Mitsubishi Electric Research Laboratories (MERL)
Dr. Kuan-Chuan Peng is a Research Scientist at Mitsubishi Electric Research Labs (MERL). Before joining MERL, he was a Staff Scientist at Siemens Corporate Technology. He received his Ph.D. degree in Electrical and Computer Engineering from Cornell University in 2016. He received his Bachelor's degree in Electrical Engineering and an M.S. degree in Computer Science from National Taiwan University in 2009 and 2012 respectively. His research interests include incremental learning, developing practical solutions given biased or scarce data, and fundamental computer vision and machine learning problems. His works have appeared in PAMI, CVPR, ICCV, ECCV, etc. He co-organized the following workshops: (1) 2020 Workshop on Fair, Data-Efficient and Trusted Computer Vision in conjunction with CVPR 2020. (2) 2020 Workshop on Vision Applications and Solutions to Biased or Scarce Data in conjunction with WACV 2020. (3) 2018 and 2019 Vision with Biased or Scarce Data workshop in conjunction with CVPR in 2018 and 2019.
Worcester Polytechnic Institute (WPI)
Dr. Ziming Zhang is an assistant professor at Worcester Polytechnic Institute. Before joining WPI he was a research scientist at Mitsubishi Electric Research Laboratories (MERL) in 2016-2019. Prior to that, he was a research assistant professor at Boston University. Dr. Zhang received his PhD in 2013 from Oxford Brookes University, UK, under the supervision of Prof. Philip H. S. Torr (now in the University of Oxford). His research areas lie in computer vision and machine learning, especially in object recognition/detection, data-efficient learning (e.g. zero-shot learning) and applications (e.g. person re-identification), deep learning, optimization. His works have appeared in PAMI, CVPR, ICCV, ECCV, NIPS. He serves as a review/PC member for top conferences (e.g. CVPR, ICCV, NIPS, ICML, ICLR, AAAI, AISTATS, IJCAI) and journals (e.g. PAMI, IJCV, JMLR). He won the R&D100 Award 2018. He co-organized the 2020 Workshop on Vision Applications and Solutions to Biased or Scarce Data in conjunction with WACV 2020.