I am a Ph.D. candidate in the Department of Statistics at the University of Washington. I am fortunate to be advised by Zaid Harchaoui and Soumik Pal. Prior to that, I received my B.S. in mathematics and applied mathematics at Tsinghua University.
I am broadly interested in safe and interpretable statistical machine learning with applications to natural language processing. I am also excited about optimal transport and its applications to statistical inference and machine learning.
Contact me at liu16 [at] uw [dot] edu.
- 12/02/2022: Presentation at NeurIPS 2022 Workshop on Score-Based Methods on likelihood score under self-concordance.
- 10/21/2022: Presentation at IFDS weekly seminar on confidence set under self-concordance.
- 09/22/2022: I defended!
- 10/09/2022: Our paper on meta-learning with heterogeneous covariate spaces has been accepted (with minor revision) at TMLR.
- 09/29/2022: Presentation at SIAM MDS 2022 on statistical analysis of divergence frontiers.
- 08/25/2022: Presentation at COMPSTAT 2022 on large-scale entropy regularized optimal transport independence criterion.
- 08/08/2022: Presentation at JSM 2022 on independence testing with entropy regularized optimal transport.
- 05/14/2022: Our paper on orthogonal statistical learning (or double ML) has been accepted at COLT 2022.
- 03/30/2022: Oral presentation at AISTATS 2022 on entropy regularized optimal transport independence criterion.
- 12/13/2021: Our paper on discrete Schrödinger bridges and two-sample testing has received the Best Paper Award at NeurIPS 2021 OTML Workshop.
- 09/28/2021: Our paper on statistical analysis of divergence frontiers has been accepted at NeurIPS 2021.