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.

Title: Generative Artificial Intelligence for Uncertainty Quantification

Abstract: Generative artificial intelligence models—including variational autoencoders, normalizing flows, generative adversarial networks, and diffusion models—have dramatically advanced the realism and quality of generated images, text, and audio. Beyond these tasks, generative models hold great promise as powerful tools for probability density estimation and high-dimensional sampling, which are central to uncertainty quantification (UQ) tasks such as amortized Bayesian inference and data assimilation. However, while research on image synthesis emphasizes producing high-quality individual samples, UQ applications require accurate approximation of statistical quantities of interest rather than visually realistic samples. As a result, direct application of existing generative models to UQ problems can lead to biased approximations or unstable training. In this talk, we will introduce several new generative approaches tailored to UQ. These include training-free diffusion models for density estimation, a score-based nonlinear filter for data assimilation, and training-free conditional diffusion models for amortized Bayesian inference. We will demonstrate their effectiveness across a range of tasks, including density estimation for unimodal and multimodal distributions, learning stochastic dynamical systems, parameter estimation via amortized inference, and scalable data assimilation for atmospheric models.

Bio: Dr. Guannan Zhang is a Distinguished Staff Scientist in Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL). He earned my Ph.D. in applied mathematics at Florida State University in 2012. He joined ORNL in 2012 as the Householder fellow. He received the DOE Early Career Award in 2022. Guannan's research interests include high-dimensional approximation, uncertainty quantification, machine learning and artificial intelligence, stochastic methods for scientific inverse problems. 

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.

Apply HERE. Email indiaprogram@cc.gatech.edu for questions.

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.

Apply HERE. Email indiaprogram@cc.gatech.edu for questions and to find out more about upcoming Info Sessions.

Bio: Ferdous Alam is an Assistant Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology and an affiliated faculty member of ML@GT. He is also a core faculty member at the Institute for Robotics and Intelligent Machines (IRIM). He leads the Inference Lab, where his research focuses on the computational foundations of AI-driven engineering design and robotic manufacturing, including generative modeling of mechanical systems, representation learning for 3D geometry, and robot learning for manufacturing tasks. Before joining Georgia Tech, Dr. Alam was a postdoctoral researcher at MIT, and he previously received his PhD from The Ohio State University. His research has been recognized with several Best Paper Awards at leading ASME conferences, as well as the Google Research Scholar Award in Applied Science.

Zoom Link: https://gatech.zoom.us/j/99778983822?pwd=O0YsaiaJp6ipUrlfIYSHa9ChwbLwXJ.1
Meeting ID: 997 7898 3822 
Passcode: 524773

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

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 for its annual fall 5K Community Walk/Run. The event focuses on fun and promotes health, wellness, and community. Whether you walk or run, you will connect with students, faculty, staff, alumni, and friends while celebrating school spirit and supporting an active lifestyle.

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: Sarah Li is an assistant professor at the Daniel Guggenheim School of Aerospace Engineering, a faculty member of the Institute for Robotics and Intelligent Machines, a faculty member of ML@GT, and an affiliate of the Supply Chain and Logistics Institute at Georgia Tech. Her research advances multi-agent models and algorithms to overcome challenges facing future air and space mobility systems. Her technical work lies at the intersection of game theory, stochastic control, and optimization to enable optimal and safe decision-making of autonomous systems in interactive settings. Sarah earned her Ph.D. in Aeronautics and Astronautics from the University of Washington and her B.A.Sc. in Engineering Physics from the University of British Columbia.

Bio: Andrea Bajcsy is an Assistant Professor in the Robotics Institute at Carnegie Mellon University where she leads the Interactive and Trustworthy Robotics Lab (Intent Lab). She broadly works at the intersection of robotics, machine learning, control theory, and human-AI interaction. Prior to joining CMU, Andrea received her Ph.D. in Electrical Engineering & Computer Science from University of California, Berkeley in 2022. She is the recipient of the DARPA Young Faculty Award (2025), NSF CAREER Award (2025), Amazon Research Award (2025), Google Research Scholar Award (2024), Finalist for Best Paper Award of the IEEE RAS Technical Committee on Robot Control (2024), Rising Stars in EECS Award (2021), Honorable Mention for the T-RO Best Paper Award (2020), NSF Graduate Research Fellowship (2016), and worked at NVIDIA Research for Autonomous Driving.

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.

Bio: Dr. 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). He is also a core faculty member of the Statistics and Data Science Center and the Center for Computational Science and Engineering. 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 the Learning for Dynamics and Control (L4DC), INFORMS JFIG, 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 in 2026, the Frank E. Perkins Award for Excellence in Graduate Advising (MIT Institute Award) in 2025, and the UROP Outstanding Mentor Award in 2023.

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

Bio: I am an applied mathematician interested in the interplay of scientific computing and artificial intelligence. I am a Winship Distinguished Research Associate Professor in the Department of Mathematics and the Department of Computer Science at Emory University and a member of Emory’s Scientific Computing Group. I lead the Emory REU/RET site for Computational Mathematics for Data Science. Prior to joining Emory, I was a postdoc at the University of British Columbia and I held PhD positions at the University of Lübeck and the University of Münster.

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