Reasons to go:

• For this year (2027), we will be offering a $1000 subsidy for program participants!
• Global tech impact: See where the next billion users are coming online.
• Career growth: Employers love global tech experience.
• Community: Learn and travel with fellow GT CS students.
• Adventure: From hackathons to heritage sites—discover India.

Structure of the program:

• Two online courses for the first four weeks, then the last two weeks onsite in Mumbai, India
• Two weeks in Mumbai will be planned for you with class time and field trips, as well as cultural outings while interacting with students at Mumbai’s Atlas Skilltech University 
• First four weeks, take the online courses anywhere; for the last two weeks, participation onsite in Mumbai and lodging at Atlas Skilltech's accommodation are required

Email indiaprogram@cc.gatech.edu for questions. Application deadline: February 1st in Atlas.

Ask questions about the CoC's newest program, "Technology Design and Ethics in India," a 6-week program (May 17 - June 25, 2027), with 4 weeks of online instruction followed by 2 weeks of continued GT instruction in Mumbai, India. Drop in for a few minutes to ask your question or stay the whole hour and hear what other students are asking. 

Program Courses:

The 2 foundational Computer Science courses of the program will be taught by Georgia Tech CS faculty and Program Director, Naveena Karusala.

• CS 4001 – Computing & Society (Ethics Credit) - Students will critically examine how issues around technology, such as globalization, bias, privacy, and others, affect people worldwide.

• CS 3751 – Intro to User Interface Design - Students will explore culturally situated approaches to design and, through the course project, create an inventive design artifact.

No questions and ready to go? Apply HERE. Email contact: indiaprogram@cc.gatech.edu. To learn more about available virtual meeting opportunities, visit cc.gatech.edu/india.

Mathematics is being transformed by the combination of AI, formal methods, and computer algebra (AI+FM+CA). The Center aims to catalyze and reorient this work at Georgia Tech and tighten its connections to mathematics by securing public and private funding, arranging compute resources, supporting team building, and organizing seminars, workshops, and tutorials. We also hope to openly discuss the AI-driven transformation of mathematical research, including concerns about the diverging value systems of academia and the companies working on AI for math.

The town hall is held as part of the AI for the Working Mathematician lunch discussion group, which meets on select Thursdays, 11:30am to 12:30pm in Skiles 006.

Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group builds chatbots and language analysis tools for psychological and medical research.  They study the drivers of physical and mental well-being and their cultural variation, and develop socially sensitive AI-based tutors and coaches.

For CODA guest access, please contact shatcher8@gatech.eduat least 2 business days prior to the event.

Zoom link: https://gatech.zoom.us/j/91607375625?pwd=ybCsFPzknA8VqYZaSqvenqlWbrvyzx.1

Meeting ID: 916 0737 5625
Passcode: 038828

On the experimental side, testing theories at the world's largest experiments, such as the Large Hadron Collider, is yet another computational challenge. High-dimensional data arrive at 40 MHz (~40 TB/s), and decisions must be made rapidly. I will discuss recent advancements in robust neural inference techniques with reliable uncertainty quantification that let us exploit the full high-dimensional data in hypothesis tests. Additional machine learning methods make these genAI predictions trustworthy, enabling tests that outperform likelihood-ratio-based statistical tests.

Bio: Aishik Ghosh is an assistant professor in the School of Physics at Georgia Tech with a focus on developing AI methods to accelerate fundamental physics and astrophysics. His group works on theoretical physics, statistical methods, and experiment design. For robust scientific applications, Dr. Ghosh focuses on uncertainty quantification, interpretability, and verifiability of AI algorithms, targeting publications in physics journals and ML conferences. He has also published policy documents with the Organization for Economic Co-operation and Development and took a leading role in a book on AI and the Future of Science. He received his PhD in particle physics from Paris-Saclay University for introducing digital twins and neural inference techniques to a Large Hadron Collider experiment and was a postdoc at UC Irvine and Lawrence Berkeley National Laboratory. Website: https://profaishikghosh.github.io/

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Join Via Zoom: https://gatech.zoom.us/j/97583133407?pwd=arlGKeB48JGRzqOQt3mPELCH1sfL5J.1 

Meeting ID: 975 8313 3407 

Passcode: 799040

Dalney Building – Room 180
926 Dalney Street NW
Atlanta, GA 30318
West Midtown

Abstract: For decades, we have envisioned robots that can move, adapt, and collaborate with humans as fluidly as we do with each other. Yet even the most advanced systems struggle to achieve what I call fluid physical interaction: a regime where motion, force, and intent predictions are continuously co-regulated through constraint-aware estimation, learning, and control; enabling safe, adaptive, and seamless exchanges of energy and information. Achieving this requires robots that are stable yet flexible, adaptive yet predictable, and compliant yet purposeful. In this talk, I will show how a dynamical-systems perspective provides a unifying foundation for realizing fluid physical interaction. By shaping robot behavior through stable vector fields, viability manifolds, and adaptive dynamics models, we can generate motion policies that remain reactive, safe, and consistent even under uncertainty. I will present recent results that demonstrate how this framework enables robots to generalize complex behaviors from minimal demonstrations, adapt online to changes in their dynamics and environment, avoid obstacles in real time, and maintain long-horizon safety during physical collaboration. I will also discuss new efforts that combine dynamical-systems principles with visuomotor learning to achieve stable, compliant, and force-aware behavior from simulation-guided data. Together, these developments move us closer to embodied, adaptive, and compliant robot intelligence that can truly flow with humans and the environments they inhabit.
 

Bio: Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, with secondary appointments in Computer and Information Science and Electrical and Systems Engineering. She is a core faculty member of the GRASP Lab. Her research integrates learning, control, and estimation to enable safe, adaptive, and fluid human–robot interaction and collaboration. Her work bridges control theory, machine learning, perception, and biomechanics, with an emphasis on formal guarantees for robot behavior in dynamic, human-centered environments. Before joining Penn, she was a Postdoctoral Associate in MIT’s CSAIL. She earned her Ph.D. in Robotics, Control, and Intelligent Systems from EPFL and conducted research at the German Aerospace Center (DLR) and NYU Abu Dhabi. She holds a B.Sc. in Mechatronics Engineering from Tecnológico de Monterrey and an M.Sc. in Automation and Robotics from TU Dortmund. She received a 2025 NSF CAREER Award and was a finalist for the 2020 Georges Giralt Ph.D. Award and the 2017 KUKA Innovation Award. Her publications have received multiple best paper honors at ICRA, CASE, RSS, and IEEE Transactions on Robotics. She chairs the IEEE RAS Women in Engineering Committee, leading global efforts to recognize and celebrate women in robotics. 

Title: Optimization in Scientific ML - is Accuracy Certification a Viable Goal?

Zoom Link: https://gatech.zoom.us/j/9923742334?pwd=aVdnMjZHUDFwWE44UkdNVmNhVVhwdz09

Abstract: Generating nonlinear reduced models for parametric families of PDEs or learning associated parameter-to-solution maps often leads to minimizing a convex energy in a Hilbert space over a nonlinear hypothesis class. Hence, optimization over the budget of trainable weights, defining the hypothesis class, becomes (highly) non-convex. In this talk we discuss adaptive strategies for enhancing robustness and accuracy of optimization success, revolving around "conditional convergence guarantees". The main conceptual constituents are natural gradient flow in combination with an adaptive network expansion. Adaptation aims at increasing alignment with an ideal descent path determined by the gradient in the ambient (infinite dimensional) Hilbert space. This can be further combined with perturbed proximal point methods that can be understood as approximate implicit Euler schemes for the ideal Hilbert space gradient flow. The rationale is that each proximal point step is (much) easier to realize than the global optimization task because in each step the initial guess gets the closer to the optimum, the smaller the step-size. This adaptation to an implicit ideal Hilbert space gradient directions is of interest because the dynamical systems arising in the context of natural gradient flows are generically extremely stiff or even DAEs. Exploiting stable variational formulations of the PDE system, allows one to derive in certain relevant cases a posteriori criteria to check whether an approximate proximal step is sufficiently accurate to guarantee convergence. Roughly, network expansions are triggered when these criteria fail to be met even when the step-size reaches some lower bound. In this sense the results can be understood as "conditional convergence" with accuracy certificates. Part of the material draws on the recent paper [1].

[1]  Wolfgang Dahmen, Wuchen Li, Yuankai Tengang, Zhu Wang, Expansive Natural Neural Gradient Flows for Energy Minimization, Journal of Computational Physics, 563 (2026), 115117

Bio: Wolfgang Dahmen is a Research Professor in the Department of Mathematics at the University of South Carolina, where he previously held the SmartState Chair and Williams-Hedberg-Hedberg Chair. He earned his Ph.D. from RWTH Aachen and his Habilitation from the University of Bonn and has held faculty appointments at the University of Bielefeld, the Free University of Berlin, and RWTH Aachen. His research spans applied and numerical analysis, approximation theory, model order reduction, high-dimensional problems, and scientific machine learning. Dr. Dahmen is a recipient of the Gottfried Wilhelm Leibniz Prize, the Robert Piloty Prize, and the Keck Futures Initiative Award of the US National Academies. He is an elected Fellow of both SIAM and the AMS, a member of the German National Academy of Sciences Leopoldina and the North Rhine-Westphalian Academy of Sciences, and former Chair of the Foundations of Computational Mathematics (FoCM).

Title: Bridging Particle-scale Physics to Continuum Closure for Particle-laden Flows

Ask questions about the CoC's newest program, "Technology Design and Ethics in India," a 6-week program (May 17 - June 25, 2027), with 4 weeks of online instruction followed by 2 weeks of continued GT instruction in Mumbai, India. Drop in for a few minutes to ask your question or stay the whole hour and hear what other students are asking. 

Program Courses:

The 2 foundational Computer Science courses of the program will be taught by Georgia Tech CS faculty and Program Director, Naveena Karusala.

• CS 4001 – Computing & Society (Ethics Credit) - Students will critically examine how issues around technology, such as globalization, bias, privacy, and others, affect people worldwide.

• CS 3751 – Intro to User Interface Design - Students will explore culturally situated approaches to design and, through the course project, create an inventive design artifact.

No questions and ready to go? Apply HERE. Email contact: indiaprogram@cc.gatech.edu. To learn more about available virtual meeting opportunities, visit cc.gatech.edu/india.

Bio: Saiprasad Ravishankar is currently an Associate Professor in the Departments of Computational Mathematics, Science and Engineering and Biomedical Engineering at Michigan State University (MSU). He directs the signals, learning, and imaging (SLIM) research group and the data-driven neuroscience of meditation (DANSOM) lab at MSU. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, India, in 2008, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2010 and 2014, respectively, from the University of Illinois at Urbana-Champaign, where he was then an Adjunct Lecturer and a Postdoctoral Research Associate. From August 2015 to 2018, he was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019, before joining MSU. His research interests include machine learning, computational and biomedical imaging, signal processing, image processing, inverse problems, data science, neuroscience, and physics and astrophysics applications. His research has been recognized with numerous awards including an NSF CAREER Award, IEEE Signal Processing Society Young Author Best Paper Award, and best student paper awards or finalist at numerous conferences such as the IEEE International Symposium on Biomedical Imaging (ISBI) 2018, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017, ISBI 2020, and Optical Imaging Congress 2023.

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

While we prefer you join in person, if you are unable to attend, join online at:
https://gatech.zoom.us/j/98109284552?pwd=CrXDIye86vmEtCywAV7wmjjz1LqEY8.1
Meeting ID: 981 0928 4552
Passcode: 947902

Bio:  Saiprasad Ravishankar is currently an Associate Professor in the Departments of Computational Mathematics, Science and Engineering and Biomedical Engineering at Michigan State University (MSU). He directs the signals, learning, and imaging (SLIM) research group and the data-driven neuroscience of meditation (DANSOM) lab at MSU. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, India, in 2008, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2010 and 2014, respectively, from the University of Illinois at Urbana-Champaign, where he was then an Adjunct Lecturer and a Postdoctoral Research Associate. From August 2015 to 2018, he was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019, before joining MSU. His research interests include machine learning, computational and biomedical imaging, signal processing, image processing, inverse problems, data science, neuroscience, and physics and astrophysics applications. His research has been recognized with numerous awards including an NSF CAREER Award, IEEE Signal Processing Society Young Author Best Paper Award, and best student paper awards or finalist at numerous conferences such as the IEEE International Symposium on Biomedical Imaging (ISBI) 2018, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017, ISBI 2020, and Optical Imaging Congress 2023.

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

While we prefer you join in person, if you are unable to attend, join online at
https://gatech.zoom.us/j/93328955144?pwd=qepbKpZ2pV9Jl0ocrT6JGbHCX169qw.1
Meeting ID: 933 2895 5144
Passcode: 331085

Title: Applying Scientific Computing Perspectives to Neural Network Training

Abstract: Machine learning techniques and their AI decedents are increasingly important part of modern science and engineering. They have proven to be powerful in areas such as surrogate model development, hypothesis generation, and data analysis. Often, these approaches rely on a parameterized function approximation known as the neural network. These approximators are renowned for their flexibility and relative ease of use (if not accuracy). With that ease of use comes a large computational expense. For instance, recent work has demonstrated that pre-training a LLM on the Frontier supercomputer would require two years with ideal parallelization [1]. This talk seeks to examine the neural network with a scientific computing lens to enhance our ability to understand details of the approximation and improve training. Underpinning these ideas is an aspirational hypothesis that insights will lead to multilevel (or hierarchical) methodologies to accelerate training using dramatically fewer resources. 

This talk proceeds in two parts. In the first part, we discuss an adaptive basis perspective that has proved fruitful in Scientific Machine Learning (SciML) [2]. The central idea is that the output of the penultimate layer can be seen as providing a basis for the approximation space. With this perspective we develop efficient “operator-split” training algorithms, and new initialization strategies motivated by stability concerns. The second part extends the adaptive basis perspective by considering all layers of the neural network to be basis functions in a learned approximation space. We discuss the challenges associated with the typical neural network architectures that use globally supported basis functions. We then describe Kolmogorov-Arnold networks (KANs) as an alternate architecture built on locally supported splines basis functions [3]. A one-to-one relationship between KANs and a multi-channel ReLU network is shown and used to argue a relaxation property reminiscent of properties of matrix-split iterative linear solvers. Finally, using a natural refinement algorithm combined with the relaxation property, we develop a multilevel training algorithm for KANs demonstrating it on problems in SciML.

1. Dash, et al., Optimizing distributed training on frontier for large language models. In ISC High Performance 2024 Research Paper Proceedings (39th Intl. Conference), 2024.
2. Cyr, et al., Robust training and initialization of deep neural networks: An adaptive basis viewpoint, Mathematical and Scientific Machine Learning, 2020. 
3. Southworth, et al. Multilevel Training for Kolmogorov Arnold Networks, arXiv:2603.04827, 2026 (submitted to SISC)

Bio: Eric C. Cyr has been at Sandia Albuquerque since 2009 and is a Distinguished Member of the Technical staff. Prior to that he received a BS in Computer Science from Clemson University in 2002, and a PhD in Computer Science from University of Illinois at Urbana-Champaign in 2008. His formal training is in numerical methods, and scientific computing. As a Sandian he has worked in a range of diverse areas including high-performance computing, preconditioners for multi-physics, discretizations for computational plasma simulation, sensitivity analysis for PDEs, numerical optimization, and software development for science applications. In 2018, Eric received the DOE Early Career Award to fund a focus on developing layer-parallel methods for training deep neural networks. This work has broadened his exposure to machine learning. To this end, Eric is working to extend the applicability of HPC to deep learning, understand the behavior and approximation properties of neural networks, and develop methodologies where machine learning can be used to enhance the impact of computation on science and engineering disciplines.

In January 2014, Georgia Tech launched an online Master of Science in Computer Science for under $7,000 — a fraction of the $40,000 charged by comparable public universities and $70,000 at private institutions. What began with 5 courses and 380 students has become, by every measure, the largest academic degree program in the world in any subject: 52 courses, 19,000 enrolled students, and 16,500 graduates — more than 7,000 in the last three years alone. Applications doubled in the last 4 years to over 11,000 and are still climbing after more than a decade.

Harvard researchers concluded that OMSCS is the first rigorous evidence that an online degree program can increase educational attainment, projecting it will raise the number of annual MS CS graduates in the United States by at least 7%. The program has catalyzed more than 50 similar programs at over 30 universities worldwide. In November 2023, Forbes called it the best degree program ever.

This talk describes how OMSCS came about, what Georgia Tech has learned from twelve years of running it, and what it reveals about the future of higher education — a future in which online learning plays a far larger and more consequential role than most institutions have yet imagined.

Please join us for coffee and an informal discussion of this incident and what it implies about AI safety and security.  We will be joined by members of the AI Safety Initiative, who hosted an event about this incident last month.

Georgia Tech students, faculty, and staff are welcome. If you do not have access to Coda, near the beginning of the event we will have a staff member or student waiting by the reception desk near the elevators to escort people up.

Title: Rethinking Memory: From Long-Horizon Video to Interactive World Models

Abstract: Video generation has made remarkable progress in visual quality, yet turning short clips into continuously evolving, controllable experiences remains a fundamental challenge. A model must preserve what matters from the past while allowing new events to unfold, respond to user input, and remain computationally efficient as generation unfolds. In this talk, I will explore these challenges through a central question: What should a video model remember, and how should it use that memory?

I will present three complementary approaches to rethinking memory in autoregressive video generation. First, Infinity-RoPE (CVPR’26) revisits how temporal context is organized, enabling longer video generation and more responsive control through inference-time interventions. Next, AdaState (NeurIPS’26) replaces a frozen anchor frame with an evolving latent state, balancing temporal consistency with dynamic scene progression. Finally, VideoMLA (NeurIPS’26) rethinks how video models store and access past context, substantially compressing their memory while preserving generation quality.

I will close with broader directions toward controllable world models (SPAWN, NeurIPS’26), physically grounded (HiPhy, NeurIPS’26) and diverse (DPP-GRPO, CVPR’26) video generation models. This research advances a broader vision for generative cinematography, supported by an NSF CAREER’26 award, toward generative worlds that remember the past, respond to our choices, and evolve as we interact with them.

Bio: Pinar Yanardag is an Assistant Professor of Computer Science at Virginia Tech, where she leads GEMLAB (Generative Modeling Lab). Her research focuses on controllable and personalized generative AI, with an emphasis on video generation and interactive world models. Her goal is to build systems that give people greater creative control over generated content and experiences. Since joining Virginia Tech in Fall 2023, her lab has produced over 25 publications at venues including CVPR, ICCV, and NeurIPS. She completed her PhD at Purdue as a Fulbright Fellow and was a postdoctoral researcher at the MIT Media Lab, where her generative AI projects attracted over one million participants and coverage from TIME, The Washington Post, and CNN. She is a recipient of the NSF CAREER award for her research on generative cinematography, and her research has received support from industry partners including Google, Adobe, and Snap.

Abstract: Following Mathlib's success in building a shared foundation for formalized mathematics, CSLib is a community-oriented effort to build a similar infrastructure for computer science and software in the Lean theorem prover.

In this talk, I will present CSLib's current state, including its architecture, governance, organization, and how to contribute. Rather than giving only a high-level overview, I will use minimal working examples to illustrate the library's current API. I will showcase abstractions and definitions already available, ranging from computational paradigms to modeling and programming languages, logics, algorithms, and infrastructure to verify their properties.

I will also introduce the CSLib Initiative, supported by Renaissance Philanthropy, which aims to coordinate and accelerate the development of the library and its surrounding ecosystem. I will discuss the CSLib Initiative roadmap

Finally, I will show how CSLib is already being used beyond the library itself, through community projects, textbooks, courses, and large-scale verification efforts. These include Software Foundations in Lean, the Functional Algorithms Design and Computational Semantics with Lean, and the port of Amazon's s2n-bignum formal proofs to Lean.

Bio: Alexandre Rademaker is the Director of the CSLib Initiative at Renaissance Philanthropy. CSLib is a global open-source effort to build a library of formalized computer science in Lean. He also cooperates with NYU in DARPA's ExpMath program, which aims to accelerate mathematics research with AI.

Alexandre is also a professor at EMAp/FGV (Applied Mathematics School, Getulio Vargas Foundation), where he has been teaching for about 20 years. He worked at IBM Research for 12 years until April 2025, when he left to lead the Specification IDE project at Atlas Computing, part of the Formal Verification of Software initiative funded by Schmidt Sciences under their Science of Trustworthy AI program.

During his PhD, he held research fellowships at Microsoft Research, working with the Z3 SMT solver team, and at SRI International. He has published more than 100 papers and spent much of his career leading open-source research collaborations with international communities, working with computational linguistics and building language resources. He was publication chair of the Language Resources and Evaluation Conference (LREC) and has served on the boards of research associations in Brazil and internationally.

Alexandre received a PhD in Computer Science from PUC-Rio, where his thesis on proof theory for description logics was published by Springer, and is based in Rio de Janeiro.

Join us for an informal gathering, tour our space and and learn more about our IRI and the latest in ML and AI research. This event is open to faculty, students, staff, and research professionals

There will be refreshments and swag for all.

Program Courses:

• CS 4001 – Computing & Society (Ethics Credit) - Students will critically examine how issues around technology, such as globalization, bias, privacy, and others, affect people worldwide.

• CS 3751 – Intro to User Interface Design - Students will explore culturally situated approaches to design and, through the course project, create an inventive design artifact.

No questions and ready to go? Apply HERE. Email indiaprogram@cc.gatech.edu for questions. To find out more about available virtual meeting opportunities, go to cc.gatech.edu/india.

Friday, Oct. 2 
6:00 - 8:00 p.m.
Tech Rec
John Lewis Student Center
Pizza to be provided

For more about CSE GSE, check out: https://cse.gatech.edu/cse-graduate-student-association

Berlin is a growing Tech Hub in Europe, home to innovative companies and research institutions, and host to tech meetups, hackathons, and conferences. With focus on entrepreneurship and start-up culture, this program is a great option if you are a CM major, if your CS Threads are Intelligence or Information-Internetworks, or if you’re doing the Create-X: Idea-To-Protype Junior Design option.

Review the attached flyer and email berlin@cc.gatech.edu for questions. Application deadline: December 1st in Atlas.

We invite you to study abroad this summer with the College of Computing! Take 12 GT credit hours while immersing yourself in the exciting environments of Barcelona.

Email barcelona@cc.gatech.edu for questions. Application deadline: December 1st in Atlas.

Bio: Enric Boix-Adsera is an assistant professor of statistics and data science. His parents are originally from Barcelona, and he grew up mostly in Princeton, New Jersey. Before coming to Wharton, he earned a doctorate in electrical engineering and computer science from MIT.

Boix-Adsera studies the learning processes of artificial intelligence models, such as ChatGPT, with the goal of improving their efficiency and reliability.

 For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.