACC academic teams during her Georgia Tech career. 

To earn an All-ACC academic team selection, student-athletes must attain a 3.0 grade point average in the previous semester and maintain an overall 3.0 cumulative average. Student-athletes must also compete in the ACC and/or NCAA championships during the most recent season.

In 2024, Driskill’s academic achievement earned her the Donald V. Jackson Fellowship. Georgia Tech’s College of Computing presents the award annually to well-rounded, first-year master’s students. The College selects fellows who best embody Jackson’s academic excellence and leadership values.

Driskill was instrumental in helping the women’s cross country team earn high rankings at the NCAA South Regionals both years she competed. 

Driskill was the second-fastest Yellow Jacket and finished 34th overall at the 2023 regional, earning Georgia Tech 6th place among a field of 29 teams. At last year’s regional, she helped Georgia Tech place 7th out of 31 teams by finishing 41st overall and fourth from Georgia Tech.

“Last year, I qualified for the preliminary round of the track national championship,” Driskill said, remembering her proudest accomplishment at Georgia Tech. 

“I love representing Georgia Tech at every competition throughout the season, but the opportunity to do it at a higher level and more prominent competition was extra special.”

A native of Tucson, Arizona, Driskill intends to return to the Southwest after graduating from Georgia Tech this summer. She will work with the Technical Internships to Advance National Security program (TITANS) at Sandia National Laboratories in Albuquerque, New Mexico.

Driskill started her collegiate career in 2020, competing on the cross country and track teams at the University of Arizona. She graduated in 2023 with a B.S. in computer science and a minor in mathematics. 

In high school, Driskill was a four-year cross country letter winner and a two-year letter winner in track and field and softball. She earned first-team all-state honors her senior year in 2019 after state champion finishes in the 1600 and 3200-meter events.

“Grace’s contributions go way beyond what she does while running. She has a fantastic attitude, an easy-going demeanor, and a great sense of humor,” Drosky said. “She has become an integral figure on our teams and will be missed.”

The Society for Industrial and Applied Mathematics (SIAM) selected Elizabeth Cherry and Katya Scheinberg as Class of 2025 fellows. The two Georgia Tech faculty join an illustrious class of 23 other researchers from around the globe in this year’s class. 

SIAM selected Cherry to recognize her contributions to mathematical and computational modeling and extensive service to the SIAM community. She studies the electrical behavior of cardiac cells and tissue.

Cherry’s computer models and simulations improve understanding of cardiac dynamics in normal and diseased states. Using these tools, she designs advanced strategies for preventing and treating arrhythmias.

“SIAM has played a huge role in my professional development—the first conference I attended as a graduate student was a SIAM conference, and I’ve attended at least one SIAM conference almost every year since then,” Cherry said. 

“Given this long history, it means a lot to me for SIAM to acknowledge my contributions in this way.”

Scheinberg, from Georgia Tech’s College of Engineering, was selected for her foundational contributions to derivative-free optimization and optimization applications in data science and her dedicated service to the optimization community.

[Related: Coca-Cola Foundation Chair Katya Scheinberg selected for 2025 Class of SIAM Fellows]

Cherry is the fifth faculty member from the School of Computational Science and Engineering (CSE) to be selected as a SIAM Fellow.

Cherry’s announcement as a SIAM Fellow comes weeks after serving in a leadership role at a SIAM conference. She co-chaired the organizing committee of the SIAM Conference on Computational Science and Engineering (CSE25).

In 2023, SIAM members reelected Cherry to a second term as a council member-at-large. She began her three-year term in January 2024.

"SIAM Fellows are selected for deep mathematical contributions. Receiving Fellow status is a high honor for any applied mathematician," said Regents’ Professor Srinivas Aluru, senior associate dean of the College of Computing and Class of 2020 SIAM Fellow. 

"Not only are Elizabeth's contributions technically outstanding, but her work also provides deep insights into the functioning of the heart and its abnormalities."

Cherry’s leadership and service extends outside of SIAM, influencing students and faculty across Georgia Tech. 

In December, the College of Computing appointed Cherry as associate dean for graduate education. Before this appointment, she served as associate chair for academic affairs of the School of CSE. 

With her new role as associate dean, Cherry continues serving as director of CSE programs at Georgia Tech. 

In March 2024, Cherry was among five Georgia Tech faculty members selected for the ACC Academic Leaders Network (ACC ALN) Fellows program. The ALN program fosters cross-institutional networking and collaboration between ACC schools, increasing each institution’s academic leadership capacity.

Cherry was part of a team of Georgia Tech and Emory University researchers who won a Georgia Clinical and Translational Science Alliance award in 2023. The group earned the Team Science Award of Distinction for Early Stage Research Teams award for work that captures high-resolution visualizations of spiral waves that create heart arrhythmias.

SIAM will recognize Cherry, Scheinberg, and Class of 2025 fellows during a reception at the SIAM/CAIMS Annual Meetings this July in Montréal.

“It is such an honor to be recognized as a SIAM Fellow,” Cherry said. “I’m thrilled to join my CSE colleagues who have also received this recognition.”

“In this past year, my first as the dean of computing, I have seen exactly how much work it takes from everyone to keep this community going, not to mention excelling,” said Vivek Sarkar, dean and John P. Imlay Jr. Chair of the College of Computing. 

“We are strong across the board, and that makes our winners all the more impressive.”

The School of Computational Science and Engineering (CSE) is one unit that reinforces the College’s emphasis on collaboration, problem solving, and excellence. By earning awards this year at the College, Institute, and levels beyond, the School of CSE continues to distinguish itself as a top-tier department for research and learning.

Select award winners from the School of CSE recognized at this year’s banquet were:

• Professor Polo Chau- Dean’s Award
• Pratham Mehta, M.S. CS student- The Donald V. Jackson Fellowship
• Parisa Babolhavaeji- The Marshall D. Williamson Fellowship
• Aeree Cho, Ph.D. student- Rising Star Doctoral Student Research Award
• Alumnus Zijie (Jay) Wang (Ph.D. ML-CSE 2024)- Outstanding Doctoral Dissertation Award

The College of Computing also recognized awardees with ties to the School of CSE. These included: 

• Lecturer and alumnus Max Roozbahani (Ph.D. CSE 2019)- William A. "gus" Baird Faculty Teaching Award. Instructor of the online section of CSE6242: Data and Visual Analytics.
• Lecturer and alumnus Nimisha Roy (Ph.D. CSE 2021)- William D. "Bill" Leahy Jr. Outstanding Instructor Award
• Teaching Assistant Susanta Routray- Outstanding Instructional Associate Teaching Award. Co-head TA of the online section of CSE6242: Data and Visual Analytics.

Chau teaches the CSE6242 course, and advises Babolhavaeji, Cho, Mehta, and Wang. Along with the College of Computing awards, Chau received the Innovator’s Award at the M.S. Analytics Ten Year Anniversary. He has served as the program’s associate director since 2014 and over 1,000 students have taken his data and visual analytics course each semester in recent years.

Along with receiving the College of Computing’s dissertation, Wang received a 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI).

SIGCHI is the world’s largest association of human-computer interaction professionals and practitioners. Wang is one of five recipients of the award this year.

Earlier in the year, Forbes recognized Wang by naming him to its 30 Under 30 in Science for 2025. 

Wang’s dissertation earned him the 2025 Best Ph.D. Thesis Award from the Georgia Tech Sigma Chi chapter. 

At the same ceremony, Sigma Chi presented Regents’ Professor Mark Borodovsky with the Best Faculty Paper Award for his work on GeneMark-ETP. Borodovsky holds joint appointments with the School of CSE and the Wallace H. Coulter Department of Biomedical Engineering.

Trailblazing work in biocomputing earned Regents’ Professor Srinivas Aluru the 2025 Charles Babbage Award. The Institute of Electrical and Electronics Engineers Computer Society (IEEE CS) presented the award for Aluru’s pioneering contributions intersecting parallel computing and computational biology.

News of Aluru’s Babbage Award arrived at the same time the College of Computing announced the appointments of associate deans. The College appointed Aluru as senior associate dean, and Associate Professor Elizabeth Cherry became associate dean for graduate education.

Aluru and Cherry’s appointments marked the first time in the School’s history that faculty represented the School as associate deans. 

Aluru ended his role as executive director of Georgia Tech’s Institute for Data Engineering and Science (IDEaS) when he accepted the senior associate dean role. In his place, IDEaS appointed Regents’ Professor C. David Sherrill as interim executive director. 

Sherrill holds joint appointments with CSE and the School of Chemistry and Biochemistry. He has served as associate director of IDEaS since its founding in 2016. His appointment as interim executive director comes after his election to the International Academy of Quantum Molecular Science (IAQMS).

Cherry's appointment as associate dean was one of many accolades she received in 2025. In March, the Society for Industrial and Applied Mathematics (SIAM) selected her as a Class of 2025 Fellow, recognizing her contributions to computational cardiology research and extensive service to the SIAM community. Cherry is the fifth faculty member from the School of CSE selected as a SIAM Fellow.

Cherry co-chaired the organizing committee for the SIAM Conference on Computational Science and Engineering (CSE25). She is also serving a second consecutive term as a SIAM council member-at-large.

Members of the SIAM Activity Group on Computational Science and Engineering (SIAG/CSE) elected School of CSE Professor and Associate Chair Edmond Chow as vice chair. Chow’s two-year term began in January after serving as the group’s program director.

Cherry previously served as the School of CSE’s associate chair for academic affairs. When she accepted her new associate dean role, the School appointed B. Aditya Prakash as associate chair.

Prakash was one of three School of CSE faculty members who received promotions that take effect in July. He was promoted to full professor. Assistant Professors Chao Zhang and Xiuwei Zhang earned tenure and promotions. Each has been promoted to associate professor.

Prakash advised Alexander Rodríguez (Ph.D. CS 2023), now an assistant professor at the University of Michigan. Rodríguez won an outstanding dissertation award runner-up at the International Conference on Knowledge Discovery and Data Mining (KDD 2024).

Rodríguez’s dissertation on Artificial Intelligence for Data-centric Surveillance and Forecasting of Epidemics earned him the College of Computing's Outstanding Dissertation Award in 2024.

Assistant Professor Florian Schäfer co-authored a paper selected for one of five best technical paper awards at the annual conference for ACM’s Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH 24). 

Schäfer’s work in numerical computation and statistical inference led to his appointment as an initiative lead within Georgia Tech’s Institute for Matter and Systems (IMS). IMS selected Schäfer to lead the initiative on Matter and Information, looking to him to facilitate innovative approaches and impact in alignment with IMS’ mission.

Assistant Professor Spencer Bryngelson and his group received an ACCESS-CI Maximize allocation from the National Science Foundation. The award amounts to 225,000 GPU hours annually to run their multiphase fluid flow simulation algorithms on powerful supercomputers.

One of Bryngelson’s Ph.D. students, Ben Wilfong, received the 2024-2025 CRNCH Fellowship. Wilfong will use the fellowship to optimize superchip architectures, such as NVIDIA Grace Hopper and AMD MI300A.

Early in the year, Suzan Manasreh and Elizabeth Hong won President’s Undergraduate Research Awards (PURA) for Fall 2024. Manasreh studies in Bryngelson’s group, and Professor Rich Vuduc advises Hong. 

M.S. CSE student Grace Driskill attained achievement in the classroom, on the track, and cross country courses. The first-ever School of CSE student-athlete earned a third selection to an All-ACC academic team.

Driskill made history by recording the fourth fastest 3000-meter time in history of the Georgia Tech Women’s Indoor Track program. She clocked a 9:22.21 on Feb. 15 at Boston University’s David Hemery Valentine Invitational.

Students praised Assistant Professor Raphaël Pestourie, who was selected for the Fall 2024 CIOS Honor Roll. The honor roll recognized Pestourie for outstanding teaching and educational impact through his CSE 8803: Scientific Machine Learning course.

In the waning weeks of the semester, CSE-AE Ph.D. student Atticus Rex received the NSF Graduate Research Fellowship Program (GRFP) award for computational and data-enabled science research. Rex is advised by Assistant Professor Elizabeth Qian, who holds joint appointments with the School of CSE and the Daniel Guggenheim School of Aerospace Engineering.

In March, the American Institute of Mining, Metallurgical and Petroleum Engineers (AIME) awarded honorary membership to Regents’ Professor Surya Kalidindi. Kalidindi is affiliated with the School of CSE, the George W. Woodruff School of Mechanical Engineering, and the School of Materials Science and Engineering.

Wang is a recipient of the 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI). The award recognizes Wang for his lifelong work on democratizing human-centered AI.

“Throughout my Ph.D. and industry internships, I observed a gap in existing research: there is a strong need for practical tools for applying human-centered approaches when designing AI systems,” said Wang, now a safety researcher at OpenAI.

“My work not only helps people understand AI and guide its behavior but also provides user-friendly tools that fit into existing workflows.”

[Related: Georgia Tech College of Computing Swarms to Yokohama, Japan, for CHI 2025]

Wang’s dissertation presented techniques in visual explanation and interactive guidance to align AI models with user knowledge and values. The work culminated from years of research, fellowship support, and internships.

Wang’s most influential projects formed the core of his dissertation. These included:

• CNN Explainer: an open-source tool developed for deep-learning beginners. Since its release in July 2020, more than 436,000 global visitors have used the tool.
• DiffusionDB: a first-of-its-kind large-scale dataset that lays a foundation to help people better understand generative AI. This work could lead to new research in detecting deepfakes and designing human-AI interaction tools to help people more easily use these models.
• GAM Changer: an interface that empowers users in healthcare, finance, or other domains to edit ML models to include knowledge and values specific to their domain, which improves reliability.
• GAM Coach: an interactive ML tool that could help people who have been rejected for a loan by automatically letting an applicant know what is needed for them to receive loan approval.
• Farsight: a tool that alerts developers when they write prompts in large language models that could be harmful and misused.  

“I feel extremely honored and lucky to receive this award, and I am deeply grateful to many who have supported me along the way, including Polo, mentors, collaborators, and friends,” said Wang, who was advised by School of Computational Science and Engineering (CSE) Professor Polo Chau.

“This recognition also inspired me to continue striving to design and develop easy-to-use tools that help everyone to easily interact with AI systems.”

Like Wang, Chau advised Georgia Tech alumnus Fred Hohman (Ph.D. CSE 2020). Hohman won the ACM SIGCHI Outstanding Dissertation Award in 2022.

Chau’s group synthesizes machine learning (ML) and visualization techniques into scalable, interactive, and trustworthy tools. These tools increase understanding and interaction with large-scale data and ML models. 

Chau is the associate director of corporate relations for the Machine Learning Center at Georgia Tech. Wang called the School of CSE his home unit while a student in the ML program under Chau.

Wang is one of five recipients of this year’s award to be presented at the 2025 Conference on Human Factors in Computing Systems (CHI 2025). The conference occurs April 25-May 1 in Yokohama, Japan. 

SIGCHI is the world’s largest association of human-computer interaction professionals and practitioners. The group sponsors or co-sponsors 26 conferences, including CHI.

Wang’s outstanding dissertation award is the latest recognition of a career decorated with achievement.

Months after graduating from Georgia Tech, Forbes named Wang to its 30 Under 30 in Science for 2025 for his dissertation. Wang was one of 15 Yellow Jackets included in nine different 30 Under 30 lists and the only Georgia Tech-affiliated individual on the 30 Under 30 in Science list.

While a Georgia Tech student, Wang earned recognition from big names in business and technology. He received the Apple Scholars in AI/ML Ph.D. Fellowship in 2023 and was in the 2022 cohort of the J.P. Morgan AI Ph.D. Fellowships Program.

Along with the CHI award, Wang’s dissertation earned him awards this year at banquets across campus. The Georgia Tech chapter of Sigma Xi presented Wang with the Best Ph.D. Thesis Award. He also received the College of Computing’s Outstanding Dissertation Award.

“Georgia Tech attracts many great minds, and I’m glad that some, like Jay, chose to join our group,” Chau said. “It has been a joy to work alongside them and witness the many wonderful things they have accomplished, and with many more to come in their careers.”

As a competitive runner, Driskill holds the fourth-fastest 3000-meter time in the history of Georgia Tech’s women's indoor track program. Off the track, Driskill is a three-time All-ACC Academic team honoree on the women’s cross-country and track teams, and in 2024, she received the College of Computing’s Donald V. Jackson Fellowship. 

We caught up with Driskill to learn more about her and how she balances academic and athletic responsibilities. 

[Related: Computing Student Runs into History Books for Athletic and Academic Achievement]

Student: Grace Driskill

Current Degree Program: M.S. in Computational Science and Engineering

Research Interests: Computational biology, computing applications toward health

Hometown: Tucson, Arizona

Where are you from, and how did you get to Georgia Tech?

I grew up in Tucson, Arizona. I attended the University of Arizona, earning a bachelor’s degree in computer science while running on the track and field team. When I finished my undergraduate, I still had two seasons of eligibility to run track. I looked into master’s programs so that I could continue my athletic career and expand my horizons academically. 

The cards fell into place for me to come here. Georgia Tech has a great reputation as a school for computing and engineering. Additionally, the cross country and women’s track coach, Alan Drosky, is very experienced and knowledgeable in coaching distance runners.

You are the first ever student-athlete in the School of CSE’s history. What does this mean to you? What does it mean for the CSE program?

I didn’t realize I was the first student-athlete in the School of CSE, but I think it’s pretty cool. It is meaningful to me, and hopefully, others, because it goes against the thought that you need to fit into some typical mold to be a student-athlete or to study CSE. 

CSE is a challenging degree for any student, let alone a student-athlete. What interested you about the program in the first place?

I think the CSE program at Georgia Tech is unique; not many colleges in the U.S. offer a master’s degree in the topic. I like the interdisciplinary nature of it. I enjoyed studying computer science during my undergraduate, but I thought leaning more into computing applications was an interesting opportunity.

Could you describe an average day for you as a CSE student-athlete?

After eating some breakfast and having a coffee, the first thing I typically do is go for an easy run that might be 3-8 miles. Then, I go to class or do homework for a few hours, making sure to take a break for lunch. 

At 3:30 p.m., I have practice with the team. This typically includes a higher-intensity run and exercises to practice form or help with strength and mobility. After practice, I usually stretch and take an ice bath for recovery. I typically get home around 6-6:30. Then, I’ll eat dinner and maybe do some more work, but I try to get to bed at a decent hour. 

What CSE research areas interest you most and why?

Computational biology and applications in healthcare. Health affects everyone throughout their lives, so it is an important topic. Additionally, there are many applications and different angles to use computing in the field.

What do you do to relax or for fun?

Currently, I have been watching the TV show Severance. A new episode comes out weekly, so getting together with friends is something relaxing to look forward to. I also like to cook. When I have some free time, I enjoy taking a bit more time while cooking dinner to make something exciting or different.

What is your proudest achievement at Georgia Tech? 

Last year, I qualified for the preliminary round of the track national championship. I love representing Georgia Tech at every competition throughout the season, but the opportunity to do it at a higher level and in a more prominent competition was extra special.

What are your plans after completing your M.S.?

After graduation, I will work at Sandia National Labs in their Technical Internships to Advance National Security (TITANS) program. 

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

In experiments predicting medium-range weather forecasting and shallow water wave propagation, Latent-EnSF demonstrated higher accuracy, faster convergence, and greater efficiency than existing methods for sparse data assimilation.

“We are currently involved in an NSF-funded project aimed at providing real-time information on extreme flooding events in Pinellas County, Florida,” said Si, who studies computational science and engineering (CSE). 

“We're actively working on integrating Latent-EnSF into the system, which will facilitate accurate and synchronized modeling of natural disasters. This initiative aims to enhance community preparedness and safety measures in response to flooding risks.” 

Latent-EnSF outperformed three comparable models in assimilation speed, accuracy, and efficiency in shallow water wave propagation experiments. These tests show models can make better and faster predictions of coastal flood waves, tides, and tsunamis. 

In experiments on medium-range weather forecasting, Latent-EnSF surpassed the same three control models in accuracy, convergence, and time. Additionally, this test demonstrated Latent-EnSF's scalability compared to other methods.

These promising results support using ML models to simulate climate, weather, and other complex systems.

Traditionally, such studies require employment of large, energy-intensive supercomputers. However, advances like Latent-EnSF are making smaller, more efficient ML models feasible for these purposes.

The Georgia Tech team mentioned this comparison in its paper. It takes hours for the European Center for Medium-Range Weather Forecasts computer to run its simulations. Conversely, the ML model FourCastNet calculated the same forecast in seconds.

“Resolution, complexity, and data-diversity will continue to increase into the future,” said Chen, an assistant professor in the School of CSE. 

“To keep pace with this trend, we believe that ML models and ML-based data assimilation methods will become indispensable for studying large-scale complex systems.”

Data assimilation is the process by which models continuously ingest new, real-world data to update predictions. This data is often sparse, meaning it is limited, incomplete, or unevenly distributed over time. 

Latent-EnSF builds on the Ensemble Filter Scores (EnSF) model developed by Florida State University and Oak Ridge National Laboratory researchers. 

EnSF’s strength is that it assimilates data with many features and unpredictable relationships between data points. However, integrating sparse data leads to lost information and knowledge gaps in the model. Also, such large models may stop learning entirely from small amounts of sparse data.

The Georgia Tech researchers employ two variational autoencoders (VAEs) in Latent-EnSF to help ML models integrate and use real-world data. The VAEs encode sparse data and predictive models together in the same space to assimilate data more accurately and efficiently.

Integrating models with new methods, like Latent-EnSF, accelerates data assimilation. Producing accurate predictions more quickly during real-world crises could save lives and property for communities.

[Related: University of South Florida Researchers Track Flooding in Coastal Communities During Hurricanes Helene and Milton]

To share Latent-EnSF to the broader research community, Chen and Si presented their paper at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organized CSE25, held March 3-7 in Fort Worth, Texas.

Chen was one of ten School of CSE faculty members who presented research at CSE25, representing one-third of the School’s faculty body. Latent-EnSF was one of 15 papers by School of CSE authors and one of 23 Georgia Tech papers presented at the conference.

The pair will also present Latent-EnSF at the upcoming International Conference on Learning Representations (ICLR 2025). Occurring April 24-28 in Singapore, ICLR is one of the world’s most prestigious conferences dedicated to artificial intelligence research.

“We hope to bring attention to experts and domain scientists the exciting area of ML-based data assimilation by presenting our paper,” Chen said. “Our work offers a new solution to address some of the key shortcomings in the area for broader applications.”

Students and faculty from the School of Computational Science and Engineering (CSE) are leading the Georgia Tech contingent at the SIAM Conference on Computational Science and Engineering (CSE25). The Society of Industrial and Applied Mathematics (SIAM) organizes CSE25, occurring March 3-7 in Fort Worth, Texas.

At CSE25, the School of CSE researchers are presenting papers that apply computing approaches to varying fields, including:                   

• Experiment designs to accelerate the discovery of material properties
• Machine learning approaches to model and predict weather forecasting and coastal flooding
• Virtual models that replicate subsurface geological formations used to store captured carbon dioxide
• Optimizing systems for imaging and optical chemistry
• Plasma physics during nuclear fusion reactions

[Related: GT CSE at SIAM CSE25 Interactive Graphic] 

“In CSE, researchers from different disciplines work together to develop new computational methods that we could not have developed alone,” said School of CSE Professor Edmond Chow. 

“These methods enable new science and engineering to be performed using computation.” 

CSE is a discipline dedicated to advancing computational techniques to study and analyze scientific and engineering systems. CSE complements theory and experimentation as modes of scientific discovery. 

Held every other year, CSE25 is the primary conference for the SIAM Activity Group on Computational Science and Engineering (SIAG CSE). School of CSE faculty serve in key roles in leading the group and preparing for the conference.

In December, SIAG CSE members elected Chow to a two-year term as the group’s vice chair. This election comes after Chow completed a term as the SIAG CSE program director. 

School of CSE Associate Professor Elizabeth Cherry has co-chaired the CSE25 organizing committee since the last conference in 2023. Later that year, SIAM members reelected Cherry to a second, three-year term as a council member at large. 

At Georgia Tech, Chow serves as the associate chair of the School of CSE. Cherry, who recently became the associate dean for graduate education of the College of Computing, continues as the director of CSE programs. 

“With our strong emphasis on developing and applying computational tools and techniques to solve real-world problems, researchers in the School of CSE are well positioned to serve as leaders in computational science and engineering both within Georgia Tech and in the broader professional community,” Cherry said. 

Georgia Tech’s School of CSE was first organized as a division in 2005, becoming one of the world’s first academic departments devoted to the discipline. The division reorganized as a school in 2010 after establishing the flagship CSE Ph.D. and M.S. programs, hiring nine faculty members, and attaining substantial research funding.

Ten School of CSE faculty members are presenting research at CSE25, representing one-third of the School’s faculty body. Of the 23 accepted papers written by Georgia Tech researchers, 15 originate from School of CSE authors.

The list of School of CSE researchers, paper titles, and abstracts includes:
Bayesian Optimal Design Accelerates Discovery of Material Properties from Bubble Dynamics
Postdoctoral Fellow Tianyi Chu, Joseph Beckett, Bachir Abeid, and Jonathan Estrada (University of Michigan), Assistant Professor Spencer Bryngelson
[Abstract]

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data
Ph.D. student Phillip Si, Assistant Professor Peng Chen
[Abstract]

A Goal-Oriented Quadratic Latent Dynamic Network Surrogate Model for Parameterized Systems
Yuhang Li, Stefan Henneking, Omar Ghattas (University of Texas at Austin), Assistant Professor Peng Chen
[Abstract]

Posterior Covariance Structures in Gaussian Processes
Yuanzhe Xi (Emory University), Difeng Cai (Southern Methodist University), Professor Edmond Chow
[Abstract]

Robust Digital Twin for Geological Carbon Storage
Professor Felix Herrmann, Ph.D. student Abhinav Gahlot, alumnus Rafael Orozco (Ph.D. CSE-CSE 2024), alumnus Ziyi (Francis) Yin (Ph.D. CSE-CSE 2024), and Ph.D. candidate Grant Bruer
[Abstract]

Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models
Rafael Orozco, Ph.D. student Tuna Erdinc, alumnus Mathias Louboutin (Ph.D. CS-CSE 2020), and Professor Felix Herrmann
[Abstract]

Optimizing Coupled Systems: Insights from Co-Design Imaging and Optical Chemistry
Assistant Professor Raphaël Pestourie, Wenchao Ma and Steven Johnson (MIT), Lu Lu (Yale University), Zin Lin (Virginia Tech)
[Abstract]

Multifidelity Linear Regression for Scientific Machine Learning from Scarce Data
Assistant Professor Elizabeth Qian, Ph.D. student Dayoung Kang, Vignesh Sella, Anirban Chaudhuri and Anirban Chaudhuri (University of Texas at Austin)
[Abstract]

LyapInf: Data-Driven Estimation of Stability Guarantees for Nonlinear Dynamical Systems
Ph.D. candidate Tomoki Koike and Assistant Professor Elizabeth Qian
[Abstract]

The Information Geometric Regularization of the Euler Equation
Alumnus Ruijia Cao (B.S. CS 2024), Assistant Professor Florian Schäfer
[Abstract]

Maximum Likelihood Discretization of the Transport Equation
Ph.D. student Brook Eyob, Assistant Professor Florian Schäfer
[Abstract]

Intelligent Attractors for Singularly Perturbed Dynamical Systems
Daniel A. Serino (Los Alamos National Laboratory), Allen Alvarez Loya (University of Colorado Boulder), Joshua W. Burby, Ioannis G. Kevrekidis (Johns Hopkins University), Assistant Professor Qi Tang (Session Co-Organizer)
[Abstract]

Accurate Discretizations and Efficient AMG Solvers for Extremely Anisotropic Diffusion Via Hyperbolic Operators
Golo Wimmer, Ben Southworth, Xianzhu Tang (LANL), Assistant Professor Qi Tang 
[Abstract]

Randomized Linear Algebra for Problems in Graph Analytics
Professor Rich Vuduc
[Abstract]

Improving Spgemm Performance Through Reordering and Cluster-Wise Computation
Assistant Professor Helen Xu
[Abstract]

“This is a very well-deserved recognition for Srinivas as he joins the illustrious list of past recipients of the Charles Babbage Award,” said Vivek Sarkar, the John P. Imlay Jr. Dean of the College of Computing.

“Srinivas’ accomplishments reflect positively on himself and all of us at Georgia Tech. This is indeed an occasion to celebrate.”

The IEEE Computer Society presents the Babbage Award annually. The award recognizes significant contributions to parallel computation. 

[Related: IEEE-CS interview with Aluru on his award-winning career]

The award is named after Charles Babbage, widely considered to be a “father of the computer.” Babbage and Ada Lovelace are credited with inventing the first mechanical computers in the 19th century, eventually leading to more complex designs.

Aluru is a pioneer in computational genomics, an area of biology that studies the order, structure, function, and evolution of genetic material. Throughout his career, his lab has developed software and algorithms to analyze the genomes of several species of plants, animals, and microorganisms.

Genome base pair sizes can number into the billions, which can be interpreted as massive datasets. Ever since the early years of his career, Aluru championed parallel computing as a practical approach to studying these challenging datasets. 

Parallelism divides a large problem into smaller ones, allowing different processors on a computer to solve the simpler tasks simultaneously. This approach breaks a genome into smaller segments, allowing computers to efficiently transcribe genetic code and identify insightful patterns. 

“Srinivas Aluru’s groundbreaking contributions have profoundly shaped the intersection of parallel processing and bioinformatics. His work is nothing short of extraordinary,” said Yves Robert, awards chair of the IEEE Computer Society Babbage Committee. 

“It is a privilege to recognize a researcher whose work will undoubtedly have a lasting impact for generations to come.”

IEEE selected Aluru as a fellow in 2010, and he recently served as the editor-in-chief of the journal IEEE/ACM Transactions on Computational Biology and Bioinformatics. 

Aluru has fellowships with the American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Society of Industrial and Applied Mathematics. He is a past recipient of the NSF CAREER Award, IBM Faculty Award, and the Swarnajayanti Fellowship from the government of India.

Along with receiving the Babbage Award, Aluru’s leadership acumen earned him the recent appointment as senior associate dean of Georgia Tech’s College of Computing. 

Aluru helped form the Institute for Data Engineering and Science (IDEaS) at Georgia Tech in 2016, serving as co-executive director. Later, he became the institute’s sole executive director from 2019 to 2025. Regents’ Professor C. David Sherrill became interim executive director of IDEaS when Aluru accepted his associate dean appointment.  

Aluru started at Georgia Tech in 2013 to join the new School of Computational Science and Engineering, established in 2010. He served as the School’s interim chair from 2019 to 2020. In 2023, the University System of Georgia appointed Aluru as Regents’ Professor.

Aluru completed his Ph.D. at Iowa State University in 1994. He then worked at Ames National Laboratory, Syracuse University, and New Mexico State University before returning to his alma mater from 1999 to 2013.

“This award is a recognition of over two and a half decades of research efforts in my group, reflecting not only my work but that of numerous graduate students and collaborators,” said Aluru. 

“I hope the award draws attention to the importance of parallel methods in computational biology and points key advancements to new entrants in the field.”

A team of researchers from the College of Computing at Georgia Tech has developed a framework for assessing the capabilities of large language models (LLMs).

Ph.D. students Mohit Chandra and Yiqiao (Ahren) Jin are the co-lead authors of the paper Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries. 

Their paper’s findings reveal a gap between LLMs and their ability to answer health-related questions. Chandra and Jin point out the limitations of LLMs for users and developers but also highlight their potential. 

Their XLingEval framework cautions non-English speakers from using chatbots as alternatives to doctors for advice. However, models can improve by deepening the data pool with multilingual source material such as their proposed XLingHealth benchmark.     

“For users, our research supports what ChatGPT’s website already states: chatbots make a lot of mistakes, so we should not rely on them for critical decision-making or for information that requires high accuracy,” Jin said.   

“Since we observed this language disparity in their performance, LLM developers should focus on improving accuracy, correctness, consistency, and reliability in other languages,” Jin said. 

Using XLingEval, the researchers found chatbots are less accurate in Spanish, Chinese, and Hindi compared to English. By focusing on correctness, consistency, and verifiability, they discovered: 

• Correctness decreased by 18% when the same questions were asked in Spanish, Chinese, and Hindi. 
• Answers in non-English were 29% less consistent than their English counterparts. 
• Non-English responses were 13% overall less verifiable. 

XLingHealth contains question-answer pairs that chatbots can reference, which the group hopes will spark improvement within LLMs.  

The HealthQA dataset uses specialized healthcare articles from the popular healthcare website Patient. It includes 1,134 health-related question-answer pairs as excerpts from original articles.  

LiveQA is a second dataset containing 246 question-answer pairs constructed from frequently asked questions (FAQs) platforms associated with the U.S. National Institutes of Health (NIH).  

For drug-related questions, the group built a MedicationQA component. This dataset contains 690 questions extracted from anonymous consumer queries submitted to MedlinePlus. The answers are sourced from medical references, such as MedlinePlus and DailyMed.   

In their tests, the researchers asked over 2,000 medical-related questions to ChatGPT-3.5 and MedAlpaca. MedAlpaca is a healthcare question-answer chatbot trained in medical literature. Yet, more than 67% of its responses to non-English questions were irrelevant or contradictory.  

“We see far worse performance in the case of MedAlpaca than ChatGPT,” Chandra said. 

“The majority of the data for MedAlpaca is in English, so it struggled to answer queries in non-English languages. GPT also struggled, but it performed much better than MedAlpaca because it had some sort of training data in other languages.” 

Ph.D. student Gaurav Verma and postdoctoral researcher Yibo Hu co-authored the paper. 

Jin and Verma study under Srijan Kumar, an assistant professor in the School of Computational Science and Engineering, and Hu is a postdoc in Kumar’s lab. Chandra is advised by Munmun De Choudhury, an associate professor in the School of Interactive Computing. 
 
The team will present their paper at The Web Conference, occurring May 13-17 in Singapore. The annual conference focuses on the future direction of the internet. The group’s presentation is a complimentary match, considering the conference's location.  

English and Chinese are the most common languages in Singapore. The group tested Spanish, Chinese, and Hindi because they are the world’s most spoken languages after English. Personal curiosity and background played a part in inspiring the study. 

“ChatGPT was very popular when it launched in 2022, especially for us computer science students who are always exploring new technology,” said Jin. “Non-native English speakers, like Mohit and I, noticed early on that chatbots underperformed in our native languages.” 

School of Interactive Computing communications officer Nathan Deen and School of Computational Science and Engineering communications officer Bryant Wine contributed to this report.

“I am excited about this fellowship because there are so many people at Georgia Tech using AI to create social impact,” said Wang, an assistant professor in the School of Computational Science and Engineering (CSE).

“I feel so fortunate to be part of this community and to help Georgia Tech bring more impact on society.”

AI2050 has allocated up to $5.5 million to support the cohort. Fellows receive up to $300,000 over two years and will join the Schmidt Sciences network of experts to advance their research in artificial intelligence (AI).

Wang’s AI2050 project centers on leveraging decision-focused AI to address challenges facing health and environmental sustainability. His goal is to strengthen and deploy decision-focused AI in collaboration with stakeholders to solve broad societal problems.

Wang’s method to decision-focused AI integrates machine learning with optimization to train models based on decision quality. These models borrow knowledge from decision-making processes in high-stakes domains to improve overall performance.

Part of Wang’s approach is to work closely with non-profit and non-governmental organizations. This collaboration helps Wang better understand problems at the point-of-need and gain knowledge from domain experts to custom-build AI models.   

“It is very important to me to see my research impacting human lives and society,” Wang said. That reinforces my interest and motivation in using AI for social impact.”

[Related: Wang, New Faculty Bolster School’s Machine Learning Expertise]

This year’s cohort is only the second in the fellowship’s history. Wang joins a class that spans four countries, six disciplines, and seventeen institutions.

AI2050 commits $125 million over five years to identify and support talented individuals seeking solutions to ensure society benefits from AI. Last year’s AI2050 inaugural class of 15 early career fellows received $4 million.

The namesake of AI2050 comes from the central motivating question that fellows answer through their projects:

It’s 2050. AI has turned out to be hugely beneficial to society. What happened? What are the most important problems we solved and the opportunities and possibilities we realized to ensure this outcome?

AI2050 encourages young researchers to pursue bold and ambitious work on difficult challenges and promising opportunities in AI. These projects involve research that is multidisciplinary, risky, and hard to fund through traditional means.

Schmidt Sciences, LLC is a 501(c)3 non-profit organization supported by philanthropists Eric and Wendy Schmidt. Schmidt Sciences aims to accelerate and deepen understanding of the natural world and develop solutions to real-world challenges for public benefit.

Schmidt Sciences identify under-supported or unconventional areas of exploration and discovery with potential for high impact. Focus areas include AI and advanced computing, astrophysics and space, biosciences, climate, and cross-science.

“I am most grateful for the advice from my mentors, colleagues, and collaborators, and of course AI2050 for choosing me for this prestigious fellowship,” Wang said. “The School of CSE has given me so much support, including career advice from junior and senior level faculty.”

B. Aditya Prakash is part of a research group that will host a workshop on how topics from computational epidemiology can enhance computer science classes.

These lessons would produce computer science graduates with improved skills in data science, modeling, simulation, artificial intelligence (AI), and machine learning (ML). 

Because epidemics transcend the sphere of public health, these topics would groom computer scientists versed in issues from social, financial, and political domains. 

The group’s virtual workshop takes place on March 20 at the technical symposium for the Special Interest Group on Computer Science Education (SIGCSE). SIGCSE is one of 38 special interest groups of the Association for Computing Machinery (ACM). ACM is the world’s largest scientific and educational computing society. 

“We decided to do a tutorial at SIGCSE because we believe that computational epidemiology concepts would be very useful in general computer science courses,” said Prakash, an associate professor in the School of Computational Science and Engineering (CSE). 

“We want to give an introduction to concepts, like what computational epidemiology is, and how topics, such as algorithms and simulations, can be integrated into computer science courses.” 

Prakash kicks off the workshop with an overview of computational epidemiology. He will use examples from his CSE 8803: Data Science for Epidemiology course to introduce basic concepts. 

This overview includes a survey of models used to describe behavior of diseases. Models serve as foundations that run simulations, ultimately testing hypotheses and making predictions regarding disease spread and impact. 

Prakash will explain the different kinds of models used in epidemiology, such as traditional mechanistic models and more recent ML and AI based models.

Prakash’s discussion includes modeling used in recent epidemics like Covid-19, Zika, H1N1 bird flu, and Ebola. He will also cover examples from the 19th and 20th centuries to illustrate how epidemiology has advanced using data science and computation. 

“I strongly believe that data and computation have a very important role to play in the future of epidemiology and public health is computational,” Prakash said. 

“My course and these workshops give that viewpoint, and provide a broad framework of data science and computational thinking that can be useful.”

While humankind has studied disease transmission for millennia, computational epidemiology is a new approach to understanding how diseases can spread throughout communities.  

The Covid-19 pandemic helped bring computational epidemiology to the forefront of public awareness. This exposure has led to greater demand for further application from computer science education. 

Prakash joins Baltazar Espinoza and Natarajan Meghanathan in the workshop presentation. Espinoza is a research assistant professor at the University of Virginia. Meghanathan is a professor at Jackson State University. 

The group is connected through Global Pervasive Computational Epidemiology (GPCE). GPCE is a partnership of 13 institutions aimed at advancing computational foundations, engineering principles, and technologies of computational epidemiology. 

The National Science Foundation (NSF) supports GPCE through the Expeditions in Computing program. Prakash himself is principal investigator of other NSF-funded grants in which material from these projects appear in his workshop presentation.

[Related: Researchers to Lead Paradigm Shift in Pandemic Prevention with NSF Grant]

Outreach and broadening participation in computing are tenets of Prakash and GPCE because of how widely epidemics can reach. The SIGCSE workshop is one way that the group employs educational programs to train the next generation of scientists around the globe.

“Algorithms, machine learning, and other topics are fundamental graduate and undergraduate computer science courses nowadays,” Prakash said. 

“Using examples like projects, homework questions, and data sets, we want to show that the topics and ideas from computational epidemiology help students see a future where they apply their computer science education to pressing, real world challenges.” 

We used the lull between events, research, and classes to meet with Orozco and learn more about his background and interests in this Meet CSE profile.

Student: Rafael Orozco  

Research Interests: Medical Imaging; Seismic Imaging; Generative Models; Inverse Problems; Bayesian Inference; Uncertainty Quantification 

Hometown: Sonora, Mexico 

Tell us briefly about your educational background and how you came to Georgia Tech. 
I studied in Mexico through high school. Then, I did my first two years of undergrad at the University of Arizona and transferred to Bucknell University. I was attracted to Georgia Tech’s CSE program because it is a unique combination of domain science and computer science. It feels like I am both a programmer and a scientist.  

How did you first become interested in computer science and machine learning? 

In high school, I saw a video demonstration of a genetic algorithm on the internet and became interested in the technology. My high school in Mexico did not have a computer science class, but a teacher mentored me and helped me compete at the Mexican Informatics Olympiad. When I started at Arizona, I researched the behavior of clouds from a Bayesian perspective. Since then, my research interests have always involved using Bayesian techniques to infer unknowns.  

You mentioned your background a few times. Since it is National Hispanic Heritage Month, what does this observance mean to you? 

I am quite proud to be a part of this group. In Mexico and the U.S., fellow Hispanics have supported me and my pursuits, so I know firsthand of their kindness and resourcefulness. I think that Hispanic people welcome others, celebrating the joy our culture brings, and they appreciate that our country uses the opportunity to reflect on Hispanic history. 

You study in Professor Felix Herrmann’s Seismic Laboratory for Imaging and Modeling (SLIM) group. In your own words, what does this research group do? 

We develop techniques and software for imaging Earth’s subsurface structures. These range from highly performant partial differential equation solvers to randomized numerical algebra to generative artificial intelligence (AI) models.  

One of the driving goals of each software package we develop is that it needs to be scalable to real world applications. This entails imaging seismic areas that can be kilometers cubed in volume, represented typically by more than 100,000,000 simulation grid cells. In my medical applications, high-resolution images of human brains that can be resolved to less than half a millimeter.  

The International Meeting for Applied Geoscience and Energy (IMAGE) is a recent conference where SLIM gave nine presentations. What research did you present here? 
The challenge of applying machine learning to seismic imaging is that there are no examples of what the earth looks like. While making high quality reference images of human tissues for supervised machine learning is possible, no one can “cut open” the earth to understand exactly what it looks like.  

To address this challenge, I presented an algorithm that combines generative AI with an unsupervised training objective. We essentially trick the generative model into outputting full earth models by making it blind to which part of the Earth we are asking for. This is like when you take an exam where only a few questions will be graded, but you don’t know which ones, so you answer all the questions just in case.  

While seismic imaging is the basis of SLIM research, there are other applications for the group’s work. Can you discuss more about this? 

The imaging techniques that the energy industry has been using for decades toward imaging Earth’s subsurface can be applied almost seamlessly to create medical images of human sub tissue.  

Lately, we have been tackling the particularly difficult modality of using high frequency ultrasound to image through the human skull. In our recent paper, we are exploring a powerful combination between machine learning and physics-based methods that allows us to speed up imaging while adding uncertainty quantification.  
 
We presented the work at this year’s MIDL conference (Medical Imaging with Deep Learning) in July. The medical community was excited with our preliminary results and gave me valuable feedback on how we can help bring this technique closer to clinical viability. 

Farsight is a tool designed for developers who use large language models (LLMs) to create applications powered by artificial intelligence (AI). Farsight alerts prototypers when they write LLM prompts that could be harmful and misused.

Downstream users can expect to benefit from better quality and safer products made with Farsight’s assistance. The tool’s lasting impact, though, is that it fosters responsible AI awareness by coaching developers on the proper use of LLMs.

Machine Learning Ph.D. candidate Zijie (Jay) Wang is Farsight’s lead architect. He will present the paper at the upcoming Conference on Human Factors in Computing Systems (CHI 2024). Farsight ranked in the top 5% of papers accepted to CHI 2024, earning it an honorable mention for the conference’s best paper award.

“LLMs have empowered millions of people with diverse backgrounds, including writers, doctors, and educators, to build and prototype powerful AI apps through prompting. However, many of these AI prototypers don’t have training in computer science, let alone responsible AI practices,” said Wang.

“With a growing number of AI incidents related to LLMs, it is critical to make developers aware of the potential harms associated with their AI applications.”

Wang referenced an example when two lawyers used ChatGPT to write a legal brief. A U.S. judge sanctioned the lawyers because their submitted brief contained six fictitious case citations that the LLM fabricated.

With Farsight, the group aims to improve developers’ awareness of responsible AI use. It achieves this by highlighting potential use cases, affected stakeholders, and possible harm associated with an application in the early prototyping stage. 

A user study involving 42 prototypers showed that developers could better identify potential harms associated with their prompts after using Farsight. The users also found the tool more helpful and usable than existing resources. 

Feedback from the study showed Farsight encouraged developers to focus on end-users and think beyond immediate harmful outcomes.

“While resources, like workshops and online videos, exist to help AI prototypers, they are often seen as tedious, and most people lack the incentive and time to use them,” said Wang.

“Our approach was to consolidate and display responsible AI resources in the same space where AI prototypers write prompts. In addition, we leverage AI to highlight relevant real-life incidents and guide users to potential harms based on their prompts.”

Farsight employs an in-situ user interface to show developers the potential negative consequences of their applications during prototyping. 

Alert symbols for “neutral,” “caution,” and “warning” notify users when prompts require more attention. When a user clicks the alert symbol, an awareness sidebar expands from one side of the screen. 

The sidebar shows an incident panel with actual news headlines from incidents relevant to the harmful prompt. The sidebar also has a use-case panel that helps developers imagine how different groups of people can use their applications in varying contexts.

Another key feature is the harm envisioner. This functionality takes a user’s prompt as input and assists them in envisioning potential harmful outcomes. The prompt branches into an interactive node tree that lists use cases, stakeholders, and harms, like “societal harm,” “allocative harm,” “interpersonal harm,” and more.

The novel design and insightful findings from the user study resulted in Farsight’s acceptance for presentation at CHI 2024.

CHI is considered the most prestigious conference for human-computer interaction and one of the top-ranked conferences in computer science.

CHI is affiliated with the Association for Computing Machinery. The conference takes place May 11-16 in Honolulu.

Wang worked on Farsight in Summer 2023 while interning at Google + AI Research group (PAIR).

Farsight’s co-authors from Google PAIR include Chinmay Kulkarni, Lauren Wilcox, Michael Terry, and Michael Madaio. The group possesses closer ties to Georgia Tech than just through Wang.

Terry, the current co-leader of Google PAIR, earned his Ph.D. in human-computer interaction from Georgia Tech in 2005. Madaio graduated from Tech in 2015 with a M.S. in digital media. Wilcox was a full-time faculty member in the School of Interactive Computing from 2013 to 2021 and serves in an adjunct capacity today.

Though not an author, one of Wang’s influences is his advisor, Polo Chau. Chau is an associate professor in the School of Computational Science and Engineering. His group specializes in data science, human-centered AI, and visualization research for social good.  

“I think what makes Farsight interesting is its unique in-workflow and human-AI collaborative approach,” said Wang. 

“Furthermore, Farsight leverages LLMs to expand prototypers’ creativity and brainstorm a wide range of use cases, stakeholders, and potential harms.”

ARCollab is an iOS AR application designed for doctors to interact with patient-specific 3D heart models in a shared environment. It is the first surgical planning tool that uses multi-user mobile AR in iOS.

The application’s collaborative feature overcomes limitations in traditional surgical modeling and planning methods. This offers patients better, personalized care from doctors who plan and collaborate with the tool.

Georgia Tech researchers partnered with Children’s Healthcare of Atlanta (CHOA) in ARCollab’s development. Pratham Mehta, a computer science major, led the group’s research.

“We have conducted two trips to CHOA for usability evaluations with cardiologists and surgeons. The overall feedback from ARCollab users has been positive,” Mehta said. 

“They all enjoyed experimenting with it and collaborating with other users. They also felt like it had the potential to be useful in surgical planning.”

ARCollab’s collaborative environment is the tool’s most novel feature. It allows surgical teams to study and plan together in a virtual workspace, regardless of location.

ARCollab supports a toolbox of features for doctors to inspect and interact with their patients' AR heart models. With a few finger gestures, users can scale and rotate, “slice” into the model, and modify a slicing plane to view omnidirectional cross-sections of the heart.

Developing ARCollab on iOS works twofold. This streamlines deployment and accessibility by making it available on the iOS App Store and Apple devices. Building ARCollab on Apple’s peer-to-peer network framework ensures the functionality of the AR components. It also lessens the learning curve, especially for experienced AR users.

ARCollab overcomes traditional surgical planning practices of using physical heart models. Producing physical models is time-consuming, resource-intensive, and irreversible compared to digital models. It is also difficult for surgical teams to plan together since they are limited to studying a single physical model.

Digital and AR modeling is growing as an alternative to physical models. CardiacAR is one such tool the group has already created. 

However, digital platforms lack multi-user features essential for surgical teams to collaborate during planning. ARCollab’s multi-user workspace progresses the technology’s potential as a mass replacement for physical modeling.

“Over the past year and a half, we have been working on incorporating collaboration into our prior work with CardiacAR,” Mehta said. 

“This involved completely changing the codebase, rebuilding the entire app and its features from the ground up in a newer AR framework that was better suited for collaboration and future development.”

Its interactive and visualization features, along with its novelty and innovation, led the Conference on Human Factors in Computing Systems (CHI 2024) to accept ARCollab for presentation. The conference occurs May 11-16 in Honolulu.

CHI is considered the most prestigious conference for human-computer interaction and one of the top-ranked conferences in computer science.

M.S. student Harsha Karanth and alumnus Alex Yang (CS 2022, M.S. CS 2023) co-authored the paper with Mehta. They study under Polo Chau, an associate professor in the School of Computational Science and Engineering.

The Georgia Tech group partnered with Timothy Slesnick and Fawwaz Shaw from CHOA on ARCollab’s development.

“Working with the doctors and having them test out versions of our application and give us feedback has been the most important part of the collaboration with CHOA,” Mehta said. 

“These medical professionals are experts in their field. We want to make sure to have features that they want and need, and that would make their job easier.”

Researchers from the School of Computational Science and Engineering (CSE) created the Large Pre-Trained Time-Series Model (LPTM) framework. LPTM is a single foundational model that completes forecasting tasks across a broad range of domains. 

Along with performing as well or better than models purpose-built for their applications, LPTM requires 40% less data and 50% less training time than current baselines. In some cases, LPTM can be deployed without any training data.

The key to LPTM is that it is pre-trained on datasets from different industries like healthcare, transportation, and energy. The Georgia Tech group created an adaptive segmentation module to make effective use of these vastly different datasets.

The Georgia Tech researchers will present LPTM in Vancouver, British Columbia, Canada, at the 2024 Conference on Neural Information Processing Systems (NeurIPS 2024). NeurIPS is one of the world’s most prestigious conferences on artificial intelligence (AI) and ML research.

“The foundational model paradigm started with text and image, but people haven’t explored time-series tasks yet because those were considered too diverse across domains,” said B. Aditya Prakash, one of LPTM’s developers. 

“Our work is a pioneer in this new area of exploration where only few attempts have been made so far.”

[MICROSITE: Georgia Tech at NeurIPS 2024]

Foundational models are trained with data from different fields, making them powerful tools when assigned tasks. Foundational models drive GPT, DALL-E, and other popular generative AI platforms used today. LPTM is different though because it is geared toward time-series, not text and image generation.  

The Georgia Tech researchers trained LPTM on data ranging from epidemics, macroeconomics, power consumption, traffic and transportation, stock markets, and human motion and behavioral datasets.

After training, the group pitted LPTM against 17 other models to make forecasts as close to nine real-case benchmarks. LPTM performed the best on five datasets and placed second on the other four.

The nine benchmarks contained data from real-world collections. These included the spread of influenza in the U.S. and Japan, electricity, traffic, and taxi demand in New York, and financial markets.   

The competitor models were purpose-built for their fields. While each model performed well on one or two benchmarks closest to its designed purpose, the models ranked in the middle or bottom on others.

In another experiment, the Georgia Tech group tested LPTM against seven baseline models on the same nine benchmarks in zero-shot forecasting tasks. Zero-shot means the model is used out of the box and not given any specific guidance during training. LPTM outperformed every model across all benchmarks in this trial.

LPTM performed consistently as a top-runner on all nine benchmarks, demonstrating the model’s potential to achieve superior forecasting results across multiple applications with less and resources.

“Our model also goes beyond forecasting and helps accomplish other tasks,” said Prakash, an associate professor in the School of CSE. 

“Classification is a useful time-series task that allows us to understand the nature of the time-series and label whether that time-series is something we understand or is new.”

One reason traditional models are custom-built to their purpose is that fields differ in reporting frequency and trends. 

For example, epidemic data is often reported weekly and goes through seasonal peaks with occasional outbreaks. Economic data is captured quarterly and typically remains consistent and monotone over time. 

LPTM’s adaptive segmentation module allows it to overcome these timing differences across datasets. When LPTM receives a dataset, the module breaks data into segments of different sizes. Then, it scores all possible ways to segment data and chooses the easiest segment from which to learn useful patterns.

LPTM’s performance, enhanced through the innovation of adaptive segmentation, earned the model acceptance to NeurIPS 2024 for presentation. NeurIPS is one of three primary international conferences on high-impact research in AI and ML. NeurIPS 2024 occurs Dec. 10-15.

Ph.D. student Harshavardhan Kamarthi partnered with Prakash, his advisor, on LPTM. The duo are among the 162 Georgia Tech researchers presenting over 80 papers at the conference. 

Prakash is one of 46 Georgia Tech faculty with research accepted at NeurIPS 2024. Nine School of CSE faculty members, nearly one-third of the body, are authors or co-authors of 17 papers accepted at the conference. 

Along with sharing their research at NeurIPS 2024, Prakash and Kamarthi released an open-source library of foundational time-series modules that data scientists can use in their applications.

“Given the interest in AI from all walks of life, including business, social, and research and development sectors, a lot of work has been done and thousands of strong papers are submitted to the main AI conferences,” Prakash said. 

“Acceptance of our paper speaks to the quality of the work and its potential to advance foundational methodology, and we hope to share that with a larger audience.”

SubjECTive-QA is the first human-curated dataset on question-answer pairs from earnings call transcripts (ECTs). The dataset teaches models to identify subjective features in ECTs, like clarity and cautiousness.   

The dataset lays the foundation for a new approach to identifying disinformation and misinformation caused by nuances in speech. While ECT responses can be technically true, unclear or irrelevant information can misinform stakeholders and affect their decision-making. 

Tests on White House press briefings showed that the dataset applies to other sectors with frequent question-and-answer encounters, notably politics, journalism, and sports. This increases the odds of effectively informing audiences and improving transparency across public spheres.   

The intersecting work between natural language processing and finance earned the paper acceptance to NeurIPS 2024, the 38th Annual Conference on Neural Information Processing Systems. NeurIPS is one of the world’s most prestigious conferences on artificial intelligence (AI) and machine learning (ML) research.

"SubjECTive-QA has the potential to revolutionize nowcasting predictions with enhanced clarity and relevance,” said Agam Shah, the project’s lead researcher. 

“Its nuanced analysis of qualities in executive responses, like optimism and cautiousness, deepens our understanding of economic forecasts and financial transparency."

[MICROSITE: Georgia Tech at NeurIPS 2024]

SubjECTive-QA offers a new means to evaluate financial discourse by characterizing language's subjective and multifaceted nature. This improves on traditional datasets that quantify sentiment or verify claims from financial statements.

The dataset consists of 2,747 Q&A pairs taken from 120 ECTs from companies listed on the New York Stock Exchange from 2007 to 2021. The Georgia Tech researchers annotated each response by hand based on six features for a total of 49,446 annotations.

The group evaluated answers on:

• Relevance: the speaker answered the question with appropriate details.
• Clarity: the speaker was transparent in the answer and the message conveyed.
• Optimism: the speaker answered with a positive outlook regarding future outcomes.
• Specificity: the speaker included sufficient and technical details in their answer.
• Cautiousness: the speaker answered using a conservative, risk-averse approach.
• Assertiveness: the speaker answered with certainty about the company’s events and outcomes.

The Georgia Tech group validated their dataset by training eight computer models to detect and score these six features. Test models comprised of three BERT-based pre-trained language models (PLMs), and five popular large language models (LLMs) including Llama and ChatGPT. 

All eight models scored the highest on the relevance and clarity features. This is attributed to domain-specific pretraining that enables the models to identify pertinent and understandable material.

The PLMs achieved higher scores on the clear, optimistic, specific, and cautious categories. The LLMs scored higher in assertiveness and relevance. 

In another experiment to test transferability, a PLM trained with SubjECTive-QA evaluated 65 Q&A pairs from White House press briefings and gaggles. Scores across all six features indicated models trained on the dataset could succeed in other fields outside of finance. 

"Building on these promising results, the next step for SubjECTive-QA is to enhance customer service technologies, like chatbots,” said Shah, a Ph.D. candidate studying machine learning. 

“We want to make these platforms more responsive and accurate by integrating our analysis techniques from SubjECTive-QA."

SubjECTive-QA culminated from two semesters of work through Georgia Tech’s Vertically Integrated Projects (VIP) Program. The VIP Program is an approach to higher education where undergraduate and graduate students work together on long-term project teams led by faculty. 

Undergraduate students earn academic credit and receive hands-on experience through VIP projects. The extra help advances ongoing research and gives graduate students mentorship experience.

Computer science major Huzaifa Pardawala and mathematics major Siddhant Sukhani co-led the SubjECTive-QA project with Shah. 

Fellow collaborators included Veer Kejriwal, Abhishek Pillai, Rohan Bhasin, Andrew DiBiasio, Tarun Mandapati, and Dhruv Adha. All six researchers are undergraduate students studying computer science. 

Sudheer Chava co-advises Shah and is the faculty lead of SubjECTive-QA. Chava is a professor in the Scheller College of Business and director of the M.S. in Quantitative and Computational Finance (QCF) program.

Chava is also an adjunct faculty member in the College of Computing’s School of Computational Science and Engineering (CSE).

"Leading undergraduate students through the VIP Program taught me the powerful impact of balancing freedom with guidance,” Shah said. 

“Allowing students to take the helm not only fosters their leadership skills but also enhances my own approach to mentoring, thus creating a mutually enriching educational experience.”

Presenting SubjECTive-QA at NeurIPS 2024 exposes the dataset for further use and refinement. NeurIPS is one of three primary international conferences on high-impact research in AI and ML. The conference occurs Dec. 10-15.

The SubjECTive-QA team is among the 162 Georgia Tech researchers presenting over 80 papers at NeurIPS 2024. The Georgia Tech contingent includes 46 faculty members, like Chava. These faculty represent Georgia Tech’s Colleges of Business, Computing, Engineering, and Sciences, underscoring the pertinence of AI research across domains. 

"Presenting SubjECTive-QA at prestigious venues like NeurIPS propels our research into the spotlight, drawing the attention of key players in finance and tech,” Shah said.

“The feedback we receive from this community of experts validates our approach and opens new avenues for future innovation, setting the stage for transformative applications in industry and academia.”

The new algorithm is faster than existing methods while remaining highly accurate. The solver surpasses the limits of current models by demonstrating scalability across chemical system sizes ranging from large to small. 

Computer scientists and engineers benefit from the algorithm’s ability to balance processor loads. This work allows researchers to tackle larger, more complex problems without the prohibitive costs associated with previous methods.

Its ability to solve block linear systems drives the algorithm’s ingenuity. According to the researchers, their approach is the first known use of a block linear system solver to calculate electronic correlation energy.

The Georgia Tech team won’t need to travel far to share their findings with the broader high-performance computing community. They will present their work in Atlanta at the 2024 International Conference for High Performance Computing, Networking, Storage and Analysis (SC24).

[MICROSITE: Georgia Tech at SC24] 

“The combination of solving large problems with high accuracy can enable density functional theory simulation to tackle new problems in science and engineering,” said Edmond Chow, professor and associate chair of Georgia Tech’s School of Computational Science and Engineering (CSE).

Density functional theory (DFT) is a modeling method for studying electronic structure in many-body systems, such as atoms and molecules. 

An important concept DFT models is electronic correlation, the interaction between electrons in a quantum system. Electron correlation energy is the measure of how much the movement of one electron is influenced by presence of all other electrons.

Random phase approximation (RPA) is used to calculate electron correlation energy. While RPA is very accurate, it becomes computationally more expensive as the size of the system being calculated increases.

Georgia Tech’s algorithm enhances electronic correlation energy computations within the RPA framework. The approach circumvents inefficiencies and achieves faster solution times, even for small-scale chemical systems.

The group integrated the algorithm into existing work on SPARC, a real-space electronic structure software package for accurate, efficient, and scalable solutions of DFT equations. School of Civil and Environmental Engineering Professor Phanish Suryanarayana is SPARC’s lead researcher.

The group tested the algorithm on small chemical systems of silicon crystals numbering as few as eight atoms. The method achieved faster calculation times and scaled to larger system sizes than direct approaches.

“This algorithm will enable SPARC to perform electronic structure calculations for realistic systems with a level of accuracy that is the gold standard in chemical and materials science research,” said Suryanarayana.

RPA is expensive because it relies on quartic scaling. When the size of a chemical system is doubled, the computational cost increases by a factor of 16. 

Instead, Georgia Tech’s algorithm scales cubically by solving block linear systems. This capability makes it feasible to solve larger problems at less expense. 

Solving block linear systems presents a challenging trade-off in solving different block sizes. While larger blocks help reduce the number of steps of the solver, using them demands higher computational cost per step on computer processors. 

Tech’s solution is a dynamic block size selection solver. The solver allows each processor to independently select block sizes to calculate. This solution further assists in scaling, and improves processor load balancing and parallel efficiency.

“The new algorithm has many forms of parallelism, making it suitable for immense numbers of processors,” Chow said. “The algorithm works in a real-space, finite-difference DFT code. Such a code can scale efficiently on the largest supercomputers.”

Georgia Tech alumni Shikhar Shah (Ph.D. CSE 2024), Hua Huang (Ph.D. CSE 2024), and Ph.D. student Boqin Zhang led the algorithm’s development. The project was the culmination of work for Shah and Huang, who completed their degrees this summer. John E. Pask, a physicist at Lawrence Livermore National Laboratory, joined the Tech researchers on the work.

Shah, Huang, Zhang, Suryanarayana, and Chow are among more than 50 students, faculty, research scientists, and alumni affiliated with Georgia Tech who are scheduled to give more than 30 presentations at SC24. The experts will present their research through papers, posters, panels, and workshops. 

SC24 takes place Nov. 17-22 at the Georgia World Congress Center in Atlanta. 

“The project’s success came from combining expertise from people with diverse backgrounds ranging from numerical methods to chemistry and materials science to high-performance computing,” Chow said.

“We could not have achieved this as individual teams working alone.”

We sat down with Mi to learn more about her background and to introduce her to the Georgia Tech and College of Computing communities. 

Faculty: Lu Mi, assistant professor, School of CSE

Research Interests: Computational Neuroscience, Machine Learning

Education: Ph.D. in Computer Science from the Massachusetts Institute of Technology; B.S. in Measurement, Control, and Instruments from Tsinghua University

Hometown: Sichuan, China (home of the giant pandas) 

How have your first few months at Georgia Tech gone so far?

I’ve really enjoyed my time at Georgia Tech. Developing a new course has been both challenging and rewarding. I’ve learned a lot from the process and conversations with students. My colleagues have been incredibly welcoming, and I’ve had the opportunity to work with some very smart and motivated students here at Georgia Tech.

You hit the ground running this year by teaching your CSE 8803 course on brain-inspired machine intelligence. What important concepts do you teach in this class?

This course focuses on comparing biological neural networks with artificial neural networks. We explore questions like: How does the brain encode information, perform computations, and learn? What can neuroscience and artificial intelligence (AI) learn from each other? Key topics include spiking neural networks, neural coding, and biologically plausible learning rules. By the end of the course, I expect students to have a solid understanding of neural algorithms and the emerging NeuroAI field.

When and how did you become interested in computational neuroscience in the first place?

I’ve been fascinated by how the brain works since I was young. My formal engagement with the field began during my Ph.D. research, where we developed algorithms to help neuroscientists map large-scale synaptic wiring diagrams in the brain. Since then, I’ve had the opportunity to collaborate with researchers at institutions like Harvard, the Janelia Research Campus, the Allen Institute for Brain Science, and the University of Washington on various exciting projects in this field.

What about your experience and research are you currently most proud of?

I’m particularly proud of the framework we developed to integrate black-box machine learning models with biologically realistic mechanistic models. We use advanced deep-learning techniques to infer unobserved information and combine this with prior knowledge from mechanistic models. This allows us to test hypotheses by applying different model variants. I believe this framework holds great potential to address a wide range of scientific questions, leveraging the power of AI.

What about Georgia Tech convinced you to accept a faculty position?

Georgia Tech CSE felt like a perfect fit for my background and research interests, particularly within the AI4Science initiative and the development of computational tools for biology and neuroscience. My work overlaps with several colleagues here, and I’m excited to collaborate with them. Georgia Tech also has a vibrant and impactful Neuro Next Initiative community, which is another great attraction.

What are your hobbies and interests when not researching and teaching?

I enjoy photography and love spending time with my two corgi dogs, especially taking them for walks.

What have you enjoyed most so far about living in Atlanta? 

I’ve really appreciated the peaceful, green environment with so many trees. I’m also looking forward to exploring more outdoor activities, like fishing and golfing.

Georgia Tech Ph.D. student Austin P. Wright is first author of a paper that introduces Nested Fusion. The new algorithm improves scientists’ ability to search for past signs of life on the Martian surface. 

In addition to supporting NASA’s Mars 2020 mission, scientists from other fields working with large, overlapping datasets can use Nested Fusion’s methods toward their studies.

Wright presented Nested Fusion at the 2024 International Conference on Knowledge Discovery and Data Mining (KDD 2024) where it was a runner-up for the best paper award. KDD is widely considered the world's most prestigious conference for knowledge discovery and data mining research.

“Nested Fusion is really useful for researchers in many different domains, not just NASA scientists,” said Wright. “The method visualizes complex datasets that can be difficult to get an overall view of during the initial exploratory stages of analysis.”

Nested Fusion combines datasets with different resolutions to produce a single, high-resolution visual distribution. Using this method, NASA scientists can more easily analyze multiple datasets from various sources at the same time. This can lead to faster studies of Mars’ surface composition to find clues of previous life.

The algorithm demonstrates how data science impacts traditional scientific fields like chemistry, biology, and geology.

Even further, Wright is developing Nested Fusion applications to model shifting climate patterns, plant and animal life, and other concepts in the earth sciences. The same method can combine overlapping datasets from satellite imagery, biomarkers, and climate data.

“Users have extended Nested Fusion and similar algorithms toward earth science contexts, which we have received very positive feedback,” said Wright, who studies machine learning (ML) at Georgia Tech.

“Cross-correlational analysis takes a long time to do and is not done in the initial stages of research when patterns appear and form new hypotheses. Nested Fusion enables people to discover these patterns much earlier.”

Wright is the data science and ML lead for PIXLISE, the software that NASA JPL scientists use to study data from the Mars Perseverance Rover.

Perseverance uses its Planetary Instrument for X-ray Lithochemistry (PIXL) to collect data on mineral composition of Mars’ surface. PIXL’s two main tools that accomplish this are its X-ray Fluorescence (XRF) Spectrometer and Multi-Context Camera (MCC).

When PIXL scans a target area, it creates two co-aligned datasets from the components. XRF collects a sample's fine-scale elemental composition. MCC produces images of a sample to gather visual and physical details like size and shape. 

A single XRF spectrum corresponds to approximately 100 MCC imaging pixels for every scan point. Each tool’s unique resolution makes mapping between overlapping data layers challenging. However, Wright and his collaborators designed Nested Fusion to overcome this hurdle.

In addition to progressing data science, Nested Fusion improves NASA scientists' workflow. Using the method, a single scientist can form an initial estimate of a sample’s mineral composition in a matter of hours. Before Nested Fusion, the same task required days of collaboration between teams of experts on each different instrument.

“I think one of the biggest lessons I have taken from this work is that it is valuable to always ground my ML and data science problems in actual, concrete use cases of our collaborators,” Wright said. 

“I learn from collaborators what parts of data analysis are important to them and the challenges they face. By understanding these issues, we can discover new ways of formalizing and framing problems in data science.”

Wright presented Nested Fusion at KDD 2024, held Aug. 25-29 in Barcelona, Spain. KDD is an official special interest group of the Association for Computing Machinery. The conference is one of the world’s leading forums for knowledge discovery and data mining research.

Nested Fusion won runner-up for the best paper in the applied data science track, which comprised of over 150 papers. Hundreds of other papers were presented at the conference’s research track, workshops, and tutorials. 

Wright’s mentors, Scott Davidoff and Polo Chau, co-authored the Nested Fusion paper. Davidoff is a principal research scientist at the NASA Jet Propulsion Laboratory. Chau is a professor at the Georgia Tech School of Computational Science and Engineering (CSE).

“I was extremely happy that this work was recognized with the best paper runner-up award,” Wright said. “This kind of applied work can sometimes be hard to find the right academic home, so finding communities that appreciate this work is very encouraging.”

Georgia Tech researchers partnered with medical experts from Children’s Healthcare of Atlanta (CHOA) to develop and test ARCollab. The iOS-based app leverages advanced AR technologies to let doctors collaborate together and interact with a patient’s 3D heart model when planning surgeries.

The usability evaluation demonstrates the app’s effectiveness, finding that ARCollab is easy to use and understand, fosters collaboration, and improves surgical planning.

“This tool is a step toward easier collaborative surgical planning. ARCollab could reduce the reliance on physical heart models, saving hours and even days of time while maintaining the collaborative nature of surgical planning,” said M.S. student Pratham Mehta, the app’s lead researcher.

“Not only can it benefit doctors when planning for surgery, it may also serve as a teaching tool to explain heart deformities and problems to patients.”

Two cardiologists and three cardiothoracic surgeons from CHOA tested ARCollab. The two-day study ended with the doctors taking a 14-question survey assessing the app’s usability. The survey also solicited general feedback and top features.

The Georgia Tech group determined from the open-ended feedback that:

• ARCollab enables new collaboration capabilities that are easy to use and facilitate surgical planning.
• Anchoring the model to a physical space is important for better interaction.
• Portability and real-time interaction are crucial for collaborative surgical planning.

Users rated each of the 14 questions on a 7-point Likert scale, with one being “strongly disagree” and seven being “strongly agree.” The 14 questions were organized into five categories: overall, multi-user, model viewing, model slicing, and saving and loading models.

The multi-user category attained the highest rating with an average of 6.65. This included a unanimous 7.0 rating that it was easy to identify who was controlling the heart model in ARCollab. The scores also showed it was easy for users to connect with devices, switch between viewing and slicing, and view other users’ interactions.

The model slicing category received the lowest, but formidable, average of 5.5. These questions assessed ease of use and understanding of finger gestures and usefulness to toggle slice direction.

Based on feedback, the researchers will explore adding support for remote collaboration. This would assist doctors in collaborating when not in a shared physical space. Another improvement is extending the save feature to support multiple states.

“The surgeons and cardiologists found it extremely beneficial for multiple people to be able to view the model and collaboratively interact with it in real-time,” Mehta said.

The user study took place in a CHOA classroom. CHOA also provided a 3D heart model for the test using anonymous medical imaging data. Georgia Tech’s Institutional Review Board (IRB) approved the study and the group collected data in accordance with Institute policies.

The five test participants regularly perform cardiovascular surgical procedures and are employed by CHOA. 

The Georgia Tech group provided each participant with an iPad Pro with the latest iOS version and the ARCollab app installed. Using commercial devices and software meets the group’s intentions to make the tool universally available and deployable.

“We plan to continue iterating ARCollab based on the feedback from the users,” Mehta said. 

“The participants suggested the addition of a ‘distance collaboration’ mode, enabling doctors to collaborate even if they are not in the same physical environment. This allows them to facilitate surgical planning sessions from home or otherwise.”

The Georgia Tech researchers are presenting ARCollab and the user study results at IEEE VIS 2024, the Institute of Electrical and Electronics Engineers (IEEE) visualization conference. 

IEEE VIS is the world’s most prestigious conference for visualization research and the second-highest rated conference for computer graphics. It takes place virtually Oct. 13-18, moved from its venue in St. Pete Beach, Florida, due to Hurricane Milton.

The ARCollab research group's presentation at IEEE VIS comes months after they shared their work at the Conference on Human Factors in Computing Systems (CHI 2024).

Undergraduate student Rahul Narayanan and alumni Harsha Karanth (M.S. CS 2024) and Haoyang (Alex) Yang (CS 2022, M.S. CS 2023) co-authored the paper with Mehta. They study under Polo Chau, a professor in the School of Computational Science and Engineering.

The Georgia Tech group partnered with Dr. Timothy Slesnick and Dr. Fawwaz Shaw from CHOA on ARCollab’s development and user testing.

"I'm grateful for these opportunities since I get to showcase the team's hard work," Mehta said.

“I can meet other like-minded researchers and students who share these interests in visualization and human-computer interaction. There is no better form of learning.”

Their study of language detection software found that algorithms struggle to differentiate anti-Asian violence-provoking speech from general hate speech. Left unchecked, threats of violence online can go unnoticed and turn into real-world attacks. 

Researchers from Georgia Tech and the Anti-Defamation League (ADL) teamed together in the study. They made their discovery while testing natural language processing (NLP) models trained on data they crowdsourced from Asian communities. 

“The Covid-19 pandemic brought attention to how dangerous violence-provoking speech can be. There was a clear increase in reports of anti-Asian violence and hate crimes,” said Gaurav Verma, a Georgia Tech Ph.D. candidate who led the study. 

“Such speech is often amplified on social platforms, which in turn fuels anti-Asian sentiments and attacks.”

Violence-provoking speech differs from more commonly studied forms of harmful speech, like hate speech. While hate speech denigrates or insults a group, violence-provoking speech implicitly or explicitly encourages violence against targeted communities.

Humans can define and characterize violent speech as a subset of hateful speech. However, computer models struggle to tell the difference due to subtle cues and implications in language.

The researchers tested five different NLP classifiers and analyzed their F1 score, which measures a model's performance. The classifiers reported a 0.89 score for detecting hate speech, while detecting violence-provoking speech was only 0.69. This contrast highlights the notable gap between these tools and their accuracy and reliability. 

The study stresses the importance of developing more refined methods for detecting violence-provoking speech. Internet misinformation and inflammatory rhetoric escalate tensions that lead to real-world violence. 

The Covid-19 pandemic exemplified how public health crises intensify this behavior, helping inspire the study. The group cited that anti-Asian crime across the U.S. increased by 339% in 2021 due to malicious content blaming Asians for the virus. 

The researchers believe their findings show the effectiveness of community-centric approaches to problems dealing with harmful speech. These approaches would enable informed decision-making between policymakers, targeted communities, and developers of online platforms.

Along with stronger models for detecting violence-provoking speech, the group discusses a direct solution: a tiered penalty system on online platforms. Tiered systems align penalties with severity of offenses, acting as both deterrent and intervention to different levels of harmful speech. 

“We believe that we cannot tackle a problem that affects a community without involving people who are directly impacted,” said Jiawei Zhou, a Ph.D. student who studies human-centered computing at Georgia Tech. 

“By collaborating with experts and community members, we ensure our research builds on front-line efforts to combat violence-provoking speech while remaining rooted in real experiences and needs of the targeted community.”

The researchers trained their tested NLP classifiers on a dataset crowdsourced from a survey of 120 participants who self-identified as Asian community members. In the survey, the participants labeled 1,000 posts from X (formerly Twitter) as containing either violence-provoking speech, hateful speech, or neither.

Since characterizing violence-provoking speech is not universal, the researchers created a specialized codebook for survey participants. The participants studied the codebook before their survey and used an abridged version while labeling. 

To create the codebook, the group used an initial set of anti-Asian keywords to scan posts on X from January 2020 to February 2023. This tactic yielded 420,000 posts containing harmful, anti-Asian language. 

The researchers then filtered the batch through new keywords and phrases. This refined the sample to 4,000 posts that potentially contained violence-provoking content. Keywords and phrases were added to the codebook while the filtered posts were used in the labeling survey.

The team used discussion and pilot testing to validate its codebook. During trial testing, pilots labeled 100 Twitter posts to ensure the sound design of the Asian community survey. The group also sent the codebook to the ADL for review and incorporated the organization’s feedback. 

“One of the major challenges in studying violence-provoking content online is effective data collection and funneling down because most platforms actively moderate and remove overtly hateful and violent material,” said Tech alumnus Rynaa Grover (M.S. CS 2024).

“To address the complexities of this data, we developed an innovative pipeline that deals with the scale of this data in a community-aware manner.”

Emphasis on community input extended into collaboration within Georgia Tech’s College of Computing. Faculty members Srijan Kumar and Munmun De Choudhury oversaw the research that their students spearheaded.

Kumar, an assistant professor in the School of Computational Science and Engineering, advises Verma and Grover. His expertise is in artificial intelligence, data mining, and online safety.

De Choudhury is an associate professor in the School of Interactive Computing and advises Zhou. Their research connects societal mental health and social media interactions.

The Georgia Tech researchers partnered with the ADL, a leading non-governmental organization that combats real-world hate and extremism. ADL researchers Binny Mathew and Jordan Kraemer co-authored the paper.

The group will present its paper at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), which takes place in Bangkok, Thailand, Aug. 11-16 

ACL 2024 accepted 40 papers written by Georgia Tech researchers. Of the 12 Georgia Tech faculty who authored papers accepted at the conference, nine are from the College of Computing, including Kumar and De Choudhury.

“It is great to see that the peers and research community recognize the importance of community-centric work that provides grounded insights about the capabilities of leading language models,” Verma said. 

“We hope the platform encourages more work that presents community-centered perspectives on important societal problems.” 

Visit https://sites.gatech.edu/research/acl-2024/ for news and coverage of Georgia Tech research presented at ACL 2024.

The Georgia Tech group combined two current ML methods into their hybrid model called MRM-GP (Multi-Region Markovian Gaussian Process). 

Neuroscientists who use MRM-GP learn more about communications and interactions within the brain. This in turn improves understanding of brain functions and disorders.

“Clinically, MRM-GP could enhance diagnostic tools and treatment monitoring by identifying and analyzing neural activity patterns linked to various brain disorders,” said Weihan Li, the study’s lead researcher. 

“Neuroscientists can leverage MRM-GP for its robust modeling capabilities and efficiency in handling large-scale brain data.” 

MRM-GP reveals where and how communication travels across brain regions. 

The group tested MRM-GP using spike trains and local field potential recordings, two kinds of measurements of brain activity. These tests produced representations that illustrated directional flow of communication among brain regions. 

Experiments also disentangled brainwaves, called oscillatory interactions, into organized frequency bands. MRM-GP’s hybrid configuration allows it to model frequencies and phase delays within the latent space of neural recordings.

MRM-GP combines the strengths of two existing methods: the Gaussian process (GP) and linear dynamical systems (LDS). The researchers say that MRM-GP is essentially an LDS that mirrors a GP.

LDS is a computationally efficient and cost-effective method, but it lacks the power to produce representations of the brain. GP-based approaches boost LDS's power, facilitating the discovery of variables in frequency bands and communication directions in the brain.

Converting GP outputs into an LDS is a difficult task in ML. The group overcame this challenge by instilling separability in the model’s multi-region kernel. Separability establishes a connection between the kernel and LDS while modeling communication between brain regions.

Through this approach, MRM-GP overcomes two challenges facing both neuroscience and ML fields. The model helps solve the mystery of intraregional brain communication. It does so by bridging a gap between GP and LDS, a feat not previously accomplished in ML.

“The introduction of MRM-GP provides a useful tool to model and understand complex brain region communications,” said Li, a Ph.D. student in the School of Computational Science and Engineering (CSE). 

“This marks a significant advancement in both neuroscience and machine learning.”

Fellow doctoral students Chengrui Li and Yule Wang co-authored the paper with Li. School of CSE Assistant Professor Anqi Wu advises the group. 

Each MRM-GP student pursues a different Ph.D. degree offered by the School of CSE. W. Li studies computer science, C. Li studies computational science and engineering, and Wang studies machine learning. The school also offers Ph.D. degrees in bioinformatics and bioengineering.

Wu is a 2023 recipient of the Sloan Research Fellowship for neuroscience research. Her work straddles two of the School’s five research areas: machine learning and computational bioscience. 

MRM-GP will be featured at the world’s top conference on ML and artificial intelligence. The group will share their work at the International Conference on Machine Learning (ICML 2024), which will be held July 21-27 in Vienna. 

ICML 2024 also accepted for presentation a second paper from Wu’s group intersecting neuroscience and ML. The same authors will present A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing.

Twenty-four Georgia Tech faculty from the Colleges of Computing and Engineering will present 40 papers at ICML 2024. Wu is one of six faculty representing the School of CSE who will present eight total papers.

The group’s ICML 2024 presentations exemplify Georgia Tech’s focus on neuroscience research as a strategic initiative.  

Wu is an affiliated faculty member with the Neuro Next Initiative, a new interdisciplinary program at Georgia Tech that will lead research in neuroscience, neurotechnology, and society. The University System of Georgia Board of Regents recently approved a new neuroscience and neurotechnology Ph.D. program at Georgia Tech. 

“Presenting papers at international conferences like ICML is crucial for our group to gain recognition and visibility, facilitates networking with other researchers and industry professionals, and offers valuable feedback for improving our work,” Wu said. 

“It allows us to share our findings, stay updated on the latest developments in the field, and enhance our professional development and public speaking skills.”

Visit https://sites.gatech.edu/research/icml-2024 for news and coverage of Georgia Tech research presented at ICML 2024.

The Tech group dubbed its method PRODIGY (PROjected DIffusion for controlled Graph Generation). PRODIGY enables diffusion models to generate 3D images of complex structures, such as molecules from chemical formulas. 

Scientists in pharmacology, materials science, social network analysis, and other fields can use PRODIGY to simulate large-scale networks. By generating 3D molecules from multiple graph datasets, the group proved that PRODIGY could handle complex structures.

In keeping with its name, PRODIGY is the first plug-and-play machine learning (ML) approach to controllable graph generation in diffusion models. This method overcomes a known limitation inhibiting diffusion models from broad use in science and engineering.

“We hope PRODIGY enables drug designers and scientists to generate structures that meet their precise needs,” said Kartik Sharma, lead researcher on the project. “It should also inspire future innovations to precisely control modern generative models across domains.” 

PRODIGY works on diffusion models, a generative AI model for computer vision tasks. While suitable for image creation and denoising, diffusion methods are limited because they cannot accurately generate graph representations of custom parameters a user provides.

PRODIGY empowers any pre-trained diffusion model for graph generation to produce graphs that meet specific, user-given constraints. This capability means, as an example, that a drug designer could use any diffusion model to design a molecule with a specific number of atoms and bonds.

The group tested PRODIGY on two molecular and five generic datasets to generate custom 2D and 3D structures. This approach ensured the method could create such complex structures, accounting for the atoms, bonds, structures, and other properties at play in molecules. 

Molecular generation experiments with PRODIGY directly impact chemistry, biology, pharmacology, materials science, and other fields. The researchers say PRODIGY has potential in other fields using large networks and datasets, such as social sciences and telecommunications.

These features led to PRODIGY’s acceptance for presentation at the upcoming International Conference on Machine Learning (ICML 2024). ICML 2024 is the leading international academic conference on ML. The conference is taking place July 21-27 in Vienna.

Assistant Professor Srijan Kumar is Sharma’s advisor and paper co-author. They worked with Tech alumnus Rakshit Trivedi (Ph.D. CS 2020), a Massachusetts Institute of Technology postdoctoral associate.

Twenty-four Georgia Tech faculty from the Colleges of Computing and Engineering will present 40 papers at ICML 2024. Kumar is one of six faculty representing the School of Computational Science and Engineering (CSE) at the conference.

Sharma is a fourth-year Ph.D. student studying computer science. He researches ML models for structured data that are reliable and easily controlled by users. While preparing for ICML, Sharma has been interning this summer at Microsoft Research in the Research for Industry lab.

“ICML is the pioneering conference for machine learning,” said Kumar. “A strong presence at ICML from Georgia Tech illustrates the ground-breaking research conducted by our students and faculty, including those in my research group.”

Visit https://sites.gatech.edu/research/icml-2024 for news and coverage of Georgia Tech research presented at ICML 2024.

Tech hosted the 2024 International Conference on Preconditioning Techniques for Scientific and Industrial Applications (Precond 24), June 10-12. 

Preconditioning accelerates matrix computations, a kind of math used in most large-scale models. These computer models become faster, more efficient, and more accessible with help from preconditioned equations.

“Preconditioning transforms complex numerical problems into more easily solved ones,” said Edmond Chow, a professor at Georgia Tech and co-chair of Precond 24’s local organization and program committees. 

“The new problem wields a better condition number, giving rise to the name preconditioning.”

Researchers from 13 countries presented their work through 20 mini-symposia and seven invited talks at Precond 24. Their work showcased the practicality of preconditioners. 

Vandana Dwarka, an assistant professor at Delft University of Technology, shared newly developed preconditioners for electromagnetic simulations. This technology can be used in further applications ranging from imaging to designing nuclear fusion devices.

Xiaozhe Hu presented a physics-based preconditioner that simulates biophysical processes in the brain, such as blood flow and metabolic waste clearance. Hu brought this research from Tufts University, where he is an associate professor.

Tucker Hartland, a postdoctoral researcher at Lawrence Livermore National Laboratory, discussed preconditioning in contact mechanics. This work improves the modeling of interactions between physical objects that touch each other. Many fields stand to benefit from Hartland’s study, including mechanical engineering, civil engineering, and materials science.

A unique aspect of this year’s conference was an emphasis on machine learning (ML). Between a panel discussion, tutorial, and several talks, experts detailed how to employ ML for preconditioning and how preconditioning can train ML models.

Precond 24 invited seven speakers from institutions around the world to share their research with conference attendees. The presenters were: 

• Monica Dessole, CERN, Switzerland
• Selime Gurol, CERFACS, France
• Alexander Heinlein, Delft University of Technology, Netherlands
• Rui Peng Li, Lawrence Livermore National Laboratory, USA 
• Will Pazner, Portland State University, USA 
• Tyrone Rees, Science and Technology Facilities Council, UK
• Jacob B. Schroder, University of New Mexico, USA

Along with hosting Precond 24, several Georgia Tech researchers participated in the conference through presentations. 

Ph.D. students Hua Huang and Shikhar Shah each presented a paper on the conference’s first day. Alumnus Srinivas Eswar (Ph.D. CS 2022) returned to Atlanta to share research from his current role at Argonne National Laboratory. Chow chaired the ML panel and a symposium on preconditioners for matrices.

“It was an engaging and rewarding experience meeting so many people from this very tight-knit community,” said Shah, who studies computational science and engineering (CSE). “Getting to see talks close to my research provided me with a lot of inspiration and direction for future work.”

Precond 2024 was the thirteenth meeting of the conference, which occurs every two years. 

The conference returned to Atlanta this year for the first time since 2005. Atlanta joins Minneapolis as one of only two cities in the world to host Precond more than once. Precond 24 marked the sixth time the conference met in the U.S. 

Georgia Tech and Emory University’s Department of Mathematics organized and sponsored Precond 24. The U.S. Department of Energy Office of Science co-sponsored the conference with Tech and Emory. 

Georgia Tech entities swarmed together in support of Precond 24. The Office of the Associate Vice President for Research Operations and Infrastructure, College of Computing, and School of CSE co-sponsored the conference.

“The enthusiasm at the conference has been very gratifying. So many people organized sessions at the conference and contributed to the very strong attendance,” Chow said. 

“This is a testament to the continued importance of preconditioning and related numerical methods in a rapidly changing technological world.”

The feasibility study by Georgia Tech researchers explores using conditional normalizing flows (CNFs) to convert seismic data points into usable information and observable images. This potential ability could make monitoring underground storage sites more practical and studying the behavior of carbon dioxide plumes easier.

The 2023 Conference on Neural Information Processing Systems (NeurIPS 2023) accepted the group’s paper for presentation. They presented their study on Dec. 16 at the conference’s workshop on Tackling Climate Change with Machine Learning.

“One area where our group excels is that we care about realism in our simulations,” said Professor Felix Herrmann. “We worked on a real-sized setting with the complexities one would experience when working in real-life scenarios to understand the dynamics of carbon dioxide plumes.”

CNFs are generative models that use data to produce images. They can also fill in the blanks by making predictions to complete an image despite missing or noisy data. This functionality is ideal for this application because data streaming from GCS reservoirs are often noisy, meaning it’s incomplete, outdated, or unstructured data.

The group found in 36 test samples that CNFs could infer scenarios with and without leakage using seismic data. In simulations with leakage, the models generated images that were 96% similar to ground truths. CNFs further supported this by producing images 97% comparable to ground truths in cases with no leakage.

This CNF-based method also improves current techniques that struggle to provide accurate information on the spatial extent of leakage. Conditioning CNFs to samples that change over time allows it to describe and predict the behavior of carbon dioxide plumes.

This study is part of the group’s broader effort to produce digital twins for seismic monitoring of underground storage. A digital twin is a virtual model of a physical object. Digital twins are commonplace in manufacturing, healthcare, environmental monitoring, and other industries.   

“There are very few digital twins in earth sciences, especially based on machine learning,” Herrmann explained. “This paper is just a prelude to building an uncertainty aware digital twin for geological carbon storage.”

Herrmann holds joint appointments in the Schools of Earth and Atmospheric Sciences (EAS), Electrical and Computer Engineering, and Computational Science and Engineering (CSE).

School of EAS Ph.D. student Abhinov Prakash Gahlot is the paper’s first author. Ting-Ying (Rosen) Yu (B.S. ECE 2023) started the research as an undergraduate group member. School of CSE Ph.D. students Huseyin Tuna Erdinc, Rafael Orozco, and Ziyi (Francis) Yin co-authored with Gahlot and Herrmann.

NeurIPS 2023 took place Dec. 10-16 in New Orleans. Occurring annually, it is one of the largest conferences in the world dedicated to machine learning.

Over 130 Georgia Tech researchers presented more than 60 papers and posters at NeurIPS 2023. One-third of CSE’s faculty represented the School at the conference. Along with Herrmann, these faculty included Ümit Çatalyürek, Polo Chau, Bo Dai, Srijan Kumar, Yunan Luo, Anqi Wu, and Chao Zhang.

“In the field of geophysics, inverse problems and statistical solutions of these problems are known, but no one has been able to characterize these statistics in a realistic way,” Herrmann said.

“That’s where these machine learning techniques come into play, and we can do things now that you could never do before.”

School of Computational Science and Engineering (CSE) Assistant Professor Peng Chen is co-principal investigator of a $1.5 million National Science Foundation grant to develop the CRIS-HAZARD system.

CRIS-HAZARD‘s strength derives from integrating geographic information and data mined from community input, like traffic camera videos and social media posts.  

This ability helps policymakers identify areas most vulnerable to flooding and address community needs. The app also predicts and assesses flooding in real time to connect victims with first responders and emergency managers.

“Successfully deploying CRIS-HAZARD will harness community knowledge through direct and indirect engagement efforts to inform decision-making,” Chen said. “It will connect individuals to policymakers and serve as a roadmap at helping the most vulnerable communities.”

Chen’s role in CRIS-HAZARD will be to develop new ML models for the app’s prediction capability. These assimilation models integrate the mined data with predictions from current hydrodynamic models.

Along with making an immediate impact in flood-prone coastal communities, Chen said these models could have broader applications in the future. These include models for improved hurricane prediction and management of water resources.

The models Chen will build for CRIS-HAZARD derive from past applications aimed at helping communities.

Chen has crafted similar models for monitoring and mitigating disease spread, including Covid-19. He has also worked on materials science projects to accelerate the design of metamaterials and self-assembly materials.

“Scientific machine learning is very broad concept and can be applied to many different fields,” Chen said. “Our group looks at how to accelerate optimization, account for risk, and quantify uncertainty in these applications.”

Uncertainty in CRIS-HAZARD is what brings Chen to the project, headed by University of South Florida researchers. While the app’s novelty lies in its use of heterogenous data, inferring predictions can be challenging since the data comes from different sources in varying formats. 

To overcome this, Chen intends to build new data assimilation models from scratch powered by deep neural networks (DNNs).

Along with their ability to find connections between heterogeneous data, DNNs are scalable and inexpensive. This beats the alternative of using supercomputers to make the same calculations.

DNNs are also fast and can significantly reduce computational time. According to Chen, the efficiency of DNNs can achieve acceleration hundreds of thousands of times greater than classical models.

Low cost and time make it possible to run DNN-based simulations multiple times. This improves reliability in prediction results in real-time once the DNNs are properly trained.

“The data may not be consistent or compatible since there are different models we’re trying to integrate, making prediction uncertain,” Chen said. “We can run these ML models many times to quantify the uncertainty and give a probability distribution or a range of predictions.”

CRIS-HAZARD also exemplifies the power of collaboration across disciplines and universities. In this case, machine learning techniques reach across state boundaries to help people that are vulnerable to flooding or other natural disasters.

USF Professor Barnali Dixon leads the project with Associate Professor Yi Qiang— both geocomputation researchers in the School of Geosciences, incorporating data science and artificial intelligence.

Subhro Guhathakurta collaborates with Chen from Georgia Tech. Along with being a professor in the School of City & Regional Planning, Guhathkurta is director of Tech’s Master of Science in Urban Analytics program and the Center for Spatial Planning and Analytics and Visualization.

Tech is one of the first research universities in the country to receive the GH200 Grace Hopper Superchip from NVIDIA for testing, study, and research.

Designed for large-scale artificial intelligence (AI) and high-performance computing applications, the GH200 is intended for large language model (LLM) training, recommender systems, graph neural networks, and other tasks. 

Alexey Tumanov and Tushar Krishna procured Georgia Tech’s first pair of Grace Hopper chips. Spencer Bryngelson attained four more GH200s, which will arrive later this month.

“We are excited about this new design that puts everything onto one chip and accessible to both processors,” said Will Powell, a College of Computing research technologist.

“The Superchip’s design increases computation efficiency where data doesn’t have to move as much and all the memory is on the chip.” 

A key feature of the new processing chip is that the central processing unit (CPU) and graphics processing unit (GPU) are on the same board.

NVIDIA’s NVLink Chip-2-Chip (C2C) interconnect joins the two units together. C2C delivers up to 900 gigabytes per second of total bandwidth, seven times faster than PCIe Gen5 connections used in newer accelerated systems.  

As a result, the two components share memory and process data with more speed and better power efficiency. This feature is one that the Georgia Tech researchers want to explore most.

Tumanov, an assistant professor in the School of Computer Science, and his Ph.D. student Amey Agrawal, are testing machine learning (ML) and LLM workloads on the chip. Their work with the GH200 could lead to more sustainable computing methods that keep up with the exponential growth of LLMs.

The advent of household LLMs, like ChatGPT and Gemini, pushes the limit of current architectures based on GPUs. The chip’s design overcomes known CPU-GPU bandwidth limitations. Tumanov’s group will put that design to the test through their studies. 

Krishna is an associate professor in the School of Electrical and Computer Engineering and associate director of the Center for Research into Novel Computing Hierarchies (CRNCH).

His research focuses on optimizing data movement in modern computing platforms, including AI/ML accelerator systems. Ph.D. student Hao Kang uses the GH200 to analyze LLMs exceeding 30 billion parameters. This study will enable labs to explore deep learning optimizations with the new chip.  

Bryngelson, an assistant professor in the School of Computational Science and Engineering, will use the chip to compute and simulate fluid and solid mechanics phenomena. His lab can use the CPU to reorder memory and perform disk writes while the GPU does parallel work. This capability is expected to significantly reduce the computational burden for some applications.

“Traditional CPU to GPU communication is slower and introduces latency issues because data passes back and forth over a PCIe bus,” Powell said. “Since they can access each other’s memory and share in one hop, the Superchip’s architecture boosts speed and efficiency.” 

Grace Hopper is the inspirational namesake for the chip. She pioneered many developments in computer science that formed the foundation of the field today.  

Hopper invented the first compiler, a program that translates computer source code into a target language. She also wrote the earliest programming languages, including COBOL, which is still used today in data processing. 

Hopper joined the U.S. Navy Reserve during World War II, tasked with programming the Mark I computer. She retired as a rear admiral in August 1986 after 42 years of military service.

Georgia Tech researchers hope to preserve Hopper’s legacy using the technology that bears her name and spirit for innovation to make new discoveries.

“NVIDIA and other vendors show no sign of slowing down refinement of this kind of design, so it is important that our students understand how to get the most out of this architecture,” said Powell. 

“Just having all these technologies isn’t enough. People must know how to build applications in their coding that actually benefit from these new architectures. That is the skill.” 

New AI models produced through the grant will lead to new methods for the design and discovery of functional proteins. This could yield novel drugs and vaccines, personalized treatments against diseases, and other advances in biomedicine.

“This project provides a new paradigm to analyze proteins’ sequence-structure-function relationships using machine learning approaches,” said Luo, an assistant professor in Georgia Tech’s School of Computational Science and Engineering (CSE).

“We will develop new, ready-to-use computational models for domain scientists, like biologists and chemists. They can use our machine learning tools to guide scientific discovery in their research.” 

Luo’s proposal improves on datasets spearheaded by AlphaFold and other recent breakthroughs. His AI algorithms would integrate these datasets and craft new models for practical application.

One of Luo’s goals is to develop machine learning methods that learn statistical representations from the data. This reveals relationships between proteins’ sequence, structure, and function. Scientists then could characterize how sequence and structure determine the function of a protein.

Next, Luo wants to make accurate and interpretable predictions about protein functions. His plan is to create biology-informed deep learning frameworks. These frameworks could make predictions about a protein’s function from knowledge of its sequence and structure. It can also account for variables like mutations.

In the end, Luo would have the data and tools to assist in the discovery of functional proteins. He will use these to build a computational platform of AI models, algorithms, and frameworks that ‘invent’ proteins. The platform figures the sequence and structure necessary to achieve a designed proteins desired functions and characteristics.

“My students play a very important part in this research because they are the driving force behind various aspects of this project at the intersection of computational science and protein biology,” Luo said.

“I think this project provides a unique opportunity to train our students in CSE to learn the real-world challenges facing scientific and engineering problems, and how to integrate computational methods to solve those problems.”

The $1.8 million grant is funded through the Maximizing Investigators’ Research Award (MIRA). The National Institute of General Medical Sciences (NIGMS) manages the MIRA program. NIGMS is one of 27 institutes and centers under NIH.

MIRA is oriented toward launching the research endeavors of young career faculty. The grant provides researchers with more stability and flexibility through five years of funding. This enhances scientific productivity and improves the chances for important breakthroughs.

Luo becomes the second School of CSE faculty to receive the MIRA grant. NIH awarded the grant to Xiuwei Zhang in 2021. Zhang is the J.Z. Liang Early-Career Assistant Professor in the School of CSE.

[Related: Award-winning Computer Models Propel Research in Cellular Differentiation]

“After NIH, of course, I first thanked my students because they laid the groundwork for what we seek to achieve in our grant proposal,” said Luo.

“I would like to thank my colleague, Xiuwei Zhang, for her mentorship in preparing the proposal. I also thank our school chair, Haesun Park, for her help and support while starting my career.”

Gaurav Verma and Yuxi Wu are recipients of 2023 J.P. Morgan AI Research Ph.D. Fellowship Awards. They are among 13 scholars being honored this year by J.P. Morgan Chase & Co. for AI research projects taking on real-world challenges.

"Our goal is to recognize and enable the next generation of leading AI researchers. We want to create an environment where researchers can inspire change and make a lasting impact in our communities and across our industry," said Manuela Veloso, Ph.D., head of AI Research, J.P. Morgan Chase & Co.

Verma is pursuing his Ph.D. in the School of Computational Science and Engineering. Working with his advisor, Assistant Professor Srijan Kumar, Verma expects to ensure safety, equity, and well-being by creating multimodal learning and natural language processing approaches to achieve better human-AI interactions.

Wu is a Ph.D. candidate in the School of Interactive Computing. Empowering people regarding their privacy concerns is at the core of her research. Wu examines how cross-sector, collective action systems could better support end-user privacy. Professor Keith Edwards and Adjunct Assistant Professor Sauvik Das advise Wu.

"It's inspiring to see our students and their work being honored with these prestigious fellowships," said Irfan Essa, computer science professor and director of the Machine Learning Center at Georgia Tech.

"Georgia Tech continues to lead in AI education and research. These fellowships for Gaurav and Yuxi are evidence that we're continuing to move in the right direction."

Verma and Wu are part of a spectrum of AI research spanning Georgia Tech. To unite this broad community and ensure it continues moving in the right direction, the Institute recently established AI Hub at Georgia Tech.

"AI has a deep history at Georgia Tech, and we continue to serve as leaders in many areas of AI research and education," said Essa, interim co-director of AI Hub at Georgia Tech.

"Bringing all areas of AI under one umbrella, AI Hub at Georgia Tech will provide structure and governance as the Institute continues to lead and innovate in the burgeoning discipline of AI."

School of Computational Science and Engineering (CSE) Assistant Professor Peng Chen is co-principal investigator of a $1.5 million National Science Foundation grant to develop the CRIS-HAZARD system.

CRIS-HAZARD‘s strength derives from integrating geographic information and data mined from community input, like traffic camera videos and social media posts.  

This ability helps policymakers identify areas most vulnerable to flooding and address community needs. The app also predicts and assesses flooding in real time to connect victims with first responders and emergency managers.

“Successfully deploying CRIS-HAZARD will harness community knowledge through direct and indirect engagement efforts to inform decision-making,” Chen said. “It will connect individuals to policymakers and serve as a roadmap at helping the most vulnerable communities.”

Chen’s role in CRIS-HAZARD will be to develop new ML models for the app’s prediction capability. These assimilation models integrate the mined data with predictions from current hydrodynamic models.

Along with making an immediate impact in flood-prone coastal communities, Chen said these models could have broader applications in the future. These include models for improved hurricane prediction and management of water resources.

The models Chen will build for CRIS-HAZARD derive from past applications aimed at helping communities.

Chen has crafted similar models for monitoring and mitigating disease spread, including Covid-19. He has also worked on materials science projects to accelerate the design of metamaterials and self-assembly materials.

“Scientific machine learning is very broad concept and can be applied to many different fields,” Chen said. “Our group looks at how to accelerate optimization, account for risk, and quantify uncertainty in these applications.”

Uncertainty in CRIS-HAZARD is what brings Chen to the project, headed by University of South Florida researchers. While the app’s novelty lies in its use of heterogenous data, inferring predictions can be challenging since the data comes from different sources in varying formats. 

To overcome this, Chen intends to build new data assimilation models from scratch powered by deep neural networks (DNNs).

Along with their ability to find connections between heterogeneous data, DNNs are scalable and inexpensive. This beats the alternative of using supercomputers to make the same calculations.

DNNs are also fast and can significantly reduce computational time. According to Chen, the efficiency of DNNs can achieve acceleration hundreds of thousands of times greater than classical models.

Low cost and time make it possible to run DNN-based simulations multiple times. This improves reliability in prediction results in real-time once the DNNs are properly trained.

“The data may not be consistent or compatible since there are different models we’re trying to integrate, making prediction uncertain,” Chen said. “We can run these ML models many times to quantify the uncertainty and give a probability distribution or a range of predictions.”

CRIS-HAZARD also exemplifies the power of collaboration across disciplines and universities. In this case, machine learning techniques reach across state boundaries to help people that are vulnerable to flooding or other natural disasters.

USF Professor Barnali Dixon leads the project with Associate Professor Yi Qiang— both geocomputation researchers in the School of Geosciences, incorporating data science and artificial intelligence.

Subhro Guhathakurta collaborates with Chen from Georgia Tech. Along with being a professor in the School of City & Regional Planning, Guhathkurta is director of Tech’s Master of Science in Urban Analytics program and the Center for Spatial Planning and Analytics and Visualization.

In the end, M.S. student Meenakshi Manikandaswamy became the tournament champion on Feb. 21 by defeating Ph.D. candidate Ziyi (Francis) Yin. M.S. student Anisha Tripathi received the tournament’s best new player award.

“I am thrilled to be this tournament’s champion,” Manikandaswamy said. “I appreciate CSE for organizing this event and giving me the opportunity to participate. The tournament was well run and a fun experience.”

Manikandaswamy studies cybersecurity under Professor Mustaque Ahamad, who holds joint appointments in the School of Computer Science and the School of Cybersecurity and Privacy.

Manikandaswamy’s work is “red team” related. This designation means she takes an adversarial role in her research. She tries to break into systems to help experts understand and strengthen theM.S. student Meir platforms. Her group is developing a tool that scans for vulnerabilities on websites. The tool is easy to use and does not require computer science or cybersecurity expertise. 

“Chess helps to think about the opponent’s point of view, thinking about how they are trying to attack you and how to protect yourself. That definitely applies to my master’s.”

Professor Felix Herrmann advises tournament runner-up Yin. Herrmann is jointly appointment with the Schools of Earth and Atmospheric Sciences, Electrical and Computer Engineering, and CSE. 

Together, they develop computational methods for seismic imaging and modeling. Yin studies machine learning approaches to solve inverse problems and quantify uncertainty in geoscientific applications.

“Chess became a habit of ours. I think it’s a great way to take some time to relax and take a break from our research,” Yin said. “It’s a game that unites us CSE students so that we learn about each other as people and become close friends, not just colleagues.”

Manikandaswamy defeated M.S. student Aditya Kane and Yin defeated M.S. student Karan Nahar to reach the finals.

The tournament began in January, opening with four weekly matches of round-robin play. These results determined the seeding for the semifinals. 

The chess tournament’s starting field of 25 players included School of CSE students, faculty, and staff members.

The CSE Graduate Student Association (GSA) organized and managed the tournament. To encourage participation, CSE GSA solicited participation outside the School of CSE. This included the Georgia Tech Chess Club, which was how Manikandaswamy learned about the tournament.

“I think this tournament went really well, I was surprised with how much interest there is in chess across the school,” said Ph.D. student Aranya Banerjee, president of CSE GSA. “We definitely hope to do something like this again in the future.”

One of those graduates is Mikhail (Michael) Isaev, who earned a Ph.D. in computer science from the School of Computational Science and Engineering (CSE). After he walks across the stage and accepts his diploma at McCamish Pavilion on May 2, Isaev’s next move is to work at NVIDIA Research as a research scientist.

Advised by School of CSE Professor Rich Vuduc, Isaev’s research interests lie at the intersection of computer architecture, high-performance computing (HPC), and deep learning. He focuses on deep learning workload analysis and software/hardware co-design of large-scale deep learning systems.

Isaev received notable recognition for his co-design research at ModSim’22, where he won the Dr. Sudhakar Yalamanchili Award. He earned the “Sudha” Award for his research on ParaGraph.

ParaGraph provides an automated way to emulate application software in ways that a network simulator can understand. The tool makes co-design a bilateral process, facilitating better supercomputing applications and closing the gap for hardware and software experts.

While the award recognizes researchers for their contributions to the computer modeling and simulation field, it carried much more sentimental meaning to Isaev. Yalamanchili was a Georgia Tech faculty member who died in 2019. Isaev and his collaborators personally knew and worked with Yalamanchili.

“I felt very honored to receive the award,” Isaev said. “I had the pleasure to meet and talk to Sudha, so it felt great to bring home this award in his name and, in a way, give back to Georgia Tech.”

Another meaningful project Isaev worked on was Calculon, a tool for co-design optimization of large language models (LLMs).

Calculon analyzes large co-design spaces of hardware and software configurations. This ability  progresses the discovery of new, and sometimes surprising, configurations that might outperform current methods.

By focusing specifically on LLMs, Calculon modeled more aspects of performance optimization at greater accuracy and speeds several orders of magnitude faster than ParaGraph.

As tech companies train and retrain LLMs on tens of thousands of graphics processing units (GPUs), search spaces grow larger and become more vast as larger systems are introduced.

Isaev’s work on Calculon is timely since there was no open-source tool that could quickly navigate this space and determine the best configurations. Calculon is a simple, yet effective, tool that can do the job fast and efficiently, sparking interest from many companies and research community

Isaev presented Calculon at conferences like Supercomputing 2023, ModSim’23, and the ASSYST workshop at ISCA 2023. He also gave talks at NVIDIA, Google, Microsoft, IBM, and the Department of Energy to share his research.

The talks at NVIDIA, Google, and Microsoft were notable since Isaev interned as a Ph.D. student at the companies. He has also interned at HP Labs and Meta.

Isaev interned at NVIDIA four times, three with the company’s Network Research Group. That is where he worked on ParaGraph and Calculon under one of his mentors, Nic McDonald.

“While Calculon won no award, I feel it was more well-received and got better traction with the HPC community,” Isaev said.

“This is partly due to Calculon being a product of collaboration between my internships and conference presentations. It truly is a tool created by HPC researchers for HPC research.”

The banquet celebrated the college community for an exemplary academic year and recognized the most distinguished individuals of 2023-2024. For Alex Orso, the reception was a high-water mark in his role as interim dean.

“I always say that the best part about my job is to brag about the achievements and accolades of my colleagues,” said Orso.

“It is my distinct honor and privilege to recognize these award winners and the collective success of the College of Computing.”

Orso’s colleagues from the School of Computational Science and Engineering (CSE) were among the celebration’s honorees. School of CSE students, faculty, and alumni earning awards this year include:

• Grace Driskill, M.S. CSE student - The Donald V. Jackson Fellowship
• Harshvardhan Baldwa, M.S. CSE student - The Marshal D. Williamson Fellowship
• Mansi Phute, M.S. CS student- The Marshal D. Williamson Fellowship
• Assistant Professor Chao Zhang- Outstanding Junior Faculty Research Award
• Nazanin Tabatbaei, teaching assistant in Associate Professor Polo Chau’s CSE 6242 Data & Visual Analytics course- Outstanding Instructional Associate Teaching Award
• Rodrigo Borela (Ph.D. CSE-CEE 2021), School of Computing Instruction Lecturer and CSE program alumnus - William D. "Bill" Leahy Jr. Outstanding Instructor Award
• Pratham Metha, undergraduate student in Chau’s research group- Outstanding Legacy Leadership Award
• Alexander Rodriguez (Ph.D. CS 2023), School of CSE alumnus - Outstanding Doctoral Dissertation Award

At the Institute level, Georgia Tech recognized Driskill, Baldwa, and Phute for their awards on April 10 at the annual Student Honors Celebration.

Driskill’s classroom achievement earned her a spot on the 2024 All-ACC Indoor Track and Field Academic Team. This follows her selection for the 2023 All-ACC Academic Team for cross country.

Georgia Tech’s Center for Teaching and Learning released in summer 2023 the Class of 1934 Honor Roll for spring semester courses. School of CSE awardees included Assistant Professor Srijan Kumar (CSE 6240: Web Search & Text Mining), Lecturer Max Mahdi Roozbahani (CS 4641: Machine Learning), and alumnus Mengmeng Liu (CSE 6242: Data & Visual Analytics).

Accolades and recognition of School of CSE researchers for 2023-2024 expounded off campus as well.

School of CSE researchers received awards off campus throughout the year, a testament to the reach and impact of their work.

School of CSE Ph.D. student Gaurav Verma kicked off the year by receiving the J.P. Morgan Chase AI Research Ph.D. Fellowship. Verma was one of only 13 awardees from around the world selected for the 2023 class.

Along with seeing many of his students receive awards this year, Polo Chau attained a 2023 Google Award for Inclusion Research. Later in the year, the Institute promoted Chau to professor, which takes effect in the 2024-2025 academic year.

Schmidt Sciences selected School of CSE Assistant Professor Kai Wang as an AI2050 Early Career Fellow to advance artificial intelligence research for social good. By being part of the fellowship’s second cohort, Wang is the first ever Georgia Tech faculty to receive the award.

School of CSE Assistant Professor Yunan Luo received two significant awards to advance his work in computational biology. First, Luo received the Maximizing Investigator’s Research Award (MIRA) from the National Institutes of Health, which provides $1.8 million in funding for five years. Next, he received the 2023 Molecule Make Lab Institute (MMLI) seed grant.

Regents’ Professor Surya Kalidindi, jointly appointed with the George W. Woodruff School of Mechanical Engineering and School of CSE, was named a fellow to the 2023 class of the Department of Defense’s Laboratory-University Collaboration Initiative (LUCI).

2023-2024 was a monumental year for Assistant Professor Elizabeth Qian, jointly appointed with the Daniel Guggenheim School of Aerospace Engineering and the School of CSE.

The Air Force Office of Scientific Research selected Qian for the 2024 class of their Young Investigator Program. Earlier in the year, she received a grant under the Department of Energy’s Energy Earthshots Initiative.

Qian began the year by joining 81 other early-career engineers at the National Academy of Engineering’s Grainger Foundation Frontiers of Engineering 2023 Symposium. She also received the Hans Fischer Fellowship from the Institute for Advance Study at the Technical University of Munich.

It was a big academic year for Associate Professor Elizabeth Cherry. Cherry was reelected to a three-year term as a council member-at-large of the Society of Industrial and Applied Mathematics (SIAM). Cherry is also co-chair of the SIAM organizing committee for next year’s Conference on Computational Science and Engineering (CSE25).

Cherry continues to serve as the School of CSE’s associate chair for academic affairs. These leadership contributions led to her being named to the 2024 ACC Academic Leaders Network (ACC ALN) Fellows program.

School of CSE Professor and Associate Chair Edmond Chow was co-author of a paper that received the Test of Time Award at Supercomputing 2023 (SC23). Right before SC23, Chow’s Ph.D. student Hua Huang was selected as an honorable mention for the 2023 ACM-IEEE CS George Michael Memorial HPC Fellowship.

Advances in computing techniques though have ushered in a new age of understanding the macromolecules that form life as we know it. Now, one Georgia Tech research group is receiving well-deserved accolades for their applications in data science and machine learning toward single-cell omics research. 

Students studying under Xiuwei Zhang, an assistant professor in the School of Computational Science and Engineering (CSE), received awards in April at the Atlanta Workshop on Single-cell Omics (AWSOM 2023).

School of CSE Ph.D. student Ziqi Zhang received the best oral presentation award, while Mihir Birfna, an undergraduate student majoring in computer science, took the best poster prize.

Along with providing computational tools for biological researchers, the group’s papers presented at AWSOM 2023 could benefit populations of people as the research could lead to improved disease detection and prevention. They can also provide a better understanding of causes and treatments of cancer and new ability to accurately simulate cellular processes.

“I am extremely proud of the entire research group and very thankful of their work and our teamwork within our lab,” said Xiuwei Zhang. “These awards are encouraging because it means we are on the right track of developing something that will contribute both to the biology community and the computational community.”

Ziqi Zhang presented the group’s findings of their deep learning framework called scDisInFact, which can carry out multiple key single cell RNA-sequencing (scRNA-seq) tasks all at once and outperform current models that focus on the same tasks individually.

The group successfully tested scDisInFact on simulated and real Covid-19 datasets, demonstrating applicability in future studies of other diseases.

Bafna’s poster introduced CLARIFY, a tool that connects biochemical signals occurring within a cell and intercellular communication molecules. Previously, the inter- and intra-cell signaling were often studied separately due to the complexity of each problem.

Oncology is one field that stands to benefit from CLARIFY. CLARIFY helps to understand the interactions between tumor cells and immune cells in cancer microenvironments, which is crucial for enabling success of cancer immunotherapy.

At AWSOM 2023, the group presented a third paper on scMultiSim. This simulator generates data found in multi-modal single-cell experiments through modeling various biological factors underlying the generated data. It enables quantitative evaluations of a wide range of computational methods in single-cell genomics. That has been a challenging problem due to lack of ground truth information in biology, Xiuwei Zhang said. 

“We want to answer certain basic questions in biology, like how did we get these different cell types like skin cells, bone cells, and blood cells,” she said. “If we understand how things work in normal and healthy cells, and compare that to the data of diseased cells, then we can find the key differences of those two and locate the genes, proteins, and other molecules that cause problems.”

Xiuwei Zhang’s group specializes in applying machine learning and optimization skills in analysis of single-cell omics data and scRNA-seq methods. Their main interest area is studying mechanisms of cellular differentiation— the process when young, immature cells mature and take on functional characteristics.

A growing, yet effective approach to research in molecular biology, scRNA-seq gives insight of existence and behavior of different types of cells. This helps researchers better understand genetic disorders, detect mechanisms that cause tumors and cancer, and develop new treatments, cures, and drugs. 

If microenvironments filled with various macromolecules and genetic material are considered datasets, the need for researchers like Xiuwei Zhang and her group is obvious. These massive, complex datasets present both challenges and opportunities for the group experienced in computational and biological research.

Collaborating authors include School of CSE Ph.D. students Hechen Li and Michael Squires, School of Electrical and Computer Engineering Ph.D. student Xinye Zhao, Wallace H. Coulter Department of Biomedical Engineering Associate Professor Peng Qiu, and Xi Chen, an assistant professor at Southern University of Science and Technology in Shenzhen, China.

The group’s presentations at AWSOM 2023 exhibited how their work makes progress in biomedical research, as well as advancing scientific computing methods in data science, machine learning, and simulation.

scDisInFact is an optimization tool that can perform batch effect removal, condition-associated key gene detection, and perturbation, which is made possible by considering major variation factors in the data. Without considering all these factors, current models can only do these tasks individually, but scDisInFact can do each task better and all at the same time.

CLARIFY delves into how cells employ genetic material to communicate internally, using gene regulatory networks (GRNs) and externally, called cell-cell interactions (CCIs). Many computational methods can infer GRNs and inference methods have been proposed for CCIs, but until CLARIFY, no method existed to infer GRNs and CCIs in the same model.

scMultiSim simulations perform closer to real-world conditions than current simulators that model only one or two biological factors. This helps researchers more realistically test their computational methods, which can guide directions for future method development.

Whether they be computer scientists, biologists, or non-academics alike, the advantage of interdisciplinary and collaborative research, like Xiuwei Zhang’s group, is its wide-reaching impact that advances technology to improve the human condition.

“We’re exploring the possibilities that can be realized by advanced computational methods combined with cutting edge biotechnology,” said Xiuwei Zhang. “Since biotechnology keeps evolving very fast and we want to help push this even further by developing computational methods, together we will propel science forward.”

Ph.D. student Alec Helbling is the creator of ManimML, a tool that renders common ML concepts into animation. This development will enable new ML technologies by allowing designers to see and share their work in action. 

Helbling presented ManimML at IEEE VIS, the world’s highest-rated conference for visualization research and second-highest rated for computer graphics. It received so much praise at the conference that it won the venue’s prize for best poster. 

“I was quite surprised and honored to have received this award,” said Helbling, who is advised by School of Computational Science and Engineering Associate Professor Polo Chau.

“I didn't start ManimML with the intention of it becoming a research project, but because I felt like a tool for communicating ML architectures through animation needed to exist.”

[RELATED: Polo Chau is One of Three College of Computing Faculty to Receive 2023 Google Award for Inclusion Research]

ManimML uses animation to show ML developers how their algorithms work. Not only does the tool allow designers to watch their projects come to life, but they can also explain existing and new ML techniques to broad audiences, including non-experts.

ManimML is an extension of the Manim Community library, a Python tool for animating mathematical concepts. ManimML connects to the library to offer a new capability that animates ML algorithms and architectures.

Helbling chose familiar platforms like Python and Manim to make the tool accessible to large swaths of users varying in skill and experience. Enthusiasts and experts alike can find practical use in ManimML considering today’s widespread interest and application of ML.

“We know that animation is an effective means of instruction and learning,” Helbling said. “ManimML offers that ability for ML practitioners to easily communicate how their systems work, improving public trust and awareness of machine learning.”

ManimML overcomes what has been an elusive approach to visualizing ML algorithms. Current techniques require developers to create custom animations for every specific algorithm, often needing specialized software and experience.

ManimML streamlines this by producing animations of common ML architectures coded in Python, like neural networks.

A user only needs to specify a sequence of neural network layers and their respective hyperparameters. ManimML then constructs an animation of the entire network.

“To use ManimML, you simply need to specify an ML architecture in code, using a syntax familiar to most ML professionals,” Helbling said. “Then it will automatically generate an animation that communicates how the system works.”

ManimML ranked as the best poster from a field of 49 total presentations. IEEE VIS 2023 occurred Oct. 22-27 in Melbourne, Australia. This event marks the first time IEEE held the conference in the Southern Hemisphere.

ManimML has more than 23,000 downloads and a demonstration on social media has hundreds of thousands of views.

ManimML is open source and available at: https://github.com/helblazer811/ManimML

The workshop focused AI technology toward optimization of complex systems. Presentations of climatological and electromagnetic simulations showed these techniques resulted in more efficient and accurate computer modeling. The workshop also progressed AI research itself since AI models typically are not well-suited for optimization tasks.

The School of Computational Science and Engineering (CSE) and Institute for Data Engineering and Science jointly sponsored the workshop.

School of CSE Assistant Professors Peng Chen and Raphaël Pestourie led the workshop’s organizing committee and moderated the workshop’s two panel discussions. The duo also pitched their own research, highlighting potential of scientific AI.

Chen shared his work on derivative-informed neural operators (DINOs). DINOs are a class of neural networks that use derivative information to approximate solutions of partial differential equations. The derivative enhancement results in neural operators that are more accurate and efficient. 

During his talk, Chen showed how DINOs makes better predictions with reliable derivatives. These have potential to solve data assimilation problems in weather and flooding prediction. Other applications include allocating sensors for early tsunami warnings and designing new self-assembly materials.

All these models contain elements of uncertainty where data is unknown, noisy, or changes over time. Not only is DINOs a powerful tool to quantify uncertainty, but it also requires little training data to become functional.

“Recent advances in AI tools have become critical in enhancing societal resilience and quality, particularly through their scientific uses in environmental, climatic, material, and energy domains,” Chen said. 

“These tools are instrumental in driving innovation and efficiency in these and many other vital sectors.”

[Related: Machine Learning Key to Proposed App that Could Help Flood-prone Communities]

One challenge in studying complex systems is that it requires many simulations to generate enough data to learn from and make better predictions. But with limited data on hand, it is costly to run enough simulations to produce new data.

At the workshop, Pestourie presented his physics-enhanced deep surrogates (PEDS) as a solution to this optimization problem. 

PEDS employs scientific AI to make efficient use of available data while demanding less computational resources. PEDS demonstrated to be up to three times more accurate than models using neural networks while needing less training data by at least a factor of 100. 

PEDS yielded these results in tests on diffusion, reaction-diffusion, and electromagnetic scattering models. PEDS performed well in these experiments geared toward physics-based applications because it combines a physics simulator with a neural network generator.

“Scientific AI makes it possible to systematically leverage models and data simultaneously,” Pestourie said. “The more adoption of scientific AI there will be by domain scientists, the more knowledge will be created for society.”

[Related: Technique Could Efficiently Solve Partial Differential Equations for Numerous Applications]

Study and development of AI applications at these scales require use of the most powerful computers available. The workshop invited speakers from national laboratories who showcased supercomputing capabilities available at their facilities. These included Oak Ridge National Laboratory, Sandia National Laboratories, and Pacific Northwest National Laboratory.

The workshop hosted Georgia Tech faculty who represented the Colleges of Computing, Design, Engineering, and Sciences. Among these were workshop co-organizers Yan Wang and Ebeneser Fanijo. Wang is a professor in the George W. Woodruff School of Mechanical Engineering and Fanjio is an assistant professor in the School of Building Construction.

The workshop welcomed academics outside of Georgia Tech to share research occurring at their institutions. These speakers hailed from Emory University, Clemson University, and the University of California, Berkeley.

The new HPC system, valued at $5.3 million, will support data-driven research in astrophysics, computational biology, health sciences, computational chemistry, materials and manufacturing, and numerous other projects. It will also be used for research that improves the energy efficiency and performance of the HPC systems themselves.

The effort was led by Srinivas Aluru, co-executive director of the Institute for Data Engineering and Science (IDEaS) and professor in the School of Computational Science and Engineering.

“This project is exciting from many perspectives, but especially how it is pushing forward data and high performance computing research infrastructure at Georgia Tech,” said Aluru. “It reflects the teamwork of dozens of faculty, and also supports the work of over 50 research scientists and 200 graduate students.”

Also central to the award are Surya Kalidindi, professor in the George W. Woodruff School of Mechanical Engineering; Charles David Sherrill, professor in the School of Chemistry and Biochemistry; Deirdre Shoemaker, professor in the School of Physics, Rich Vuduc, associate professor in the School of Computational Science and Engineering, and Marilyn Wolf, professor in the School of Electrical and Computer Engineering and the Rhesa "Ray" S. Farmer, Jr. Distinguished Chair in Embedded Computing Systems.

The system is anticipated to begin operations in 2019, and will surpass the current campus capabilities. It will be used for applications that require large memories or local storage, provide modern GPU accelerators, and large storage capacity for data and simulation results.

HPC simulations—one of several uses of the new system—are important for solving large-scale problems in hours or days, rather than months or years. Applications of these include detection of gravitational waves, climate models, performance of materials used in manufacturing or healthcare, and drug discovery.

The new HPC acquisition will coincide with the unveiling of an 80,000 sq.ft. data center in the new Coda building. Coda, the 21-story, 650,000 sq.ft. new addition to Technology Square, lies adjacent to the Georgia Tech campus and major fiber pathways connecting the Southeast.

“We worked to ensure the acquisition is well-timed to be the pivotal supercomputer in the Coda data center,” said Aluru.

 “This award is a major boon for interdisciplinary research at Georgia Tech, one that will also be a valuable addition to the HPC-based research community nationally. With Coda opening its doors soon, this supercomputer will become the premier computing resource at Georgia Tech,” said Executive Vice President for Reseach Chaouki Abdallah.

IDEaS and many users of the new equipment will be based in Coda. System management will be handled by the Partnership for an Advanced Computing Environment, or PACE, also residing in Coda.

Research enabled by new system will aid several national initiatives in big data, including strategic computing, materials genome, manufacturing partnerships, NSF-supported observatories such as the LIGO gravitational wave observatory, and the South Pole neutrino observatory known as IceCube.

Researchers from all levels—from early career to undergraduate students—will be the target of training and outreach. Several Georgia Tech researchers and partner institutions will be awarded time on the equipment based on scientific merit and on the national significance of proposed problems.

One-fifth of the system capacity will be dedicated to the research activities of regional partners including minority serving institutions. Other users can participate through XSEDE, a national network of NSF supercomputers that scientists use to interactively share computing resources, data and expertise.

“High performance computing is a priority area for Georgia Tech. Data analysis, simulations, and computational predictive tools are essential elements of modern science, engineering and design. High performance computing is the laboratory of the 21st century,” said Rafael L. Bras, provost and executive vice president for Academic Affairs and K. Harrison Brown Family Chair. “It is extremely satisfying to see a multidisciplinary team successfully work together to make this acquisition a reality. That, after all, is the spirit and culture of Coda.”

CSE graduates take the stage Dec. 15 at McCamish Pavilion for two ceremonies to officially “get out” of Tech. 17 Ph.D. graduates don their hoods at the morning Convocation while 43 M.S. graduates receive their diplomas in the afternoon. 

The doctoral degree is the highest academic degree a professional can attain, requiring years of dedicated work to demonstrate mastery in their field of study. CSE doctoral graduates will pursue careers in academia, national laboratories, industry and the private sector. Ph.D. graduates include:

• Brad Baker (Ph.D. CSE-CSE 2023), advised by Distinguished University Professor Vince Calhoun
• Binghong Chen (Ph.D. ML-CSE 2023), co-advised by School of CSE Assistant Professor Chao Zhang and Adjunct Professor Le Song
• Mikhail Isaev (Ph.D. CS-CSE 2023), advised by School of CSE Professor Rich Vuduc
• David Kartchner (Ph.D. CSE-CSE 2023), advised by Cassie Mitchell, associate professor in the Wallace H. Coulter Department of Biomedical Engineering
• Patrick Lavin (Ph.D. CSE-CSE 2023), advised by School of CSE Professor Rich Vuduc
• Noah Lewis (Ph.D. CSE-CSE 2023), advised by Distinguished University Professor Vince Calhoun
• Haekyu Park (Ph.D. CS-CSE 2023), advised by School of CSE Associate Professor Polo Chau
• Alexander Rodríguez (Ph.D. CS-CSE 2023), advised by School of CSE Associate Professor B. Aditya Prakash
• Debbrata Saha (Ph.D. CSE-CSE 2023), advised by Distinguished University Professor Vince Calhoun
• Apaar Shanker (Ph.D. CSE-CSE 2023), advised by Regents’ Professor Surya Kalidindi, joint with the George W. Woodruff School of Mechanical Engineering and School of CSE
• Pranav Shetty (Ph.D. ML-CSE 2023), co-advised by School of CSE Assistant Professor Chao Zhang and School of Materials Science and Engineering Professor Rampi Ramprasad
• Shruti Shivakumar (Ph.D. CSE-CSE 2023), advised by School of CSE Regents’ Professor Srinivas Aluru
• Kyle Schau (Ph.D. CSE-AE 2023), advised by Joseph Oefelein, professor in the Daniel Guggenheim School of Aerospace Engineering
• Neda Tavakoli (Ph.D. CS-CSE 2023), advised by School of CSE Regents’ Professor Srinivas Aluru
• Michael Thomas (Ph.D. CSE-CEE 2023), advised by School of Civil and Environmental Engineering (CEE) Professor John Taylor
• Zhichao Wang (Ph.D. CSE-ME 2023), co-advised by Professor Emeritus David Rosen and Professor Shreyes Melkote, both in the George W. Woodruff School of Mechanical Engineering
• Chunxing Yin (Ph.D. CS-CSE 2023), advised by School of CSE Professor Rich Vuduc

Ten CSE Ph.D. students completed their M.S. degree this fall, and will continue their studies at Georgia Tech. They are:

• Ziqi Zhang (M.S. CSE-CSE 2023), advised by School of CSE Assistant Professor Xiuwei Zhang
• Trevor McCrary (M.S. CSE-CSE 2023), advised by School of CSE Professor Rich Vuduc
• Haotian Sun (M.S. CSE-CSE 2023), co-advised by School of CSE Assistant Professors Chao Zhang and Bo Dai
• Yue Yu (M.S. CSE-CSE 2023), advised by School of CSE Assistant Professor Chao Zhang
• Srinivas Vivek Bokkisa (M.S. CSE-CEE 2023), advised by School of CEE Assistant Professor Jorge Macedo
• Zefang Chen (M.S. CSE-CEE 2023), advised by School of CEE Associate Professor Xing Xie
• Modi Zhu (M.S. CSE-CEE 2023), advised by School of CEE Associate Professor Jingfeng Wang
• Shengjun Xi (M.S. CSE-MATH 2023), advised by School of Earth and Atmospheric Sciences (EAS) Professor Yuhang Wang
• Fanghe Zhao (M.S. CSE-MATH 2023), advised by School of EAS Professor Yuhang Wang
• Zhaonan Liu (M.S. CSE-MSE 2023), co-advised by Professor Emeritus Ben Wang and Professor Chuck Zhang, both in the H. Milton Stewart School of Industrial and Systems Engineering

The 33 M.S. CSE graduates represent all three Colleges of Computing, Science, and Engineering participating in Georgia Tech’s CSE program. They are:

Jaiveer Bhaidasna (M.S. CSE-CSE 2023)

Sarthak Chaturvedi (M.S. CSE-CSE 2023)

Xiaocheng Chen (M.S. CSE-CSE 2023)

Dheeraj Eidnani (M.S. CSE-CSE 2023)

Jinyoung Eum (M.S. CSE-CSE 2023)

Zihong Hao (M.S. CSE-CSE 2023)

Omar Jiménez (M.S. CSE-CSE 2023)

Bao Li (M.S. CSE-CSE 2023)

Qian Li (M.S. CSE-CSE 2023)

Qilin Li (M.S. CSE-CSE 2023)

Hanzhang Liu (M.S. CSE-CSE 2023)

Xianyi Nie (M.S. CSE-CSE 2023)

Qianyu Sun (M.S. CSE-CSE 2023)

Ho Cheong Tang (M.S. CSE-CSE 2023)

Nanzhen Yan (M.S. CSE-CSE 2023)

Zhenhao Jing (M.S. CSE-AE 2023)

Haojun Song (M.S. CSE-BIOL 2023)

Guangyu Min (M.S. CSE-CEE 2023)

Chenyu Wang (M.S. CSE-CEE 2023)

Xiangyu Wang (M.S. CSE-CEE 2023)

Jiajie Wen (M.S. CSE-CSE 2023)

Jiarui Yan (M.S. CSE-CEE 2023)

Daniel Gordon Beltran (M.S. CSE-ISYE 2023)

Ruoyu Huang (M.S. CSE-ISYE 2023)

Wenchao Wu (M.S. CSE-ISYE 2023)

Judu Xu (M.S. CSE-ISYE 2023)

Qianru Yu (M.S. CSE-ISYE 2023)

Chen Chen (M.S. CSE-MATH 2023)

Shichen Li (M.S. CSE-MATH 2023)

Zongrun Li (M.S. CSE-MATH 2023)

Bijie Liu (M.S. CSE-MATH 2023)

Yifan Ma (M.S. CSE-ME 2023)

Zhichao Wang (M.S. CSE-ME 2023)

Spencer Bryngelson’s research group manages the Multi-Component Flow Code (MFC) software package. His group was one of ten teams selected by Oak Ridge Leadership Computing Facility (OLCF) to optimize their simulator in a hackathon held Oct. 31 – Nov. 3.

During the hackathon, the group crafted MFC to run on Frontier, the world’s only exascale supercomputer. With newfound experience on Frontier, the group is poised to work with exascale machines scheduled to come online soon, like Aurora and El Capitan.

The team used the hackathon to refine MFC, making it more capable for medical, aeronautical, and defense applications.

MFC simulates compressible multiphase flows, a key issue in many engineering problems. MFC’s high quality simulations help engineers improve technologies.

Potential applications include:

• Needle-free drug injection
• Improved artificial heart pumps and valves
• Erosion-resistant aircraft surfaces
• Quieter submarines

These simulations require a blend of software, like MFC, and the largest supercomputers the government can bring to bear.

“MFC is a versatile solver that accounts for flavors of fluid flows like acoustics, surface tension, phase change, high Mach shock waves, and so on,” said Bryngelson, an assistant professor in the School of Computational Science and Engineering (CSE).

“MFC has other tricks. We can model solid and porous materials, which have been applied to simulations of the breakup of kidney stones for new lithotripsy therapies.”

Before the hackathon, Bryngelson's group already scaled MFC to the entirety of Summit, another OLCF supercomputer. Summit is based on NVIDIA GPUs, and was the world's fastest supercomputer from November 2018 to June 2020. Testing MFC on Frontier showed the group how the software performs at its peak on over 30,000 AMD GPUs. As a result, the group configured software to the industry’s two leading hardware vendors.

During the hackathon, the group resolved a compiler issue that slowed performance by five times. They squashed the bug by hacking a fix in the machine bytecode.

The issue caught the attention of Cray, a manufacturer of Frontier and its compilers. The performance bug likely infests other applications using Frontier. So, MFC’s experience will help Cray further study and prevent similar issues for future users.

[Related: New Hardware Brings Students Closer to Exascale Computing]

“Running on Frontier is about discovering the most challenging engineering problems we can solve, “Bryngelson said. “We’ve tooled MFC to perform exceptionally large simulations on the next generation of leadership-class supercomputers, like Frontier.”

Oak Ridge National Laboratory (ORNL) and the U.S. Department of Energy jointly operate Frontier. It jumped to first place on the Top500 in June 2022 soon after becoming operational and proving its exascale capability that May.  

Frontier can compute over one quintillion calculations in a second as an exascale supercomputer. If a person completed a simple math problem every second, that person would need 31.6 billion years to match what Frontier can do in a second.

The group accessed research scientists and engineers as mentors throughout the hackathon. Notable assistance came from Steve Abbott of Cray, Brian Cornille of AMD, and Reuben Budiardja of ORNL.

MFC stems from Bryngelson’s time at Caltech, dating back to 2018. He worked on the project with Tim Colonius, and the project has collaborators around the world.

Bryngelson’s students maintain MFC and participated in the hackathon. These included Ph.D. students Ben Wilfong and Anand Radhakrishnan and undergraduate students Henry Le Berre and Ansh Gupta.

“Through the hackathon, the group acquired an education that may not be possible otherwise. They now have experience using a specialized tool to use toward their research,” Bryngelson said.

“Everything we’re talking about right now is very new, and there is little track record to fall back on, so we’re helping find problems and hacking fixes as we go.”

Oak Ridge National Laboratory (ORNL) held its annual Core Universities AI Workshop at Georgia Tech Oct. 31 – Nov. 1. The workshop hosted AI experts from eight research universities, plus ORNL and the Department of Energy, to present new research and trends relevant to the field.

Georgia Tech’s School of Computational Science and Engineering (CSE) and Institute for Data Engineering and Science (IDEaS) jointly sponsored this year’s workshop. The workshop host rotates every year among ORNL core universities.

“Participants gained insights into state-of-the-art AI models and methods that prioritize safety, trustworthiness, and energy efficiency,” said Ramki Kannan (Ph.D. CS 2016), a research scientist at ORNL who helped organize the workshop.

“They also learned how to apply these models and algorithms to scientific applications, including the utilization of recent FAIR datasets and relevant policies.”

[RELATED: Biomedical Analytics Research Earns Team Gordon Bell Prize Nomination]

The workshop dealt with topics related to the large-scale application of AI. It included 12 research presentations, three keynote speeches, and two panel discussions.

The workshop began a day after release of President Joe Biden’s executive order calling for safe development and use of AI. Attendees seized the moment to discuss AI safety, trustworthiness, and privacy throughout the workshop. Three talks and one of the panels focused on these topics, and speakers referenced the subjects throughout their presentations.

Other talks focused on the application of AI as a tool to overcome today’s most pressing scientific challenges.

Two researchers discussed using AI to overcome climate change. Chris Reberg-Horton of North Carolina State University talked about optimizing AI for plant science. David Sholl of ORNL discussed how AI can solve decarbonization challenges.

“The fields of AI and various science and engineering disciplines have been experiencing rapid growth independently. However, the true potential lies in the synergy between these fields, which can translate into significant societal impact,” said Kannan, a Georgia Tech alumnus who studied under School of CSE Regents’ Professor and Chair Haesun Park.

“The future workforce across disciplines will require scientists and engineers proficient in the using AI tools to enhance productivity and achieve substantial impact.”

Networking served a vital function at the workshop and collaboration was a common discussion point during and in between presentations.

President Biden's executive order emphasizes united AI research efforts, bringing together the private sector, academia, national labs, and the government. Several speakers at the workshop echoed this collaborative approach.

ORNL’s annual AI workshop is one way that national laboratories and research universities are answering that call.

ORNL maintains formal partnerships with academic institutions, which the lab refers to as its core universities. These partnerships help universities understand and address the educational needs of national laboratories and the government in training the next generation of scientists.

ORNL’s core universities include Duke University, Florida State University, Georgia Tech, North Carolina State University, University of Tennessee, Vanderbilt University, University of Virginia, and Virginia Tech.

“Recent times have witnessed the profound impact of interdisciplinary research, which is often nurtured through collaborations,” said Kannan, who leads ORNL’s Discrete Algorithms Group.

“Scientists, researchers, and academicians participating in this workshop will leverage the connections made here to shape their research and publication agendas for the coming years.”

Professor Carl DiSalvo and Associate Professor Andrea Parker from the School of Interactive Computing and Associate Professor Polo Chau from the School of Computational Science and Engineering made the list of recipients for the 2023 Google Award for Inclusion Research (AIR).

Google AIR recognizes significant contributions in computing and technology that address the needs of historically marginalized groups. For 2023, Google sought to identify research that focused on accessibility, collaboration, collective- and society-centered AI, and the impact of AI on education. The award includes a $60,000 grant for each recipient.

Working alongside Ph.D. student Vanessa Oguamanam, Parker seeks to develop a mobile app that helps perinatal black women handle stress management. The award will help fund a mixed-method study to evaluate the app's efficacy.

"This award will enable me to establish new collaborations with two community partners doing vital work to address maternal health inequities within Georgia," Parker said. "Culturally relevant mental health resources are sorely lacking yet vitally needed to support a population that has disproportionately experienced social and structural stressors and mental health conditions."

Working with Ph.D. candidate Sara Espinosa-Milkes, DiSalvo collaborates with online resellers in co-designing speculative and applied tools and practices to enable better navigation of the algorithmic ecosystems of online second-hand economies.

DiSalvo said sites such as PoshMark, Depop, and ThredUp require resellers to navigate multiple platforms, applications, and algorithms. Their research helps resellers make sense of these technologies and construct tactics to move between these platforms.  

"The Google AIR gift is an amazing contribution and opportunity," DiSalvo said. "It provides a pathway for collaborating with colleagues at Google and much-needed support for the critical and experimental work we are trying to accomplish. It enables us to share work with researchers at Google that is participatory, critical, and grounded in humanities theories and arts methods." 

Chau received the Google AIR award for his work in AI and education, that led to the creation of a unique tool called VisGrader.

VisGrader helps instructors and students alike by automatically grading JavaScript data visualizations. The tool enhances student learning by providing immediate feedback on their designs, allowing them to improve their work iteratively.  

Ph.D. student Matthew Hull leads the project comprised of current and former teaching staff members of Georgia Tech's Data and Visual Analytics course. So far, VisGrader has auto-graded over 72,000 submissions from 4,000 students spanning four semesters since 2021. 

"I am very grateful for Google's recognition of the breakthrough research innovations at Georgia Tech and Georgia Tech's ongoing vision and efforts to broaden education access to learners around the world," Chau said. 

"Next, we are exploring how we can extend the approach to data visualizations created in other languages and platforms and how generative AI may help provide even more informative feedback to our students."

School of Interactive Computing Communications Officer Nathan Deen and School of Computational Science and Engineering Communications Officer Bryant Wine contributed to this story. Contact Nathan Deen at ndeen6@gatech.edu. Contact Bryant Wine at bwine3@gatech.edu.

Bo Dai started at Georgia Tech on May 15 and Raphaël Pestourie began on July 1. Kai Wang and Helen Xu will join the School of CSE in Spring 2024. With these hires, the School of CSE now comprises 23 full-time faculty, 15 who are assistant professors.

Before joining academia, Bo Dai worked as a staff research scientist at Google Brain. He will have a part-time affiliation with Google while at Georgia Tech.

Dai is the first CSE alumnus to return to the School as a faculty member. He earned his Ph.D. in computational science and engineering in 2018, studying under Professor Le Song.

Dai’s research focuses on developing principled and practical machine learning (ML) techniques for real-world applications. This includes creating better reinforcement learning models and data-driven decision-making models.

“I am honored and grateful to have the privilege of returning to my alma mater as a faculty member,” Dai said. “I am excited to use this unique opportunity to inspire and guide the next generation of students, and to give back and contribute to Georgia Tech.”

Raphaël Pestourie comes to CSE from the Massachusetts Institute of Technology (MIT), where he was a postdoctoral associate in the mathematics department. He arrives at Tech with a diverse educational background that fosters new insights for the ML field.

He earned a Ph.D. in applied mathematics and A.M. in statistics in 2020, both from Harvard University. By 2014, Pestourie attained four master’s degrees in his native France.

These include degrees in physics from École Centrale Paris, both business and management from École Supérieure des Sciences Economiques et Commerciales (ESSEC), and nanoscience from Université Paris-Saclay.

Pestourie’s research specialization is scientific ML and inverse design in electromagnetism.

“The goal of my group is to create accurate models that enable previously unreachable engineering solutions via optimization. We will create scientific artificial intelligence (AI) models that efficiently combine information from data and scientific knowledge toward simulations that drive engineering discovery,” said Pestourie.

“With this research agenda, I could not find a better home than CSE—the academic discipline devoted to systematic computer models to solve real-world problems.”

Kai Wang recently earned his Ph.D. in computer science at Harvard University, as a Siebel Scholar. His expertise lies in ML and optimization, focusing on data-driven decision-making and AI for social impact.

Wang’s work is currently making an impact is in India. ARMMAN, a non-profit organization, is using his algorithms to connect pregnant women and mothers and their infants with health providers. This collaboration assists the organization in its mission to improve access to maternal healthcare in the country.

Wang also worked with the World Wildlife Fund and Citizen of the Earth, Taiwan, toward environmental and wildlife conservation. Through this collaboration, Wang applied his ML research and multi-agent systems in satellite imaging to detect illegal factory expansion and animal poaching.

“Applying AI to create social impact is one of the greatest responsibilities and opportunities in our generation,” Wang said. “I am excited to work with talented students, researchers, and practitioners at Georgia Tech CSE to build reliable and scalable AI, conquer societal challenges, and make a better future together.”

Helen Xu comes to Georgia Tech from Lawrence Berkely National Laboratory (LBNL) where she was the 2022 Grace Hopper Postdoctoral Scholar. She completed her Ph.D. in computer science at MIT the same year.

Along with her time at LBNL, Xu has worked with NVIDIA, Microsoft Research, and Sandia National Laboratories.

Her research examines high-performance computing (HPC), with interests in parallel computing, cache-efficient algorithms, and performance engineering.

“I joined CSE because of its research strengths in many areas of HPC, which I hope will lead to fruitful collaborations,” Xu said. “I was also impressed by the extensive computing resources at Georgia Tech, which will help expand and accelerate my research.”

The dataset compiles meeting minutes, speeches, and press conference transcripts from the Federal Open Market Committee (FOMC). It is the largest tokenized and annotated dataset of its kind.

An investment strategy developed using the dataset predicted investment returns yielding 163.4% higher than the buy and hold method on the QQQ index fund from 2011 to 2022.

The dataset and strategy are part of new research findings from Georgia Tech. The findings document the influence the FOMC has on markets and the economy through its public communications. The research is being presented this month at the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023).

“By understanding the impact of FOMC communications on market movements, investors can make more informed decisions, and potentially protect their portfolios from sudden downturns or capitalize on growth opportunities,” said Ph.D. student and lead researcher Agam Shah.

“Additionally, it can help economists at the Federal Reserve Banks more efficiently understand the impact of their communication.”

[MICROSITE: Georgia Tech at ACL 2023] 

The dataset contains 214 meeting minutes, 1,026 speeches, and transcripts from 63 press conferences. The meeting minutes and speeches span from January 1996 to October 2022. The press conference archive dates from April 2011 to October 2022.

To explore this heap of FOMC pronouncements, Shah and his team crafted a novel machine-learning classification task. The new task categorized statements in the dataset as hawkish, dovish, or neutral, rather than just positive, negative, or neutral.

The classification task allows computer models to understand FOMC policy stances through the language used in their correspondence. This in turn guides models to predict how markets react to communications, giving investors valuable information to form their own strategies. 

“One of the reasons our research achieved these remarkable results is because it harnesses the power of natural language processing (NLP) to systematically analyze a vast amount of data which is impractical for humans to process effectively,” Shah said. “This provides a much more nuanced understanding of the market’s response to FOMC communications.”

Shah is a Ph.D. student in the School of Computational Science and Engineering (CSE). He is advised by Sudheer Chava, a professor in the Scheller College of Business. Suvan Paturi, a Georgia Tech alumnus and software engineer at Nasdaq eVestment, co-authored the paper with Shah and Chava.

The group will present their paper at a time when the FOMC and the Federal Reserve are in news headlines now more than ever. To curb inflation, the Fed has increased interest rates ten consecutive times from March 2022 to June 2023.

One example that inspired the group occurred during this period on Aug. 26, 2022. Here, FOMC Chair Jerome Powell gave an eight-minute speech that resulted in an almost $3 trillion decline in U.S. equity market value.

This study not only affirms that markets are reactive to words spoken through public communications but now those effects can be measured and predicted. It also provides new tools to help investors make better, more informed decisions. 

“The application of computational methods to finance and economics revolutionizes the way analysts interpret data. It enables us to handle enormous datasets and extract valuable insights that would otherwise remain hidden,” Shah said. 

“This empowers decision-makers to craft strategies that are based on a deeper understanding of market dynamics, leading to potentially higher returns and more efficient financial systems.”

Advances in computing techniques are ushering in a new age of understanding the macromolecules that form life as we know it. One Georgia Tech research group is now receiving well-deserved accolades for their applications in data science and machine learning toward single-cell omics research. 

Students studying under Xiuwei Zhang, an assistant professor in the School of Computational Science and Engineering (CSE), received awards in April at the Atlanta Workshop on Single-cell Omics (AWSOM 2023).

School of CSE Ph.D. student Ziqi Zhang received the best oral presentation award, while Mihir Birfna, an undergraduate student majoring in computer science, took the best poster prize.

Along with providing computational tools for biological researchers, the group’s papers presented at AWSOM 2023 could benefit populations as the research could lead to improved disease detection and prevention. They can also provide a better understanding of the causes and treatments of cancer and a new ability to accurately simulate cellular processes.

“I am extremely proud of the entire research group and very thankful for their work and our teamwork within our lab,” said Xiuwei Zhang. “These awards are encouraging because it means we are on the right track of developing something that will contribute both to the biology community and the computational community.”

Ziqi Zhang presented the group’s findings of their deep learning framework called scDisInFact, which can carry out multiple key single-cell RNA-sequencing (scRNA-seq) tasks all at once and outperform current models that focus on the same tasks individually.

The group successfully tested scDisInFact on simulated and real Covid-19 datasets, demonstrating applicability in future studies of other diseases.

Bafna’s poster introduced CLARIFY, a tool that connects biochemical signals occurring within a cell and intercellular communication molecules. Previously, the inter- and intra-cell signaling were often studied separately due to the complexity of each problem.

Oncology is one field that stands to benefit from CLARIFY. CLARIFY helps to understand the interactions between tumor cells and immune cells in cancer microenvironments, which is crucial for enabling the success of cancer immunotherapy.

At AWSOM 2023, the group presented a third paper on scMultiSim. This simulator generates data found in multi-modal single-cell experiments by modeling various biological factors underlying the generated data. It enables quantitative evaluations of a wide range of computational methods in single-cell genomics. That has been a challenging problem due to the lack of ground truth information in biology, Xiuwei Zhang said. 

“We want to answer certain basic questions in biology, like how we get these different cell types like skin cells, bone cells, and blood cells,” she said. “If we understand how things work in normal and healthy cells, and compare that to the data of diseased cells, then we can find the key differences between those two and locate the genes, proteins, and other molecules that cause problems.”

Xiuwei Zhang’s group specializes in machine learning and optimization skills used to analyze single-cell omics data and scRNA-seq methods. Their main interest is studying mechanisms of cellular differentiation— the process when young, immature cells mature and take on functional characteristics.

scRNA-seq is an approach to research in molecular biology that is growing in popularity. It gives insight into the existence and behavior of different types of cells. This helps researchers understand genetic disorders, detect mechanisms that cause tumors and cancer, and develop new treatments, cures, and drugs. 

If microenvironments filled with various macromolecules and genetic material are considered datasets, the need for researchers like Xiuwei Zhang and her group is obvious. These massive, complex datasets present challenges and opportunities for the group experienced in computational and biological research.

Collaborating authors include School of CSE Ph.D. students Hechen Li and Michael Squires, School of Electrical and Computer Engineering Ph.D. student Xinye Zhao, Wallace H. Coulter Department of Biomedical Engineering Associate Professor Peng Qiu, and Xi Chen, an assistant professor at Southern University of Science and Technology in Shenzhen, China.

The group’s presentations at AWSOM 2023 exhibited how their work makes progress in biomedical research, as well as advancing scientific computing methods in data science, machine learning, and simulation.

scDisInFact is an optimization tool that can perform batch effect removal, condition-associated key gene detection, and perturbation, which is made possible by considering major variation factors in the data. Without considering all these factors, current models can only do these tasks individually. But scDisInFact can do each of these tasks better, and all at the same time.

CLARIFY delves into how cells employ genetic material to communicate internally, using gene regulatory networks (GRNs) and externally, called cell-cell interactions (CCIs). Many computational methods can infer GRNs, and inference methods have been proposed for CCIs. But until CLARIFY, a way to infer GRNs and CCIs in the same model did not exist.

scMultiSim simulations perform closer to real-world conditions than current simulators that model only one or two biological factors. This helps researchers to realistically test their computational methods, which can guide directions for future method development.

Whether they be computer scientists, biologists, or non-academics alike, the advantage of interdisciplinary and collaborative research, like Xiuwei Zhang’s group, is its wide-reaching impact that advances technology to improve the human condition.

“We’re exploring the possibilities that can be realized by advanced computational methods combined with cutting-edge biotechnology,” said Xiuwei Zhang. “Since biotechnology keeps evolving very fast and we want to help push this further by developing computational methods, together we will propel science forward.”

“This semester’s graduating class embodies the diverse, interdisciplinary nature of the School of CSE,” said Regents’ Professor and School of CSE Chair Haesun Park. “We are immensely proud of all of this year’s CSE graduates. They are innovators and problem solvers, ready to take on pressing challenges in the scientific, engineering, social, and medical fields.”

Two School of CSE doctoral candidates walked across the stage at Bobby Dodd Stadium to receive their degrees during the Ph.D. Ceremony.

For Tianfan Fu (Ph.D. CS 2023) and Apaar Shanker (Ph.D. CSE 2023), Commencement was a curtain call for years of vigorous graduate-level research, teaching assistantships and internships, and conference presentations. Fu was advised by School of CSE Adjunct Professor Jimeng Sun. Shanker was advised by Regents’ Professor Surya Kalidindi, joint with the George W. Woodruff School of Mechanical Engineering and School of CSE.

At the master’s ceremony, three CSE Ph.D. students received diplomas for completing M.S. degrees while in pursuit of their doctorates.

William Kamerow (M.S. CSE 2023) will continue research in high-performance computing, machine learning, and electric propulsion. He is advised by Professor Mitchell Walker, faculty in the Daniel Guggenheim School of Aerospace Engineering and the College of Engineering’s associate dean for academic affairs.

Shruti Shivakumar (M.S. CSE 2023) is near completion of her Ph.D. in which she researches performant, parallel algorithms for hypergraph analytics. She studies under School of CSE Professor Srinivas Aluru, who is also the executive director of the Institute for Data Engineering and Science (IDEaS).

Ziyi (Francis) Yin (M.S. CSE 2023) will continue studying under Felix Herrmann, a joint appointment professor with the School of Earth and Atmospheric Sciences, School of Electrical and Computer Engineering, and School of CSE. Their group specializes in developing machine learning tools for seismic data acquisition, modeling, imaging, and inversion.

Joining Kamerow, Shivakumar, and Yin are 68 CSE graduates who also completed their M.S. degrees.

Along with students whose home unit is the School of CSE, the School also administers CSE graduate programs for students studying in 12 schools and departments spanning three colleges at Georgia Tech. These affiliations with the School of CSE demonstrate the interdisciplinary and collaborative threads inherently woven into the fabric of a Georgia Tech CSE degree.

College of Computing M.S. graduates included:

Mengzhen Chen (M.S. CSE-CS 2023)

Yuqi Jia (M.S. CSE-CS 2023)

Zihan Xu (M.S. CSE-CS 2023)

Shoale Badr (M.S. CSE-CSE 2023)

Guanming Chen (M.S. CSE-CSE 2023)

Jiayuan Chen (M.S. CSE-CSE 2023)

Yi-Ming Chen (M.S. CSE-CSE 2023)

Jin Guo (M.S. CSE-CSE 2023)