I am an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Previously I spent a wonderful year as a postdoc researcher in the Computer Science department at Stanford University, working with Chris Ré. I completed my PhD from Cornell University in 2017, where I was fortunate to be advised by John E. Hopcroft. My thesis committee members are Kilian Q. Weinberger and Thorsten Joachims. I've spent time at Google AI as an intern, and Facebook AI as a Research Scientist. I was named Forbes 30 Under 30 in Science, 30 Under 30 Rising Stars in AI, and JPMorgan early-career outstanding faculty. I am currently a faculty fellow at the Madison Teaching and Learning Excellence (MTLE) program.
My broad research interests are in deep learning and machine learning. My time in both academia and industry has shaped my view and approach in research. The goal of my research is to enable transformative algorithms and practices towards reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Our works explore, understand, and mitigate the many challenges where failure modes can naturally occur in deploying machine learning models in the open world. Research topics that I am currently focusing on include:
[Openings]: I am looking for highly motivated Ph.D. students to join my lab in the Fall 2021. The admission decision at UW-Madison is committee-based. For questions regarding the application process, please read the FAQ.
7/22/2021: Paper on frequency-domain image translation (FDIT) accepted to ICCV, congrats to Mu!
7/15/2021: Received Madison Teaching and Learning Excellence Fellowship.
6/18/2021: Paper on outlier mining for out-of-distribution detection is accepted to ECML 2021.
6/4/2021: Received JPMorgan Chase early career outstanding faculty award.
6/2021: Received American Family Funding Intiative Awards.
4/4/2021: Will be co-organizing ICML'21 workshops on uncertainty quantification.
2/28/2021: Two papers on OOD detection accepted to CVPR 2021 (including one oral presentation). Congrats to Rui, Ziqian and Sreya!
1/12/2021: Paper on Model Patching (for handling subgroup shift) accepted to ICLR 2021.
10/28/2020: Will serve as a mentor at the Women in Machine Learning (WiML) workshop at NeurIPS 2020.
9/25/2020: Paper on Energy-based Out-of-distribution Detection accepted to NeurIPS 2020.
8/17/2020: Joined the CS Department at UW-Madison as an Assistant Professor. Here is a featured interview article.
05/19/2020: Gave an invited talk on Out-of-distribution Uncertainty Estimation and Robustness in Open-World Machine Learning at Air Force Research Laboratory’s Workshop.
4/23/2020: Selected Young Researcher to participate in the 8th Heidelberg Laureate Forum.
3/14/2020: Will serve as Program Chair for ICML'20 workshop on Uncertainty and Robustness in Deep Learning.
2/26/2020: Wrote a blog series on automating the art of data augmentation, featuring latest works on the practice, theory and new direction of data augmentation.
12/3/2019: My talk is listed as one of 30 Influential AI Presentations in 2019.
12/3/2019: Honored and thrilled to be featured in Forbes 30 Under 30 list in Science.
3/8/2019: Honored to be featured in 30 Under 30 Leading Women in AI.
2/24/2019: Paper on KNN-based robustness accepted as an oral presentation at CVPR 2019.
2/24/2019: Will serve as Program Chair for ICML'19 workshop on Uncertainty and Robustness in Deep Learning.
1/2019: Gave a talk at Deep Learning Summit San Francisco .
5/2/2018: Research featured in a TechCrunch and Wired article.
10/1/2018: Gave an invited talk at Microsoft Research AI in Redmond, WA.
9/26/2018: Gave a talk at Grace Hopper Celebration (GHC) Artificial Intelligence track in Houston, TX.
1/29/2018: Paper on ODIN for detecting out-of-distribution examples in neural networks accepted into ICLR 2018. .
I travel and occasionally take photos. Here is my pictorial Travel Memo. This is the treasure I shoot with.