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.

Title: TBD

Abstract: TBD

Bio: TBD

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.

Bio: Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Associate Professor at MIT, where he holds dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS), and is a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). His research interests broadly lie in machine learning, systems and control, and mathematical optimization. His research lab focuses on various aspects of reliable AI systems, with applications to high-stakes and safety-critical settings. He obtained his PhD in Computing and Mathematical Sciences (CMS) from the California Institute of Technology (Caltech) in 2020, his MSc in electrical engineering from the University of Southern California in 2015, and his BSc in electrical engineering and physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University in 2021. Additionally, he was a research scientist intern at Google DeepMind in 2019. He is a recipient of several awards, including the National Science Foundation CAREER Award, research awards from Amazon, Google, and MathWorks, among others, and the inaugural Information Theory and Applications (ITA) “Sun” (Gold) Graduation Award. His work has been recognized with best paper awards at several venues, including Learning for Dynamics and Control (L4DC), the INFORMS Junior Faculty Paper Competition, and ACM Greenmetrics. He was named in the list of Outstanding Academic Leaders in Data by the CDO Magazine for two consecutive years in 2024 and 2023. His teaching and mentorship have been recognized with the Joseph A. Martore (1975) Excellence in Teaching Award, the Frank E. Perkins Award for Excellence in Graduate Advising (MIT Institute Award), and the UROP Outstanding Mentor Award.

While we prefer you join in person, if you are unable to attend, join online at https://gatech.zoom.us/j/91508317626?pwd=WrbKsZaW2tcby2uQdVcAjsYaX0FL8l.1

Meeting ID: 915 0831 7626
Passcode: 647326

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

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 upcoming Info Sessions or virtual meeting opportunities, visit cc.gatech.edu/india.

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.

Bio: Sarah H. Q. Li is an assistant professor in the School of Aerospace Engineering at the Georgia Institute of Technology, where she leads the Control, Coordination, and Competition under Uncertainty (C3U) Lab. She received her Ph.D. in aeronautics and astronautics at the University of Washington, and completed her postdoc with the Autonomous Control Lab at ETH Zurich. Her work combines optimal control, stochastic decision processes, and game theory to study how autonomous agents can coordinate safely and efficiently in uncertain environments.

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

See the Full List of Posters and Presenters Here

Bio: Lars Ruthotto is a Professor in the Departments of Mathematics and Computer Science at Emory University, where he is a member of the Scientific Computing Group and directs the Emory REU/RET Site for Computational Mathematics for Data Science. He earned his PhD in Mathematics from the University of Muenster in 2012 and was a postdoc at the University of British Columbia before joining Emory. His research develops computational methods at the interface of scientific computing, optimization, machine learning, and inverse problems, with recent emphasis on generative modeling, high-dimensional partial differential equations, and stochastic optimal control. He is a recipient of the NSF CAREER Award and serves as a Section Editor for the SIAM Journal on Scientific Computing.

Access Via Zoom: https://gatech.zoom.us/j/93198655277?pwd=R49ghn4Eacaqbc0YIBa68NdmgbPYYO.1

Meeting ID: 931 9865 5277
Passcode: 938967

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

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.

Bio:  Hal Daumé III is a professor of computer science with appointments in the Maryland Language Science Center and the University of Maryland Institute for Advanced Computer Studies, where he is also the director of TRAILS. In addition to fairness and natural language processing, his research focuses on understanding computational properties of learning and language as well as trustworthy AI.

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

The 2026 Fall GT Computing Career Fair takes place Sept. 17 at the McCamish Pavilion. Doors open at 9 a.m. 

Join the College of Computing Tailgate to celebrate Georgia Tech spirit while connecting with students, faculty, staff, alumni, and friends of the College. This event brings the GT Computing community together for networking, camaraderie, and a fun kickoff to game day traditions.

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 also a core faculty member of the GRASP Lab. Before joining Penn, she was a Postdoctoral Associate in the Interactive Robotics Group at the Massachusetts Institute of Technology (MIT). She earned her Ph.D. in Robotics, Control, and Intelligent Systems from the Swiss Federal Institute of Technology in Lausanne (EPFL) and previously conducted research at the Robotics and Mechatronics Institute of the German Aerospace Center (DLR) and at 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. Her research focuses on developing integrated learning, control, and estimation algorithms that enable fluid human-robot training, co-existence and collaboration with formal guarantees of safety, efficiency, and robustness. Her work bridges control theory, machine learning, AI, perception, and biomechanics through the lens of physical human-robot interaction. She is a recipient of a 2025 NSF CAREER Award, a finalist for the 2020 Georges Giralt Best Ph.D. and 2017 KUKA Innovation Awards, and has received multiple best paper honors at leading robotics venues such as ICRA, RSS and T-RO.

Bio: The Ghosh group engages in cross-disciplinary collaborations and welcomes students from diverse academic backgrounds. Most projects focus on addressing challenges in fundamental physics and astrophysics using computational and AI/ML tools, making the group a natural fit for students with strong skills or interests in these areas. We develop methods to automate theoretical physics calculations using reinforcement learning and LLM agents, enabling rapid testing of new theories. We also work on simulation, experimental design and high-dimensional statistical inference techniques powered by AI to accelerate scientific discovery. Data analysis problems at the scale of the Large Hadron Collider or multi-messenger astronomy often demand rapid decision-making, and we design efficient AI algorithms that can be deployed on fast hardware to meet these challenges.

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

Bio: TBA

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