The Speakers

Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science; Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Deputy Dean of Research for Schwarzman College of Computing at MIT. Rus’ research interests are in robotics, artificial intelligence, and data science. The focus of her work is developing the science and engineering of autonomy, toward the long-term objective of enabling a future with machines pervasively integrated into the fabric of life, supporting people with cognitive and physical tasks. Her research addresses some of the gaps between where robots are today and the promise of pervasive robots: increasing the ability of machines to reason, learn, and adapt to complex tasks in human-centered environments, developing intuitive interfaces between robots and people, and creating the tools for designing and fabricating new robots quickly and efficiently. Rus serves as Director of the Toyota-CSAIL Joint Research Center, whose focus is the advancement of AI research and its applications to intelligent vehicles. She is a MITRE senior visiting fellow, serves as a USA expert member for GPAI (Global Partnerships in AI), a member of the board of advisers for Scientific American, a member of the Defense Innovation Board, and a member of several other boards of technical companies. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences. She is the recipient of the 2017 Engelberger Robotics Award from the Robotics Industries Association. She earned her PhD in Computer Science from Cornell University.

Ce is an Assistant Professor in Computer Science at ETH Zurich. The mission of his research is to make machine learning techniques widely accessible-​​-​-while being cost-​efficient and trustworthy-​​-​-to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current research focuses on building next-​generation machine learning platforms and systems that are data-​centric, human-​centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin-​​Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.

Yani is an Assistant Professor at the University of Calgary in the Department of Electrical and Software Engineering of the Schulich School of Engineering, and leads the Calgary Machine Learning Lab. He was previously a Postdoctoral Research Fellow at the Vector Institute and University of Guelph, working with Prof. Graham Taylor, and a Visiting Researcher at Google Brain Toronto/Google AR Core. He completed his PhD at the University of Cambridge in 2018 supported by a Microsoft Research Ph.D. Scholarship, where I was supervised by Professor Roberto Cipolla and Dr. Antonio Criminisi. He is currently interested in efficient deep learning, specifically for computer vision problems, and sparse neural network training. I have in the past worked on exoplanet detection with NASA, medical imaging with Microsoft Research and the Gates Foundation, and 3D computer vision methods for processing and recognizing objects in large point clouds.

Martha White is an Associate Professor of Computing Science at the University of Alberta and a PI of Amii--the Alberta Machine Intelligence Institute--which is one of the top machine learning centers in the world. She holds a Canada CIFAR AI Chair and received IEEE's "AIs 10 to Watch: The Future of AI" award in 2020. She has authored more than 50 papers in top journals and conferences. Martha is an associate editor for TPAMI, and has served as co-program chair for ICLR and area chair for many conferences in AI and ML, including ICML, NeurIPS, AAAI and IJCAI. Her research focus is on developing algorithms for agents continually learning on streams of data, with an emphasis on representation learning and reinforcement learning. 

Kwabena is a Professor of Bioengineering, of Electrical Engineering and, by courtesy, of Computer Science. Boahen founded and directs Stanford’s Brains in Silicon lab, which develops silicon integrated circuits that emulate the way neurons compute and computational models that link neuronal biophysics to cognitive behavior. This interdisciplinary research bridges neurobiology and medicine with electronics and computer science, bringing together these seemingly disparate fields. He received his B.S. and M.S. in electrical engineering in 1989 from Johns Hopkins University and his PhD in computation and neural systems in 1997 from the California Institute of Technology, where he was advised by Carver Mead.