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.
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.
All Seminars Held 12pm - 1pm in CODA
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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.
Bio: Jeremy D. Brown, the John C. Malone Assistant Professor in the Department of Mechanical Engineering, explores the interface between humans and robotics, with a specific focus on medical applications and haptic feedback.
Brown’s research sits at the intersection of engineering, biomechatronics, medicine, and psychophysics. He develops novel haptic interfaces for upper-limb prosthetics and minimally invasive surgical robotics. Applications of this research include giving amputees a sense of touch through their prostheses ,and helping surgeons use surgical robots to improve their accuracy and precision when performing delicate procedures.
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Bio: Tapomayukh "Tapo" Bhattacharjee is an Assistant Professor in the Department of Computer Science at Cornell University where he directs the EmPRISE Lab (https://emprise.cs.cornell.edu/). He completed his Ph.D. in Robotics from Georgia Institute of Technology and was an NIH Ruth L. Kirschstein NRSA postdoctoral research associate in Computer Science & Engineering at the University of Washington. His primary research interests are in the area of physical robot caregiving and physical human-robot interaction. He is the recipient of TRI Young Faculty Researcher Award'24, NSF CAREER Award'23, AFCEA 40 under 40 Award'22, and his work has won Best Systems Paper Award at HRI’26, Best Paper Award at RSS’25, Best Paper and Student Paper Award Finalist and Best HRI Paper Award Finalist at ICRA’25, Best Systems Paper Award Finalist at HRI'24, Best Demo Award at HRI'24, Best RoboCup Paper Award at IROS’22, Best Paper Award Finalist and ABB Best Student Paper Award Finalist at IROS’22, Best Technical Advances Paper Award at HRI'19, and Best Demonstration Award at NeurIPS’18. His work has also been featured in many media outlets including the BBC, Reuters, New York Times, IEEE Spectrum, and GeekWire and his robot-assisted feeding work was selected to be one of the best interactive designs of 2019 by Fast Company.
]]>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: Dr. Arash Adel is the founder and director of ARG. He is an assistant professor at the School of Architecture and an associated faculty of the Department of Computer Science at Princeton University. Prior to joining Princeton University in 2023, Adel was an assistant professor of Architecture at the University of Michigan. Adel received his Doctorate in Architecture from the Swiss Federal Institute of Technology (ETH) and his Master’s in Architecture from Harvard University.
Adel’s research interests include construction robotics, human-robot collaboration, automated building assembly, additive manufacturing, computational design, and STEM education. He is particularly known for his work with novel integrative computational design methods coupled with robotic assembly techniques for manufacturing nonstandard timber buildings and structures. His experience includes numerous widely-published projects such as the DFAB HOUSE located in Dubendorf, Switzerland, and, more recently, the Robotically Fabricated Structure (RFS) timber pavilion sited at the Matthaei Botanical Gardens in Ann Arbor, Michigan, USA.
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.
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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.
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Bio: TBA
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.