Petar Veličković is a Research Scientist at DeepMind. He holds a PhD in Computer Science from the University of Cambridge (Trinity College), obtained under the supervision of Prof Pietro Liò. His research interests involve devising neural network architectures that operate on nontrivially structured data (such as graphs), and their applications in algorithmic reasoning and computational biology. In particular, he is the first author of Graph Attention Networks—a popular convolutional layer for graphs—and Deep Graph Infomax—a scalable local/global unsupervised learning pipeline for graphs (featured in ZDNet). Further, his research has been used in substantially improving the travel-time predictions in Google Maps (covered by outlets including the CNBC, Endgadget, VentureBeat, CNET, the Verge and ZDNet).

Michal Rolínek is a postdoctoral researcher in the Autonomous Learning group at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He obtained his PhD at IST Austria in theoretical combinatorial optimization under the supervision of Vladimir Kolmogorov. His interests lie at the intersection of combinatorial optimization and deep learning with applications to computer vision. His first author papers were twice among the nominees for the best paper award at CVPR (2018 and 2020). He has also been active in the mathematics olympiad community.

Katie Bouman is a Rosenberg Scholar and Assistant Professor of Computing and Mathematical Sciences (CMS) and Electrical Engineering at Caltech. Her research focuses on computational imaging: designing systems that tightly integrate algorithm and sensor design, making it possible to observe phenomena previously difficult or impossible to measure with traditional approaches. Her group at Caltech combines ideas from signal processing, computer vision, machine learning, and physics to find and exploit hidden signals for both scientific discovery and technological innovation. She worked with the Event Horizon Telescope, which published the first picture of a black hole in April 2019.

Ellen is a final-year PhD student in the Computer Science Department at Carnegie Mellon University (CMU), advised by Nina Balcan and Tuomas Sandholm. She is broadly interested in machine learning theory, artificial intelligence, algorithm design, and the interface between economics and computation. She is a recipient of the IBM PhD Fellowship, the Fellowship in Digital Health from CMU's Center for Machine Learning and Health, and the NSF Graduate Research Fellowship.

Yuandong is a Research Scientist in Facebook AI Research (FAIR). Before joining Facebook, he was a Researcher/Software Engineer in Google X, Self-driving Car team in 2013-2014. He received his Ph.D in the Robotics Institute, Carnegie Mellon University, advised by Srinivasa Narasimhan. He received Master and Bachelor degrees in Computer Science and Engineering Department, Shanghai Jiao Tong University. He am a recipient of ICCV 2013 Marr Prize Honorable Mentions for a hierarchical framework that gives globally optimal guarantees for non-convex non-rigid image deformation, and received Microsoft Research PhD Fellowship (2011-2013).

Kevin Ellis is a research scientist at Common Sense Machines. He works in program synthesis and machine learning. He did his PhD at MIT under the supervision of Josh Tenenbaum and Armando Solar-Lezama, and will be starting as an assistant professor in the computer science department at Cornell starting 2021. Website: http://web.mit.edu/ellisk/www/

Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and also serves as chief scientist of AI research for the Bosch Center for Artificial Intelligence. His work spans the intersection of machine learning and optimization, with a large focus on developing more robust and rigorous methods in deep learning. In addition, he has worked in a number of application areas, highlighted by work on sustainability and smart energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), IJCAI, KDD, and PESGM.

Armando Solar-Lezama is an Associate Professor at MIT where he leads the Computer Aided Programming Group. Before that, he was a graduate student at UC Berkeley, and an undergraduate at Texas A&M. His work focuses on developing new technologies for program synthesis as well as developing new applications of this technology. His work has been published at major venues in a variety of fields including programming languages (PLDI, POPL), formal methods (CAV), computer systems (SOSP), software engineering (ICSE, FSE), databases (SIGMOD), high-performance computing (SC) and machine learning (NeurIPS).