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Prof. Ali Jadbabaie
Prof. Ali Jadbabaie
MIT
Prof. Anant Sahai
Prof. Anant Sahai
UC Berkeley
Dr. Antonio Orvieto
Dr. Antonio Orvieto
ELLIS Inst. Tübingen
Dr. Carmen Amo Alonso
Dr. Carmen Amo Alonso
Stanford University
Prof. Elad Hazan
Prof. Elad Hazan
Princeton University
Prof. George Pappas
Prof. George Pappas
Univ. Pennsylvania
Prof. Jaime Fernandez Fisac
Prof. Jaime Fernandez Fisac
Princeton University
Prof. Paulo Tabuada
Prof. Paulo Tabuada
UC Los Angeles
Prof. Ali Jadbabaie
Prof. Ali Jadbabaie
Ali Jadbabaie is the JR East Professor and Head of the Department of Civil and Environmental Engineering at Massachusetts Institute of Technology (MIT), where he is also a core faculty in the Institute for Data, Systems, and Society (IDSS) and a Principal Investigator in the Laboratory for Information and Decision Systems. Previously, he served as the Director of the Sociotechnical Systems Research Center and as the Associate Director of IDSS as co-founder of its flagship PhD program in Social and Engineering Systems. He received a B.S. degree with High Honors in electrical engineering with a focus on control systems from Sharif University of Technology, an M.S. degree in electrical and computer engineering from the University of New Mexico, and a Ph.D. degree in control and dynamical systems from the California Institute of Technology. He was a Postdoctoral Scholar at Yale University before joining the faculty at the University of Pennsylvania, where he was subsequently promoted through the ranks and held the Alfred Fitler Moore Professorship in network science in the Department of Electrical and Systems Engineering. He is a recipient of a National Science Foundation Career Development Award, an US Office of Naval Research Young Investigator Award, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council, and the George S. Axelby Best Paper Award from the IEEE Control Systems Society. He has been a senior author of several student best paper awards, in several conferences including the American Control Conference , IEEE Conference on Decision and Control, and IEEE International Conference on Acoustics, Speech, and Signal Processing . He is an IEEE fellow, and the recipient of a Vannevar Bush Fellowship from the Office of Secretary of Defense. He is a member of the Bush Fellows Research Study Group, as well as the National Academies Intelligence Science and Technology Advisory Group (ISTEG). His research interests are broadly focused on decision making, optimization and control, machine learning, network science and network economics, as well as quantitative and computational social science.
Prof. Anant Sahai
Prof. Anant Sahai
Anant Sahai is currently the Qualcomm Chair Professor in Berkeley's Electrical Engineering and Computer Sciences (EECS) Department, and is also a part-time Visiting Faculty Researcher at Google. After graduating with his PhD from MIT, and before joining the Berkeley faculty, he was on the theoretical/algorithmic side of a team at the startup Enuvis, Inc. He has previously served as the Treasurer for the IEEE Information Theory Society. He has coordinated machine learning efforts for SpectrumX, the NSF's Center for Spectrum Innovation, and is also very involved with the data engineering efforts there. At Berkeley, he regularly teaches the main deep learning course. His research interests span machine learning, wireless communication, information theory, signal processing, and decentralized control --- with a particular interest at the intersections of these fields. Within wireless communication, he is particularly interested in Spectrum Sharing as well as very-low-latency ultra-reliable wireless communication protocols for control. He is also interested in the foundations of machine learning, particularly as it pertains to why overparameterized models do or do not work. Recently, he has also become quite interested in in-context learning in modern ML models.
Dr. Antonio Orvieto
Dr. Antonio Orvieto
Antonio studied Control Engineering in Italy and Switzerland. He holds a PhD in Computer Science from ETH Zürich and spent time at Deepmind (UK), Meta (US), MILA (CA), INRIA (FR), and HILTI (LI). He is currently a Hector Endowed Fellow and Principal Investigator (PI) at the ELLIS Institute Tübingen and Independent Group Leader of the MPI for Intelligent Systems, where he leads the Deep Models and Optimization group. He received the ETH medal for outstanding doctoral theses and the Schmidt Sciences AI2050 Early Career Fellowship. In his research, Antonio strives to improve the efficiency of deep learning technologies by pioneering new architectures and training techniques grounded in theoretical knowledge. His work encompasses two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of handling complex data. Central to his studies is exploring innovative techniques for decoding patterns in sequential data, with implications in biology, neuroscience, natural language processing, and music generation.
Dr. Carmen Amo Alonso
Dr. Carmen Amo Alonso
Carmen Amo Alonso is a Schmidt Science Fellow affiliated with Prof. Marco Pavone's group at Stanford University. Her research lies at the intersection of control theory, machine learning, and optimization, with a focus on understanding and improving language processing and generation in both humans and machines. Carmen’s work aims to uncover the control mechanisms underlying language processing and intelligence, and leverages control-theoretic principles to develop safer, more controllable AI technologies. At Stanford, Carmen was named an Emerson Consequential Scholar for the potential of her research to positively impact society. Prior to joining Stanford, she held a postdoctoral fellow position at the Artificial Intelligence Center at ETH Zurich. Carmen earned her Ph.D. in Control and Dynamical Systems from Caltech in 2023, where she was advised by Prof. John Doyle. Her thesis on the optimal control of distributed systems under local communication constraints was awarded the Milton and Francis Clauser Doctoral Prize, which recognizes the best Ph.D. dissertation of the year across all disciplines at Caltech. During her Ph.D., her research received two best paper awards, was partially funded by Amazon and D. E. Shaw fellowships, and earned her three different Rising Star titles (EECS, Cyber-Physical Systems, and Brain and Cognitive Sciences). Besides her research collaborations across academia and industry, Carmen is committed to education for all. As a member of Clubes de Ciencia, she travels to Mexico in the summer to teach underserved students.
Prof. Elad Hazan
Prof. Elad Hazan
Elad Hazan is a professor of computer science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Among his contributions are the co-invention of the AdaGrad algorithm for deep learning, the first sublinear-time algorithms for convex optimization, and online nonstochastic control theory. He is the recipient of the Bell Labs Prize, the IBM Goldberg best paper award twice, a European Research Council grant, a Marie Curie fellowship, Google Research Award and is an ACM fellow. He served on the steering committee of the Association for Computational Learning and was program chair for the Conference on Learning Theory 2015. He is the co-founder and director of Google AI Princeton.
Prof. George Pappas
Prof. George Pappas
George Pappas is the UPS Foundation Professor at the Department of Electrical and Systems Engineering at the University of Pennsylvania. He also holds a secondary appointment in the Departments of Computer and Information Sciences, as well as Mechanical Engineering and Applied Mechanics. He currently serves as the Associate Dean for Research and Innovation in the School of Engineering and Applied Science and as the Director of the Raj and Neera Singh program in Artificial Intelligence. Pappas’s research focuses on control systems, robotics, autonomous systems, formal methods, and machine learning for safe and secure cyber-physical systems. He has received numerous awards, including the NSF PECASE, the Antonio Ruberti Young Researcher Prize, the George S. Axelby Award, the O. Hugo Schuck Best Paper Award, and the George H. Heilmeier Faculty Excellence Award. Pappas has mentored more than fifty students and postdocs, now faculty in leading universities worldwide. He is a Fellow of IEEE, IFAC, and was elected to the National Academy of Engineering in 2024.
Prof. Jaime Fernandez Fisac
Prof. Jaime Fernandez Fisac
Jaime Fernandez Fisac is an Assistant Professor of Electrical and Computer Engineering at Princeton University, where he directs the Safe Robotics Laboratory(Link is external) and co-directs Princeton AI4ALL(Link is external). His research integrates control systems, game theory, and artificial intelligence to equip robots with transparent safety assurances that users and the public can trust. Before joining Princeton, he was a Research Scientist at Waymo(Link is external), where he pioneered new approaches to interaction planning that continue to shape how autonomous vehicles share the road today. He is also the co-founder of Vault Robotics(Link is external), a startup developing agile delivery robots that work alongside human drivers. Prof. Fisac holds an Engineering Degree from Universidad Politécnica de Madrid, a Master’s in Aeronautics from Cranfield University, and a Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley. His work has been featured in The Wall Street Journal and WIRED, and recognized with the Google and Sony faculty research awards and the NSF CAREER Award.
Prof. Paulo Tabuada
Prof. Paulo Tabuada
Paulo Tabuada was born in Lisbon, Portugal, one year after the Carnation Revolution. He received his "Licenciatura" degree in Aerospace Engineering from Instituto Superior Tecnico, Lisbon, Portugal in 1998 and his Ph.D. degree in Electrical and Computer Engineering in 2002 from the Institute for Systems and Robotics, a private research institute associated with Instituto Superior Tecnico. Between January 2002 and July 2003 he was a postdoctoral researcher at the University of Pennsylvania. After spending three years at the University of Notre Dame, as an Assistant Professor, he joined the Electrical and Computer Engineering Department at the University of California, Los Angeles, where he currently is the Vijay K. Dhir Professor of Engineering. Paulo Tabuada's contributions to control and cyber-physical systems have been recognized by multiple awards including the NSF CAREER award in 2005, the Donald P. Eckman award in 2009, the George S. Axelby award in 2011, the Antonio Ruberti Prize in 2015, the grade of fellow awarded by IEEE in 2017 and by IFAC in 2019. He has been program chair and general chair for several conferences in the areas of control and of cyber-physical systems such as NecSys, HSCC, ICCPS, and CDC (in 2017). He currently serves on the HSCC steering committee and served on the editorial board of the IEEE Embedded Systems Letters and the IEEE Transactions on Automatic Control.
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