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Sharon Y. Li

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I am currently a postdoc researcher in the CS department at Stanford University, working with Chris Ré. I will be joining the Computer Sciences Department at University of Wisconsin Madison as an assistant professor, starting in Fall 2020. Previously, I completed my PhD from Cornell University in 2017, where I was advised by John E. Hopcroft. My thesis committee members are Kilian Q. Weinberger and Thorsten Joachims. I've spent time at Google AI twice as an intern, and Facebook AI as a Research Scientist.

My principal research interests are in machine learning and representation learning. I am excited about developing computational methods that can extract meaningful representations from masses of complex data. In particular, my recent research focuses on developing machine learning models that (1) reduce human supervision during training , and (2) enhance reliability and robustness during deployment in the wild.

[Prospective students]: I am looking for highly motivated students to join my group in the fall! The admission decision at CS department is committee based. For admitted MS/PhD students, please send me an email with your CV if you are interested in working with me.


3/23/2020: Our latest work on robust out-of-distribution detection is released.
3/14/2020: I will be co-organizing 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 data efficient learning.
12/3/2019: Honored and thrilled to be featured in Forbes 30 Under 30 list in Science.
4/28/2019: Paper on large-scale weakly supervised learning for e-commerce search accepted to KDD 2019.
3/8/2019: Honored to be featured in 30 Under 30 Leading Women in AI.
2/24/2019: Paper on adversarial defense using KNN accepted as an oral presentation at CVPR 2019.
2/24/2019: I will be organizing ICML'19 workshop on Uncertainty and Robustness in Deep Learning.
12/13/2018: I will be giving a talk at Deep Learning Summit San Francisco in Jan 2019.
10/1/2018: Gave an invited talk at Microsoft Research AI in Redmond, WA.
9/27/2018: Served on a panel of Women in Research at Facebook.
9/26/2018: Gave a talk at Grace Hopper Celebration (GHC) Artificial Intelligence track in Houston, TX.
7/3/2018: Paper on exploring the limits of weakly supervised pretraining accepted into ECCV 2018.
5/12/2018: Paper on understanding the loss surface of neural networks accepted into ICML 2018.
4/3/2018: Received CVPR'18 Doctoral Consortium travel award.
3/13/2018: Served on a panel at Facebook's Women in Research Lean In (WiRL) Circle.
1/29/2018: Paper on detecting out-of-distribution examples in neural networks accepted into ICLR 2018.
11/25/2017: Received ACM-W Scholarship in 2017.
10/16/2017: I will be presenting at Women in Machine Learning (WiML) workshop in December this year.
10/2017: Gave a talk at Grace Hopper Celebration (GHC) Artificial Intelligence track in Orlando, FL.
8/5/2017: Selected as one of the Rising Stars in EECS 2017 by Stanford University.
6/6/2017: Paper accepted for publication in Transactions on Knowledge Discovery from Data (TKDD).
3/12/2017: Received ICLR 2017 Student Travel Award.
2/27/2017: Paper on StackedGAN has been accepted into CVPR 2017.
2/6/2017: Paper on Snapshot Ensembles has been accepted into ICLR 2017.

I travel and occasionally take photos. Here is my pictorial Travel Memo.