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Our list of speakers (sorted in alphabetical order) are:
Christopher Amato is an Associate Professor at Northeastern University where he leads the Lab for Learning and Planning in Robotics. He has published many papers in leading artificial intelligence, machine learning and robotics conferences (including winning a best paper prize at AAMAS-14 and being nominated for the best paper at RSS-15, AAAI-19, AAMAS-21 and MRS-21). He has also successfully co-organized several tutorials on multi-agent coordination and has co-authored a book on the subject. He has also won several awards such as Amazon Research Awards and an NSF CAREER Award. His research focuses on reinforcement learning in partially observable and multi-agent/multi-robot systems.
Negar Mehr is an assistant professor of Mechanical Engineering at UC Berkeley. She was previously an assistant professor of Aerospace Engineering at the University of Illinois Urbana-Champaign. Before that, she was a postdoctoral scholar at Stanford Aeronautics and Astronautics department from 2019 to 2020. She received her PhD in Mechanical Engineering from UC Berkeley in 2019 and her B.Sc. in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, in 2013. She is a recipient of the NSF CAREER Award. She was awarded the IEEE Intelligent Transportation Systems Society best Ph.D. dissertation award in 2020.
Dong-Ki Kim is a staff research scientist at Field AI, where he develops advanced foundational models for robotics in unstructured real-world environments. His research interests include multi-agent learning, with a focus on enabling multiple robots to interact, share knowledge, and learn robust coordination policies. His work received the outstanding student paper honorable mention at AAAI'19 and has been featured in NVIDIA, WIRED, and MIT News. Previously, he worked at LG AI Research, focusing on large language models for navigating complex web environments. He also worked at Carnegie Mellon University and the Toyota Technological Institute at Chicago, specializing in machine learning and robotics. He earned his M.S. and Ph.D. degrees from MIT (Committee: Professors Jonathan P. How, Jakob N. Foerster, and Pulkit Agrawal) and completed his B.S. degree (summa cum laude) at Cornell University.
Cathy Wu is an Assistant Professor at MIT in LIDS, CEE, and IDSS. She holds a Ph.D. from UC Berkeley, and B.S. and M.Eng. from MIT, all in EECS, and completed a Postdoc at Microsoft Research. Her research interests are at the intersection of machine learning, autonomy, and mobility. She is broadly interested in developing the tools and understanding necessary to confidently integrate automated decisions into societal & industrial systems. Her recent research consists of two synergistic thrusts: 1) generalizable methods for control & optimization, and 2) benchmarking performance of intelligent transportation systems. Cathy has received a number of awards, including the NSF CAREER, PhD dissertation awards, and publications with distinction. She currently serves on the Board of Governors for the IEEE ITSS and has served as an Area Chair for ICML.
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