Program

He is Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Science at the University of California at Berkeley, where he also holds appointments in vision science, cognitive science and Bioengineering. He received the PhD degree in Computer Science from Stanford University in 1985 following which he joined UC Berkeley as a faculty member. He served as Chair of the Computer Science Division during 2002-2006, and of the Department of EECS during 2004-2006. Jitendra's group has worked on computer vision, computational modeling of biological vision, computer graphics and machine learning. Several well-known concepts and algorithms arose in this work, such as anisotropic diffusion, normalized cuts, high dynamic range imaging and shape contexts. He was awarded the Longuet-Higgins Award for “A Contribution that has Stood the Test of Time” twice, in 2007 and 2008, received the PAMI Distinguished Researcher Award in computer vision in 2013 the K.S. Fu prize in 2014, and the IEEE PAMI Helmholtz prize for two different papers in 2015. Jitendra Malik is a Fellow of the IEEE, ACM, and the American Academy of Arts and Sciences, and a member of the National Academy of Sciences and the National Academy of Engineering.

He is a Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University. He directs the research group on Compositional Cognition, Vision, and Learning. He is affiliated with the Center for Brains, Minds and Machines, and the NSF Expedition in Computing, Visual Cortex On Silicon. Alan Yuille received the BA degree in mathematics from the University of Cambridge in 1976. His PhD on theoretical physics, supervised by Prof. S.W. Hawking, was approved in 1981. He was a research scientist in the Artificial Intelligence Laboratory at MIT and the Division of Applied Sciences at Harvard University from 1982 to 1988. He served as an assistant and associate professor at Harvard until 1996. He was a senior research scientist at the Smith-Kettlewell Eye Research Institute from 1996 to 2002. He was a full professor of Statistics at the University of California, Los Angeles, as a full professor with joint appointments in computer science, psychiatry, and psychology. He moved to Johns Hopkins University in January 2016. His research interests include computational models of vision, mathematical models of cognition, medical image analysis, and artificial intelligence and neural networks.

He got his bachelor's degree from Tsinghua university, PhD in computer science from Harvard university, supervised by the Fields medalist, Prof. Shing-Tung Yau. Currently, he is a New York State Empire Innovation Professor in the Computer Science Department, Stony Brook university. Dr. Gu's research focuses on applying modern geometry in engineering and medicine fields. Together with Prof. Shing-Tung Yau, Dr. Gu and other collaborators have founded an interdisciplinary field: Computational Conformal Geometry. Dr. Gu has won NSF Career award, Morningside Gold medal in applied Mathematics.

He is a research scientist at Facebook AI Research (FAIR) as of Fall 2014 with a focus on computer vision and machine learning. Prior, He spent three years at Microsoft Research in Redmond (MSR). He helped cofound Anchovi Labs (acquired by Dropbox in 2012) and was a postdoc at the Computation Vision Lab at Caltech under Pietro Perona until 2011. He received my PhD under the guidance of Serge Belongie at UCSD in 2007 and have continued doing research in vision and learning since.

He is a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University, and is also a staff research scientist at Robotics at Google. His research focuses on video representation learning; this includes neural architecture search, supervised/self-supervised representation learning, and video models for robots. He previously was an assistant professor at Indiana University Bloomington, and was a staff researcher within the Robotics Section of NASA's Jet Propulsion Laboratory (JPL). Dr. Ryoo received his Ph.D. from the University of Texas at Austin in 2008, and B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004.

He is an assistant professor at MIT EECS. Dr. Han received the Ph.D. degree in Electrical Engineering from Stanford advised by Prof. Bill Dally. Dr. Han’s research focuses on efficient deep learning computing. He proposed “Deep Compression” that can compress neural networks by an order of magnitude, and the hardware implementation “Efficient Inference Engine” that brings compressed weights and sparsity into deep learning accelerators. His work received the best paper award in ICLR’16 and FPGA’17; He is the recipient of 35 Innovators Under 35 (TR35) and NSF CAREER Award. The pruning, compression and acceleration techniques have been integrated into many AI chip products in industry. His hobbies include biking, snowboarding, drum sets, and design.

She works at IBM Research, NY. Her research is focused on automating deep learning and machine learning. To that end, she was involved in developing tools to achieve this automation for IBM. She has a masters' degree from Columbia University.

He is a researcher at IBM Research, Ireland. He works on developing a tool to automate deep learning. Previously, he received his Ph.D. in Machine Learning from the University of Hildesheim. His research interest include automation of machine learning, transfer learning, time series classification and recommender systems.