Oct 11 Afternoon, 2021 Virtual
Invited speaker 1: Haibin Ling, Professor, Stony Brook University (Confirmed)
Biography: Haibin Ling received B.S. and M.S. from Peking University in 1997 and 2000, respectively, and Ph.D. from University of Maryland in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia; from 2006 to 2007, he worked as a postdoctoral scientist at UCLA; from 2007- 2008, he worked for Siemens Corporate Research as a research scientist; and from 2008 to 2019, he was a faculty member of the Department of Computer Sciences for Temple University. In fall 2019, he joined the Department of Computer Science of Stony Brook University, where he is now a SUNY Empire Innovation Professor. His research interests include computer vision, augmented reality, medical image analysis, visual privacy protection, and human computer interaction. He received Best Student Paper Award of ACM UIST in 2003 and NSF CAREER Award in 2014. He serves as associate editors for IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Pattern Recognition (PR), and Computer Vision and Image Understanding (CVIU). He also serves as Area Chairs for CVPR 2014, 2016, 2019 and 2020.
Invited speaker 2: Shanghang Zhang, Postdoc research fellow, Berkeley AI Research Lab (BAIR), EECS, UC Berkeley. (Confirmed)
Biography: Shanghang Zhang is a postdoc research fellow in the Berkeley AI Research Lab (BAIR), EECS, UC Berkeley. She received her Ph.D. from Carnegie Mellon University in 2018, and her Master from Peking University. Her research covers multimedia intelligence and machine learning, with a focus on label efficient learning, including low-shot learning, domain adaptation, and self-supervised learning, which enables the learning system to automatically adapt to new domains, tasks, and environments, as reflected in her many publications on top-tier journals and conference proceedings, including NeurIPS, ICLR, ACM MM, TNNLS, TMM, CVPR, ICCV, and AAAI. She has also been the author and editor of book “Deep Reinforcement Learning: Fundamentals, Research and Applications” published by Springer Nature. Her recent work “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting” has received the AAAI 2021 Best Paper Award. She was one of the “2018 Rising Stars in EECS, USA”. She has also been selected for the Adobe Academic Collaboration Fund, Qualcomm Innovation Fellowship (QInF) Finalist Award, and Chiang Chen Overseas Graduate Fellowship. Her research outcomes have been successfully productized into real-world machine learning solutions and filed 5 patents. Dr. Zhang has been the chief organizer of several workshops on ICML/NeurIPS, and the special issue on ICMR.
Anticipated target audience as well as expected number of attendees
This workshop targets at researchers and students who are working on object perception, including detection, tracking, motion trend prediction, object re-identification, object recognition/verification, and other related areas. In total 150 attendees are reasonably expected.