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. I've spent time at Google AI as an intern, and Facebook AI as a Research Scientist. I have received Facebook Research Award, JPMorgan early-career faculty award, and was named Forbes 30 Under 30 in Science. I am currently a faculty fellow at the Madison Teaching and Learning Excellence (MTLE) program.
My broad research interests are in deep learning, a branch of machine learning. My time in both academia and industry has shaped my view and approach in research. My research develops algorithms and fundamental understandings to enable reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Research topics that I am currently focusing on include:
1/20/2022: Two papers accepted into ICLR 2022.
12/3/2021: Will serve as an area chair for ICML 2022.
12/1/2021: Two papers on OOD detection accepted into AAAI 2022.
11/1/2021: Received Facebook Research Award.
10/25/2021: Received gift funding from Google Brain.
10/22/2021: We released a comprehensive survey on generalized OOD detection.
10/18/2021: Received gift funding from Facebook.
9/28/2021: Three papers on out-of-distribution detection accepted to NeurIPS 2021. Congrats to the team!
7/22/2021: Paper on frequency-domain image translation (FDIT) accepted to ICCV 2021.
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 two ICML'21 workshops on uncertainty quantification.
2/28/2021: Two papers on OOD detection accepted to CVPR 2021 (including one oral presentation).
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. .