Assistant Professor

Computer Science and Engineering

University of California, Santa Cruz


Office: E2-341A

About Me

I’m an Assistant Professor of Computer Science and Engineering at UC Santa Cruz. My research interests are crowdsourcing and algorithmic fairness, both in the context of machine learning. The central question associated with my work is learning from dynamic and noisy data.

Previously I was holding a postdoctoral fellow position at Harvard University. I have a Ph.D. from the University of Michigan, Ann Arbor and a B.Sc. from Shanghai Jiao Tong University, China.

My research is generously supported by the National Science Foundation (by their CORE, FAI, CAREER and TRIPOS programs), Office of Naval Research (Basic AI Research), Amazon, UC Santa Cruz and CROSS. I was partially supported by the DARPA SCORE program.

ByteDance AI Lab I am currently on leave from UCSC and leading the Machine Learning Fairness team at ByteDance. We are looking to hire full-time research scientists, see here and here. Our UK team is also hiring interns, see here. Our Mountain View office will be looking for interns for 2023 summer too - stay tuned! If you’d like to discuss any of these career opportunities, I can be reached at .


  • [REAL@UCSC] Our group’s research results are disseminated at

  • [Datasets] is online! We have also concluded the 1st Learning and Mining with Noisy Labels Challenge (LMNL). Congrats to the winning teams!

  • [Join us] I am looking for highly-motivated postdocs, phd students, visitors, and interns to work with us on weakly-supervised learning and algorithmic fairness. If you are interested, please email me at .


  • [2022.12] Serving FAccT 2023 (AC), ICML 2023 (AC), UAI 2023 (AC) and IJCAI 2023 (SPC).
  • [2022.12] Together with Eugene Vorobeychik, we deliver a tutorial on “Strategic Machine Learning” at WINE 2022.
  • [2022.10] Together with collaborators from RIEKN (Masashi Sugiyama, Gang Niu), U Sydney (Tongliang Liu) and HKBU (Bo Han), we delivered a tutorial on Learning and Mining from Noisy Labels.
  • [2022.05] I joined Amazon Search Science and AI as an Amazon Visiting Academic (part-time). I am helping build the next-generation high-quality and budget-efficient human label platform for AI.

Recent papers

Recent awards

Invited talks

  • [2022.11] Agency Bias in Machine Learning@USC ML Seminar.

  • [2022.11] Agency Bias in Machine Learning@UW ECE Colloquium.

  • [2022.11] Fairness in Machine Learning when Agents Respond@Brandeis University CS Colloquium.

  • [2022.11] Learning from Noisy Labels without Knowing Noise Rates@UMich CSP Seminar.

  • [2022.10] Learning from Noisy Labels without Knowing Noise Rates@IDEAL, Northwestern

  • [2022.08] The Matthew Effect When Learning from Weak Supervisions@RIKEN.

  • [2022.06] Consequential Machine Learning@ByteDance AI Lab.

  • [2022.05] Learning with Noisy Labels@Google Research NYC.

  • [2022.05] Learning with Noisy Labels@Amazon Search.

  • [2022.02] Learning with Noisy Labels@Marvell.

  • [2022.02] The Matthew Effect When Learning from Weak Supervisions@Oracle Lab.

  • [2021.11] Revisiting Dynamics in Strategic Machine Learning@StratML@NeurIPS 2021.

  • [2021.08] Learning from Noisy Labels: Some Lessons and Challenges@IJCAI 2021 workshop on Weakly Supervised Representation Learning.