Contact

Assistant Professor

University of California, Santa Cruz

Email:

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 .

Pinned

[REAL@UCSC] Our group’s research results are disseminated at https://github.com/UCSC-REAL.
[Datasets] noisylabels.com 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 .

News

[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

[2023.01] We have 3 papers accepted to ICLR 2023. The papers covered topics on causal perspectives on long-term fairness, learning from noisy labels via representation regularization, and learning with prior shift. Details coming soon!
[2022.12] Machine Truth Serum (MLJ 2022) is accepted to the journal track of AAAI 2023.
[2022.09] We have 3 papers on fairness accepted to NeurIPS 2022. How do we certify fairness when distribution shifts from training? Our two papers Fairness Transferability Subject to Bounded Distribution Shift and Certifying Some Distributional Fairness with Subpopulation Decomposition (spotlight!) provide some answers to it. Our third acceptance Adaptive Data Debiasing Through Bounded Exploration provides a solution to debias a data sequentially with bounded exploration.
[2022.09] Our study of how crowds can forecast the publication of Covid-19 preprints is accepted to Royal Society Open Science. Our findings point to a quicker way to assess the credibility of time-sensitive preprints in parallel to the normal review process.
[2022.09] The extended version of Surrogate Scoring Rules (how to score crowd workers without ground truth?) is accepted to ACM TEAC.
[2022.07] Our forthcoming ECCV 2022 paper DuelGAN demonstrates another powerful application of peer loss in training a stable GAN.
[2022.06] Our paper Model Transferability with Responsive Decision Subjects is accepted to ICML 2022 workshop on New Frontiers in Adversarial Machine Learning as an ~~oral presentation~~ the best paper!
[2022.05] Our group has 6 papers accepted to ICML 2022 with one long presentation selection! Details here.
[2022.01] We have 2 papers accepted to ICLR 2022.
[2021.10] Our group has 4 papers accepted to NeurIPS 2021.

Recent awards

[2022.07] Our paper Model Transferability with Responsive Decision Subjects won the best paper award at ICML 2022 workshop on New Frontiers in Adversarial Machine Learning.
[2022.07] Our paper Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents is awarded the best paper award at AAMAS 2022 Workshop on Learning with Strategic Agents (LSA).
[2022.02] I have received the NSF CAREER award. Details: here. Thank you NSF!
[2021.08] Our paper Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels has won the best paper award at IJCAI 2021 workshop on Weakly Supervised Representation Learning. Congrats to Zhaowei and Yiwen (our former summer intern)!
[2021.07] Our paper Linear Classifiers that Encourage Constructive Adaptation has won the best paper award at ICML 2021 workshop on Algorithmic Recourse. Congrats to Yatong and Jialu!
[2021.01] Our proposal Fairness in Machine Learning with Human in the Loop is now awarded by the NSF FAI program! Thank you NSF & Amazon! As the lead institute, we will receive $1M to conduct a wide range of research on understanding the interaction of machine learning and human agents.

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.