TokenSmith is a citation-supported large language model (LLM) tutor that can be hosted locally on a user’s personal computer. The tutor only provides answers based on course materials, such as the textbook or lecture slides.
Associate Professor Joy Arulraj began the project with support from the Bill Kent Family Foundation AI in Higher Education Faculty Fellowship last year. The fellowship, led by Georgia Tech’s Center for 21st Century Universities, supports faculty projects exploring innovative and ethical uses of AI in teaching.
Arulraj has enlisted assistant professors Kexin Rong and Steve Mussmann to help build TokenSmith.
Mussmann said TokenSmith is a synergistic blend of a database system and a machine learning system. The model stores textbooks, textbook annotations by course staff, common questions and answers, a learning state of the student, and student feedback in a structured database system. However, machine learning plays a key role in the answer generation as well as adapting the system to the student, course staff guidance, and user feedback.
"What excites me most is demonstrating how data-driven ML and principled database systems design can reinforce each other — one providing adaptability and flexibility, the other providing structure and traceability — in a way that benefits students," Mussmann said.
Keeping the model local has been an important focus of the project. The team wanted to create an AI tutor that helps students learn from their class resources rather than just giving answers. With each response, TokenSmith cites the origin of the answer in the provided documents.
“One problem with LLMs is that they can hallucinate and provide wrong answers, but in this controlled environment, we can add these guardrails to make sure it’s actually helpful in an educational setting,” Rong said.
Rong said she feels that students often undervalue textbooks, and she hopes TokenSmith can motivate students to make better use of them.
“Textbooks can sometimes be daunting, but maybe if we combine them with the model, students might be more willing to read a paragraph or page in the textbook, and that could help clarify something for them,” she said.
Running the model locally is more cost-effective and helps preserve the user’s privacy. But running the new tool locally comes with technical challenges.
One challenge with creating the model is speed. Since it is a locally based model, TokenSmith depends solely on the user’s computer memory. Tests have also shown that the tutor currently struggles to answer more complex questions.
“We are interested in pushing the boundaries of these local models so that they give students good answers and also run fast enough to keep students engaged,” Arulraj said.
]]>Associate Professor Joy Arulraj began the project with support from the Bill Kent Family Foundation AI in Higher Education Faculty Fellowship last year. The fellowship, led by Georgia Tech’s Center for 21st Century Universities, supports faculty projects exploring innovative and ethical uses of AI in teaching.
]]>With imitation learning, humans demonstrate a task and robots learn to copy what they see through cameras and sensors. While at the leading edge of robotics research, this approach is limited by a major constraint: Robots can only work as fast as the people who taught them.
Now, Georgia Tech researchers have created a tool that smashes that speed barrier. The system allows robots to execute complex tasks significantly faster than human demonstrations while maintaining precision, control, and safety.
The team addresses a central challenge in modern robotics: how to combine the flexibility of learning from humans with the speed and reliability required for real-world deployment. The technology could lead to wider adoption of imitation learning in industrial and household applications and even enable robots to execute humanlike tasks better than ever before.
“The thing we’re trying to create — and I would argue industry is also trying to create — is a general-purpose robot that can do any task that human hands can do,” said Shreyas Kousik, assistant professor in the George W. Woodruff School of Mechanical Engineering and a co-lead author on the study. “To make that work outside the lab, speed really matters.”
The new tool, SAIL (Speed Adaptation for Imitation Learning), was born out of a cross-campus, interdisciplinary collaboration that brought together expertise in mechanical engineering, robotics systems, and machine learning. The research team includes Kousik; Benjamin Joffe, senior research scientist at the Georgia Tech Research Institute; and Danfei Xu, assistant professor in the School of Interactive Computing, along with graduate students and researchers from multiple labs.
Teaching robots to work faster than the speed of human demonstrations is challenging. Robots can behave differently at higher speeds, and small changes in the environment can cause errors.
“The challenge is that a robot is limited to the data it was trained on, and any changes in the environment can cause it to fail,” Kousik said.
SAIL addresses this challenge through a modular approach, with separate components working together to accelerate beyond the training data. The system keeps motions smooth at high speed, tracks movements accurately, adjusts speed dynamically based on task complexity, and schedules actions to account for hardware delays. This combination allows robots to move quickly while staying stable, coordinated, and precise.
“One of the gaps we saw was that our academic robotics systems could do impressive things, but they weren’t fast or robust enough for practical use,” Joffe said. “We wanted to study that gap carefully and design a system that addressed it end to end.”
He added, “The goal is not just to make robots faster, but to make them smart enough to know when speed helps and when it could cause mistakes.”
The team evaluated SAIL’s performance across 12 tasks, both in simulation and on two physical robot platforms. Tasks included stacking cups, folding cloth, plating fruit, packing food items, and wiping a whiteboard. In most cases, SAIL-enabled robots completed tasks three to four times faster than standard imitation-learning systems without losing accuracy.
One exception was the whiteboard-wiping task, where maintaining contact made high-speed execution difficult.
“Understanding where speed helps and where it hurts is critical,” Kousik said. “Sometimes slowing down is the right decision.”
While SAIL does not make robots universally adaptable on its own, it represents an important step toward robotic systems that can learn from humans without being constrained by human pace.
By showing how learned robotic behaviors can be accelerated safely and systematically, SAIL brings imitation learning closer to real-world use — where speed, precision, and reliability all matter.
Citation: Ranawaka Arachchige, et. al. “SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies,” Conference on Robot Learning (CoRL), 2025.
DOI: https://doi.org/10.48550/arXiv.2506.11948
Funding: The authors would like to acknowledge the State of Georgia and the Agricultural Technology Research Program at Georgia Tech for supporting the work described in this paper.
]]>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.
“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:
“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 Alex Helbling, Seongmin Lee, Ben Hoover, and alumnus Zijie (Jay) Wang 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.”
]]>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.
]]>ISyE researchers are applying AI to complex manufacturing environments, including multistage production systems, asset management, quality improvement, and human‑centered manufacturing. Faculty leaders emphasize the importance of contextualizing large volumes of manufacturing data so AI can support reliable decision‑making, efficient operations, and sustainable outcomes. At the same time, the initiative acknowledges challenges such as data integration, system complexity, and the need to balance automation with human involvement. Together, these efforts position ISyE at the forefront of shaping AI‑powered manufacturing systems that are innovative, resilient, and socially responsible.
Read the full article in ISyE Magazine
]]>This year’s festival features the world premiere of Water Memory (Jala Smirti), a chamber opera exploring how artificial intelligence can support individuals living with dementia and provide assistance for aging parents. The festival also includes the 96-Hour Opera Project, a signature competition showcasing newly created opera scenes by emerging artists.
Events will take place across multiple Atlanta venues, including the Ferst Center for the Arts at Georgia Tech and the Ray Charles Performing Arts Center at Morehouse College. Performances include the premiere of Water Memory on June 12, the competition showcase on June 13, and an encore performance on June 14.
Tickets start at $35, with festival passes available for $50. The NOW Festival continues to foster innovation in opera while mentoring the next generation of artists.
]]>That’s one of the main findings from a study by AI Caring on what older adults expect from explainable AI (XAI).
AI Caring is one of three AI Institutions led by Georgia Tech and funded by the National Science Foundation (NSF). The institution supports AI research that benefits older adults and their caregivers.
Niharika Mathur, a Ph.D. candidate in the School of Interactive Computing, was the lead author of a paper based on the study. The paper will be presented in April at the 2026 ACM Conference on Human Factors in Computing Systems (CHI) in Barcelona.
Mathur worked with the Cognitive Empowerment Program at Emory University to interview 23 older adults who live alone and use voice-activated AI assistants like Amazon’s Alexa and Google Home.
Many of them told her they feel excluded from the design of these products.
“The assumption is that all people want interactions the same way and across all kinds of situations, but that isn’t true,” Mathur said. “How older people use AI and what they want from it are different from what younger people prefer.”
One example she gave is that young people tend to be informal when talking with AI. Older people, on the other hand, talk to the agent like they would a person.
“If Older adults are talking to their family members about Alexa, they usually refer to Alexa as ‘she’ instead of ‘it,’” Mathur said. “They tend to humanize these systems a lot more than young people.”
The study evaluated AI explanations that drew information from four sources of data:
Mathur said older users trust the agent more when it bases its explanations on data from the first three sources. However, internal reasoning creates skepticism.
Internal reasoning means the AI doesn’t have enough data from the other sources to give an explanation. It provides a percentage to reflect its confidence based on what it knows.
“The overwhelming response was negative toward confidence scores,” Mathur said. “If the AI says it’s 92% confident, older adults want to know what that’s based on.”
This is another example that Mathur said points to generational preferences.
“There’s a lot of explainable AI research that shows younger people like to see numbers in explanations, and they also tend to rely too much on explanations that contain numerical confidence. Older adults are the opposite. It makes them trust it less.”
Mathur said that AI agents interacting with older adults should serve a dual purpose. They should provide users with companionship and support independence while reducing the caretaking burden often placed on family members.
Some studies have shown that engineers have tended to favor caretakers in the design of these tools. They prioritize daily tasks and routines, leaving some older adults to feel like they are merely a box to be checked.
She discovered that in urgent situations, older users prefer the AI to be straightforward, while in casual settings, they desire more conversation.
“How people interact with technological systems is grounded in what the stakes of the situation are,” she said. “If it had anything to do with their immediate sense of safety, they did not want conversational elaboration. They want the AI to be very direct and factual.”
Mathur said AI agents that interact with older adults are ideally constructed with a dual purpose. They should provide companionship and autonomy for the users while alleviating the burden of caretaking that is often placed on their family members.
Some studies have shown that engineers have strayed toward favoring caretakers in the design of these tools. They prioritize daily tasks and routines, leaving some older adults to feel like they are a box to be checked.
“They’re not being thought of as consumers,” Mathur said. “A lot of products are being made for them but not with them.”
She also said psychological well-being is one of the most important outcomes these tools should produce.
Showing older adults that they are listened to can significantly help in gaining their trust. Some interviewees told Mathur they want agents who are deliberate about understanding their preferences and don’t dismiss their questions.
Meeting these needs reduces the likelihood of protesting and creating conflict with family members.
“It highlights just how important well-designed explanations are,” she said. “We must go beyond a transparency checklist.”
]]>Thinking about polymers in that way has led Georgia Tech materials scientists to create new generative artificial intelligence tools that are like Claude or ChatGPT for new materials.
These are the first foundational models for generative polymer design that have also been validated through physical experiments: users specify the properties they need in a polymer and the model will suggest a chemical structure.
Led by Regents’ Entrepreneur Rampi Ramprasad, the researchers described their latest model this month in the Nature journal npj Artificial Intelligence — including a test material they created and validated in the lab to prove the models work.
Read the full story on the College of Engineering website.
]]>Set in the heart of Tech Square on the Georgia Tech campus, this year’s event explores how energy systems, materials, technologies, supply chains, and policy must evolve in response to AI’s accelerating impact. As digital infrastructure expands and computation intensifies, the need for reliable, resilient, and sustainable power has never been more urgent.
“Energy Day reflects Georgia Tech’s strength in connecting world-class research in materials and components with the infrastructure and partnerships needed to translate discovery into scalable energy technologies that serve industry, society, and the future economy,” said Eric Vogel, executive director of the IMS and the Hightower Professor in Materials Science and Engineering.
Energy Day 2026 also marks an important milestone with the introduction of its first group of corporate sponsors: GE Vernova, Southern Company, Georgia Power, ExxonMobil, Southwire Spark, Gems Setra, and Tektronix. Their support reflects a shared commitment to advancing energy solutions.
“Tektronix is excited to be part of Energy Day because advancing the future of energy starts with precise measurement and trusted insights,” said Christopher Bohn, president of Tektronix. “From power electronics and high voltage systems to grid scale renewables and AI driven control technologies, the breakthroughs discussed here directly align with the innovations we support through our products and solutions. Collaborating with Georgia Tech allows us to engage early with emerging research and the next generation of engineers—critical collaborators in building a cleaner, smarter, and more resilient energy ecosystem.”
The keynote address will be delivered by Vanessa Z. Chan, a nationally recognized leader at the intersection of innovation, commercialization, and emerging technologies. Chan will provide insights on accelerating technological discovery, emphasizing how AI is transforming energy and materials design. She will discuss how commercialization strategies must rapidly evolve across multidisciplinary energy domains from grid modernization to advanced batteries and clean manufacturing.
Building on the themes introduced in the keynote, the program transitions into a fireside chat with Georgia Tech EVPR Tim Lieuwen featuring Amit Kulkarni and Jim Walsh. Kulkarni is vice president of Product Management and Strategy for the Gas Power business within GE Vernova, where he oversees the world’s largest portfolio of power generation equipment. Walsh, vice president of GE Vernova’s Consulting Services, leads teams providing innovative solutions across the full spectrum of power generation, delivery, and utilization.
Next comes a policy-focused panel that will explore the surge in power demand driven by AI, how the United States is addressing today’s most urgent energy challenges, and the long-term implications of today’s decisions for a sustainable energy future. Bringing together leading voices in U.S. environmental and energy policy, the panel features Joe Aldy of Harvard University and former special assistant to the president for Energy and Environment; Al McGartland of New York University’s Institute for Policy Integrity and former Environmental Protection Agency lead economist and director of the National Center for Environmental Economics; and Kevin Rennert, fellow and director of the Comprehensive Climate Strategies Program at Resources for the Future and former staff member on the U.S. Senate Committee on Energy and Natural Resources.
The second panel focuses on critical materials — the foundation of advanced energy systems and digital technologies. As AI, data centers, and advanced energy technologies drive demand for critical materials, securing them now requires integration and coordination across the entire value chain. Panelists include Rachel Galloway, British consul general in Atlanta; Vijay Murugesan, head of Materials Intelligence and Digital Innovation at Amazon; Colin Spellmeyer, executive strategic sourcing leader at GE Vernova; Charles Sims, Tennessee Valley Authority Distinguished Professor of Energy and Environmental Policy at the University of Tennessee; and Nortey Yeboah, principal engineer at Southern Company. Together, they will offer perspectives on the policy and economic frameworks shaping the energy supply chain, from developing raw resources to manufacturing the technologies essential to future energy systems.
In the afternoon, participants can dive deeper into specialized topics through three focused technical tracks.
“Meeting the rapidly rising electricity demand driven by AI requires bold ideas, coordinated action, and research that moves at the speed of innovation,” said Yuanzhi Tang, executive director of the SEI. “Energy Day 2026 brings together the people and expertise needed to shape resilient, sustainable energy systems for the future. At Georgia Tech, we see this event as a catalyst for new partnerships, new solutions, and a shared commitment to strengthening the nation’s energy foundation.”
Energy Day 2026 is designed for researchers advancing emerging energy technologies, policymakers navigating shifting regulatory and geopolitical landscapes, industry professionals seeking insight into emerging tools and supply chains, and students preparing to enter one of the most consequential sectors of the decade. It also welcomes anyone interested in AI, sustainability, electrification, and critical materials.
Join us to explore the future of energy. To learn more and register, visit: Energy Day 2026.
]]>Now in its second year of supporting the College of Computing, the Squarepoint Foundation continues to expand opportunities, enabling students to focus fully on their studies and pursue activities outside the classroom.
A selection committee led by Mary Hudachek-Buswell, interim chair of the School of Computing Instruction (SCI), chose this year’s cohort.
“These students exemplify the curiosity, talent, and determination we strive to cultivate in computer science,” Hudachek-Buswell said. “The Squarepoint Foundation Scholarships will give them the opportunity to focus fully on their studies while pursuing research and projects that have the potential to make a real-world impact.”
The scholars have demonstrated strong leadership across campus, with all five serving as teaching assistants (TAs) and earning faculty honors. The cohort is also engaged in research and study abroad opportunities.
Founded in 2021, the Squarepoint Foundation supports STEM education and research while partnering with organizations worldwide to expand opportunity and access.
“We are proud to continue our partnership with Georgia Tech, as we extend our support to a number of students working towards achieving their academic goals,” said Allison Henry, Squarepoint Foundation manager.
“The Squarepoint Foundation aims to increase access to education, ensuring that all individuals have the opportunity to pursue the degree of their choice, no matter their circumstances. We wish these talented students the best of luck as they undertake their studies and recognize them for their hard work and dedication to the STEM field."
Meet the Scholars
Maria Cymbalyuk
Cymbalyuk studies Cybersecurity and Information Internetwork threads, focusing on how technical systems shape who is protected or exposed in digital environments. She’s interested in supporting public defenders and improving access to justice through technology.
“This scholarship made this semester feel less financially stressful and more like I can focus on building the skills and experiences I care about,” Cymbalyuk said. “I want to use my skills to build tools and do research that supports public interest organizations.”
Marziah Islam
Islam concentrates on the People and Intelligence threads, exploring how humans interact with technology. She is developing a sign-language learning mobile app through a Vertically Integrated Project and hopes to build accessible, reliable systems in healthcare technology.
“I am fascinated by the intersection of humans and computing, and I want to design technology that better supports real people,” Islam said.
Sahadev Bharath
Bharath studies Architecture and Information Internetworks threads, with interests in low-level programming, operating systems, and large-scale systems. He plans to begin his career in software engineering, focusing on distributed systems and AI infrastructure.
“Coming from India, being able to afford out-of-state tuition has been a challenge. This scholarship relieves financial stress and gives me more time to focus on my academics and career,” Bharath said.
“I am passionate about teaching and sharing my knowledge with fellow students. Being a TA has been extremely fulfilling and motivates me to continue contributing to education.”
Joie Yeung
Yeung studies Information Internetworks and Intelligence threads, with a focus on data and artificial intelligence. She has received the President’s Volunteer Service Award for completing more than 100 service hours in one year. In addition to pursuing a career in software engineering, she is passionate about mentoring younger girls and addressing the gender gap in STEM.
“I want to create meaningful and impactful technology while giving back to my communities. I also aim to show younger girls that they can succeed in computing despite the gender gap,” Yeung said.
Jun Hong Wang
Wang studies system architecture and intelligence with a minor in mathematics, concentrating on computer architecture and low-level optimization. He is considering careers in software engineering, research, or entrepreneurship at the intersection of hardware and software.
“I’m especially interested in how hardware and software intersect, and I hope to use my work to create solutions that are meaningful and helpful for the world,” Wang said.
The scholarships offer vital support as these students keep advancing research, leadership, and influence in computing.
The partnership has driven progress in areas ranging from hypersonics to bioscience, while also deepening institutional ties beyond research. Joint faculty appointments — such as Anirban Mazumdar, who holds roles at both Sandia and the George W. Woodruff School of Mechanical Engineering — demonstrate how closely the organizations work together. The collaboration has also expanded student talent pipelines, providing more avenues for Georgia Tech students to pursue careers at the national lab.
“At its core, this partnership is about people,” said Tim Lieuwen, executive vice president for Research at Georgia Tech. “Sandia and Georgia Tech share a commitment to discovery and developing the talent, creativity, and collaboration our nation needs.”
The renewed MOU, he said, “strengthens connections between our researchers, opens new doors for our students, and builds meaningful career pathways into national service. When our communities work together to address national priorities, we not only accelerate technological advances — we expand opportunities for the people who will shape the future of our nation’s security.”
Under the new MOU, Sandia and Georgia Tech will focus on integrated research across key national security‑aligned areas, including secure artificial intelligence and computing, quantum technologies, critical minerals, advanced manufacturing, energy and grid resilience, and hypersonics. The partnership emphasizes connecting manufacturing, computation, and systems approaches directly to national security applications.
“Together, we have been solving new and unprecedented challenges in science and engineering, and now we have a great opportunity to develop this partnership,” said Dan Sinars, Sandia’s deputy chief research officer. “Our research benefits both national security and national prosperity, and keeps the country at the forefront of the world.”
With this strengthened connection, the partners aim to grow their shared research footprint through increased funding, publications, and faculty-led startups. Over the long term, Georgia Tech intends to become one of Sandia’s top hiring pipelines, ensuring that talent developed through joint research continues into national security careers.
History of the Partnership
The Institute’s collaboration with Sandia began in the mid‑2010s, when the labs selected Georgia Tech as one of its partner institutions. The first MOU, signed in 2015, formalized the relationship and outlined initial technical focus areas.
In 2018, George White, executive director of strategic partnerships, and Olof Westerstahl, senior director strategic initiatives in the Office of Corporate Engagement, helped expand the partnership. They launched “Sandia Day,” an event designed to introduce Georgia Tech faculty to Sandia researchers and spark new collaborations. By 2020, the organizations signed a second MOU that expanded the partnership’s technical focus areas to include energy and grid security, materials and nanotechnology, advanced electronics, advanced manufacturing, advanced computing, cyber and information security, bioscience, hypersonics, quantum information science, and engineering sciences.
The results have been substantial. Since 2018, Sandia has sponsored $35 million in research collaborations with Georgia Tech. Researchers from both institutions have co-authored 450 publications since 2016. Research activity continues to accelerate, with $1.6 million in new contracts in the past year alone. As of August 2025, Sandia employs 325 Georgia Tech alumni — a testament to the impact of the growing talent pipeline.
“We view our work with Sandia as the model for engagement with other national labs,” said White. “With the new MOU, we will continue to grow the Sandia partnership. I would like to see our footprint double in scope in the next five years.”
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tess.malone@gatech.edu
]]>The Department of Energy Office of Science recently released two reports through its Advanced Scientific Computing Research (ASCR) program. The reports were produced by workshops that brought together researchers from universities, national labs, government, and industry to set priorities for scientific computing.
Professor Felix Herrmann served on the organizing committee for the Workshop on Inverse Methods for Complex Systems under Uncertainty. Assistant Professor Peng Chen joined Herrmann as a workshop participant, contributing expertise in data science and machine learning.
Inverse methods work backward from outcomes to find their causes. Scientists use these tools to study complex systems, like designing new materials with targeted properties and using past wildfires to map vulnerable areas and behavior of future fires.
The ASCR report highlighted Herrmann’s work on seismic exploration and monitoring through digital twins. Founded on inverse methods, digital twins upgrade from static models to virtual systems that accurately mirror their physical counterparts.
Digital twins integrate real-time data sources, including fluid flows, monitoring and control systems, risk assessments, and human decisions. These models also account for uncertainty and address data gaps or limitations.
The DOE organized the workshop to support the growing role of inverse modeling. The group identified four priority research directions (PRDs) to guide future work. The PRDs are:
“A digital twin is a system you can control, like to optimize operations or to minimize risk,” said Herrmann, who holds joint appointments in the Schools of Earth and Atmospheric Sciences, Electrical and Computer Engineering, and Computational Science and Engineering.
“Digital twins give you a principled way to consider uncertainties, which there are a lot in subsurface monitoring. If you inject carbon dioxide too fast, you will will increase the pressure and may fracture the rock. If you inject too slow, then the process may become too costly. Digital twins help us make balanced decisions under uncertainty.”
Supercomputers, algorithms, and artificial intelligence now power modern science. However, these tools consume enormous amounts of energy. This raises concerns about how to sustain computing and scientific research as we know them in the decades ahead.
Professors Rich Vuduc and Hyesoon Kim co-authored the report from the Workshop on Energy-Efficient Computing for Science. At the three-day ASCR workshop, participants identified five key research directions:
“I’m cautiously optimistic about the future of energy-efficient computing. The ASCR report says, from a technological point of view, there are things we can do,” said Vuduc.
“The report lays out paths for how we might design better apps, hardware systems, and algorithms that will use less energy. This is recognition that we should think about how architectures and software work together to drive down energy usage for systems.”
]]>The Department of Energy Office of Science recently released two reports through its Advanced Scientific Computing Research (ASCR) program. The reports were produced by workshops that brought together researchers from universities, national labs, government, and industry to set priorities for scientific computing.
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