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.

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

[2022.05] I joined Amazon Search Science and AI as an Amazon Visiting Academic. I am helping build the next-generation high-quality and budget-efficient human label platform for AI.
[2022.05] Serving NeurIPS 2022 as the Area Chair.
[2021.12] I’m serving FAccT 2022 and UAI 2022 as the Area Chair.

Recent papers

[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 award paper!
[2022.05] Our group has 6 papers accepted to ICML 2022 with one long presentation selection! Details here.
[2022.05] Our work Machine Truth Serum is accepted to Machine Learning special issue on weakly supervised learning.
[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. 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 talk

[2022.06] Invited talk @ByteDance AI Lab.
[2022.05] Invited talk @Google Research NYC.
[2022.05] Invited talk @Amazon Search.
[2022.02] Invited talk @Marvell.
[2022.02] Invited talk @Oracle Lab.
[2021.11] Invited talk @StratML@NeurIPS 2021.
[2021.08] Invited talk @IJCAI 2021 workshop on Weakly Supervised Representation Learning.