June 19, 2021

Workshop program (Pacific Time)

Keynote Speakers

Raquel Urtasun

University of Toronto, Uber ATG

Dr. Raquel Urtasun is Uber ATG Chief Scientist and the Head of Uber ATG Toronto, and has rich experience in vision problems for the driving scene under diverse and changing scenarios, which is an important topic our workshop focuses on. She is also a Full Professor in the Department of Computer Science at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. Prior to this, she was an Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with the University of Chicago. She was also a visiting professor at ETH Zurich during the spring semester of 2010. She received her Bachelor degree from Universidad Publica de Navarra in 2000, her Ph.D. degree from the Computer Science department at Ecole Polytechnique Fédérale de Lausanne (EPFL) in 2006 and did her postdoc at MIT and UC Berkeley. She is a world leading expert in AI for self-driving cars. Her research interests include machine learning, computer vision, robotics, AI and remote sensing. Her lab was selected as an NVIDIA NVAIL lab. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, two NVIDIA Pioneer Research Awards, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded at CVPR in 2013 and 2017 respectively. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto's top influencers by Adweek magazine.

Peyman Milanfar

Google Research

Dr. Peyman Milanfar is a Principal Scientist / Director at Google Research, where he leads the Computational Imaging team. Dr. Peyman Milanfar has rich experience in inverse problems and super-resolution in imaging. Prior to this, he was a Professor of Electrical Engineering at University of California Santa Cruz, from 1999-2014. He was Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 he was on leave at Google-X, where he helped develop the imaging pipeline for Google Glass. Most recently, his team at Google developed the digital zoom pipeline for the Pixel phones, which includes the multi-frame super-resolution "Super Res Zoom" technology, and the RAISR upscaling algorithm. In addition, the Night Sight mode on Pixel 3 uses the Super Res technology (whether zoomed or not) for vivid shots in all lighting conditions. His other work includes the development of fast and robust methods for super-resolution, statistical analysis of performance limits for inverse problems in imaging, and the development of adaptive non-parametric techniques (kernel regression) for image and video processing. He holds several US patents in the field of image and video processing. Milanfar did his undergraduate studies at University of California, Berkeley, graduating in 1988, with a joint degree in Mathematics and Electrical Engineering. Milanfar received his Ph.D. in Electrical Engineering and Computer Sciences from MIT in 1993, with Alan S. Willsky. He was a research scientist at SRI International from 1994 to 1999 before moving to UC Santa Cruz.

Chelsea Finn

Stanford University, Google

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction, which is well aligned with special devices and platforms our workshop works on. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for scalable acquisition of nonlinear reward functions, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.

Stanley H. Chan

Purdue University

Dr. Stanley H. Chan is currently an associate professor in the School of Electrical and Computer Engineering and the Department of Statistics at Purdue University, West Lafayette, IN. Dr. Chan received the Ph.D. degree in Electrical Engineering and the M.A. degree in Mathematics from the University of California at San Diego, in 2011 and 2009, respectively, and the B.Eng. degree (with first class honor) in Electrical Engineering from the University of Hong Kong in 2007. Prior to joining Purdue, he was a postdoctoral research fellow at Harvard John A. Paulson School of Engineering and Applied Sciences from 2012 to 2014. His research interests include signal and image processing, applied statistics, and large-scale numerical optimization. In particular, he and his students made pioneering contributions to the model-based and data-driven computational imaging methods based on non-convex optimization.

Yunchao Wei

University of Technology Sydney

Dr. Yunchao Wei received the Ph.D. degree from Beijing Jiaotong University, China, in 2016. He is currently an Assistant Professor with the University of Technology Sydney, NSW, Australia. He was a Postdoctoral Researcher with the Beckman Institute, University of Illinois at Urbana–Champaign, IL, USA, from 2017 to 2019. His current research interests include computer vision and machine learning. Dr. Wei is an ARC Discovery Early Career Researcher Award Fellow from 2019 to 2021. He is a leading researcher in the field of semantic segmentation, human parsing, and weakly-supervised learning.

Bihan Wen

Nanyang Technological University, Singapore

Dr. Bihan Wen received the B.Eng. degree in Electrical and Electronic Engineering (EEE) from Nanyang Technological University (NTU), Singapore, in 2012, the MS and PhD degrees in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (UIUC), USA, in 2015 and 2018, respectively. He joined Nanyang Technological University in March 2019 and now is a Nanyang Assistant Professor. His research interests span areas of machine learning, computational imaging, computer vision, image and video processing, and big data applications. He is an elected member of the IEEE Computational Imaging (CI) Technical Committee. He regularly serves as the Area Chairs for ICIP and ICASSP, and the program committees or reviewers for top AI conferences. He co-organized the LCI workshop at ICCV 2019, and the MIPR 2019 as the Session Chairs. He was the recipient of the 2016 Yee Fellowship, and the 2012 Professional Engineers Board (PEB) Gold Medal.

Sifei Liu


Dr. Sifei Liu is a senior research scientist at Nvidia Research in Santa Clara, US. She received her PhD from the University of California Merced, department of EECS, where she was advised by Prof. Ming-Hsuan Yang. Before that, she obtained her master’s in ECE from University of Science and Technology of China (USTC), under the supervision of Prof. Stan.Z Li and Prof. Bin Li, and bachelor’s in control science from North China Electric Power University (NCEPU). Her research interests are in computer vision (low-level vision, semantic segmentation, 3D scene understanding), deep learning (graph-structured networks, self-supervised learning), and the combination of both. She also worked as an intern student in Baidu IDL, multimedia lab in CUHK, and NVIDIA research. She was the recipient of Baidu Graduate Fellowship in 2013, and NVIDIA Pioneering Research Award in 2017.

Shanghang Zhang

University of California, Berkeley

Dr. Shanghang Zhang is a postdoctoral research fellow in the Berkeley AI Research (BAIR) Lab, the Department of Electrical Engineering and Computer Sciences, UC Berkeley, working with Prof. Kurt Keutzer and Prof. Trevor Darrell. Her research interests cover deep learning, computer vision, and reinforcement learning, especially on machine learning with limited training data, including low-shot learning, domain adaptation, and meta-learning, which enables the learning system to automatically adapt to real-world variations and new environments. She was one of the “2018 Rising Stars in EECS” (a highly selective program launched at MIT in 2012, which has since been hosted at UC Berkeley, Carnegie Mellon, and Stanford annually). She has also been selected for the Qualcomm Innovation Fellowship (QInF) Finalist Award and Chiang Chen Overseas Graduate Fellowship. She received her Ph.D. from Carnegie Mellon University in 2018.