Yang Liu (he/him) is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz (2019 - present). He also leads the machine learning fairness team at ByteDance AI Lab. He was previously a postdoctoral fellow at Harvard University (2016 - 2018). In 2015, he received his Ph.D. degree from the Department of EECS at the University of Michigan, Ann Arbor. His research focuses on developing fair and robust machine learning algorithms to tackle the challenges of biased and shifting data. He is a recipient of the NSF CAREER Award. He has been selected to participate in several high-profile projects, including NSF-Amazon Fairness in AI, DARPA SCORE, and IARPA HFC. His research has observed deployments with FICO and Amazon. His recent work has been recognized with four best paper awards at relevant workshops.

He has been an active contributor to the community of weakly supervised learning. He has co-taught a tutorial on ``Learning and Mining from Noisy Labels" at CIKM 2022. He organized the first competition on "Learning and Mining with Noisy Labels" at IJCAI 2022. He has served on the advising board for a workshop series on ``Weakly Supervised Representation Learning". 

Zhaowei Zhu (he/him) is a final-year Ph.D. candidate in Computer Science and Engineering at UC Santa Cruz. He received the M.S. degree from ShanghaiTech University, Shanghai, China, in 2019 and the B.S. degree from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2016. He has general interests in responsible and trustworthy machine learning, including weakly-supervised learning (e.g., label noise, semi-supervised learning, self-supervised learning), machine learning fairness, and federated learning. His recent work HOC won the best paper award in IJCAI 2021 Workshop on Weakly Supervised Representation Learning. He co-organized the first competition on  "Learning and Mining with Noisy Labels" at IJCAI 2022.

Jiaheng Wei (he/him) is a Ph.D. candidate in Computer Science and Engineering at UC Santa Cruz (2019 - present). He was a student researcher at Google Research, Brain Team (2022). He received an M.S. degree (Data Science) at Brown University and B.S. degree in Honors Science (Mathematics and Applied Mathematics) and Honors Youth (Gifted Young) from Xi’an Jiaotong University. His research interests mainly include robust learning under real-world constraints (i.e., label noise in human-generated data, class-imbalanced learning, group distributional robustness, and fairness). His recent work NLS is selected for a long presentation in ICML 2022. He co-organized the first competition on  "Learning and Mining with Noisy Labels" at IJCAI 2022.

Zhaowei Zhu (he/him) is a Ph.D. student of Computer Science and Engineering at UC Santa Cruz. He received an M.S. degree from the University of Chinese Academy of Sciences, Shanghai, China, and a B.S. degree from Jilin University, Jilin, China. He was a researcher in the Youtu lab, Tencent. He has broader research interests in weakly supervised learning, interpretable machine learning, and network structure design. He was awarded Technology Breakthrough Award in Tencent and invited to give talks about his research in top tech media and universities. He co-organized the first competition on  "Learning and Mining with Noisy Labels" at IJCAI 2022.