The Speakers

Dan Alistarh

Dan is a Professor at IST Austria. His research focuses on high-performance algorithms, and spans from algorithms and lower bounds, to practical implementations. Before IST, he was a researcher at ETH Zurich and Microsoft Research, Cambridge, UK. Prior to that, he was a Postdoctoral Associate at MIT CSAIL, working with Prof. Nir Shavit. He received his PhD from the EPFL, under the guidance of Prof. Rachid Guerraoui. His research is supported by a 2018 ERC Starting Grant, the Austrian FWF, and generous support from Amazon and Google. He is also a Principal Machine Learning Researcher at Neural Magic.

Daniela Rus

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.

Aakanksha Chowdhery

Aakanksha has worked on edge computing for video analytics, at the intersection of mobile systems and machine learning, in her postdoc research at Microsoft Research and Princeton. She recently worked on content-aware compression approaches for video analytics on edge devices and has applied her research to real-time video use-cases in a network of mobile, surveillance, and drone cameras. Her work has also contributed to the Open Fog Consortium standards. She graduated with a PhD in signal processing from Stanford University. Aakanksha currently leads training and infrastructure for Large Language Models at Google Brain.

Ce Zhang

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.

Nir Shavit

Nir is a professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and principal investigator of the Multiprocessor Algorithmics Group and the Computational Connectomics Group. His main interests are techniques for designing, implementing, and reasoning about multiprocessor algorithms, in particular concurrent data structures for multicore machines and the mathematical foundations of the computation models that govern their behavior. He is also interested in understanding how neural tissue computes and is part of an effort to do so by extracting connectivity maps of the brain, a field called connectomics. Nir Shavit received B.Sc. and M.Sc. degrees in Computer Science from the Technion - Israel Institute of Technology in 1984 and 1986, and a Ph.D. in Computer Science from the Hebrew University of Jerusalem in 1990. Shavit is a co-author of the book The Art of Multiprocessor Programming. He is a recipient of the 2004 Gödel Prize in theoretical computer science for his work on applying tools from algebraic topology to model shared memory computability and of the 2012 Dijkstra Prize in Distributed Computing for the introduction of Software Transactional Memory. His interests are techniques for designing, implementing, and reasoning about multiprocessor algorithms. He is also interested in understanding how neural tissue computes and is part of an effort to do so by extracting connectivity maps of the brain, a field called connectomics. Nir is the principal investigator of the Multiprocessor Algorithmics Group and the Computational Connectomics Group.

Yani Ioannou

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.

Yuejie Chi

Yuejie is a Professor in the department of Electrical and Computer Engineering, and a faculty affiliate with the Machine Learning department and CyLab at Carnegie Mellon University. She received her Ph.D. and M.A. from Princeton University, and B. Eng. (Hon.) from Tsinghua University, all in Electrical Engineering. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing, imaging, and societal systems, broadly defined. Among others, Dr. Chi received the Presidential Early Career Award for Scientists and Engineers (PECASE), the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing, and held the inaugural Robert E. Doherty Early Career Development Professorship. She was named a Goldsmith Lecturer by IEEE Information Theory Society and a Distinguished Lecturer by IEEE Signal Processing Society.

Martha White

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.