Overview

Dr. Bouman is a Rosenberg Scholar and Assistant Professor of Computing and Mathematical Sciences (CMS) and by courtesy in Electrical Engineering and Astronomy at Caltech in Pasadena, California. 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. Dr. Bouman's 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.

Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). 

Su-hua Wang’s research investigates cognitive development situated in sociocultural contexts, including technology, parent-child interaction, and children’s play.  Her research group has been working on multiple projects, to understand how learning occurs in the first five years as babies and young children interact with parents in everyday activities that involve books, toys, and technology. Her group is particularly interested in cultural ways of learning that are supported by families from different backgrounds

.Dr. Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy. He is also an Amazon scholar and co-founder of Deepart UG, and Layer7 AI GmbH, and co-initiator of the European ELLIS initiative. His main research focus is on robust vision and neural decision making with the goal to advance internal model learning with neural networks. He received the first Bernstein Prize for Computational Neuroscience in 2006 and later became director of the Bernstein Center Tübingen and vice chair of the German Bernstein network.

Dr. Noah Smith is Professor of Computer Science & Engineering at the University of Washington, Adjunct in Linguistics, Affiliate of the Center for Statistics and the Social Sciences, and Senior Data Science Fellow at the eScience Institute. He is also Senior Research Manager for the AllenNLP team at the Allen Institute for Artificial Intelligence. Previously, he was Finmeccanica Associate Professor in the School of Computer Science at Carnegie Mellon University, completed his Ph.D. as a Hertz Foundation Fellow at Johns Hopkins University, and studied at the Universities of Maryland and Edinburgh and Western Maryland College. His undergraduate, graduate, and postdoctoral advisees have earned positions at leading organizations all over the world, where they make wide-ranging and high-impact research contributions.

Dr. Liang Huang is currently an Associate Professor of EECS at Oregon State University and Distinguished Scientist (part-time) at Baidu Research USA. Before that he was Assistant Professor for three years at the City University of New York (CUNY) and a part-time Research Scientist with IBM's Watson Group. He graduated in 2008 from Penn and has worked as a Research Scientist at Google and a Research Assistant Professor at USC/ISI. Most of his work develops fast algorithms and provable theory to speedup large-scale natural language processing, structured machine learning, and computational structural biology. His recognitions include ACL 2019 Keynote Speech, ACL 2008 Best Paper Award (sole author), EMNLP 2016 Best Paper Honorable Mention, several best paper nominations (ACL 2007, EMNLP 2008, ACL 2010, SIGMOD 2018), two Google Faculty Research Awards (2010 and 2013), and a University Teaching Prize at Penn (2005). He also co-authored a best-selling textbook in China on algorithms for programming contests. His work on simultaneous translation has been covered by numerous media reports.