Bio

I am a fifth-year Ph.D. student at Department of Statistics, Columbia University and honored to have Professor Yang Feng and Professor Zhiliang Ying as my advisors. I was born in Huainan, which is a small city of China famous for its long history and the delicious tofu there. Prior to Columbia, I obtained my B.S. degree in statistics in 2019 at University of Science and Technology of China (USTC) and was honored to be supervised by Professor Weiping Zhang.

My research interests are mainly related to statistical machine learning, including classification, high-dimensional statistics and variable selection. Specially, I am interested in understanding multi-task and transfer learning problems from statistics perspective. Besides research, I love photography, running, traveling, and reading (especially Sci-Fi and history books). I also do some daily muscle training.

My Google Scholar page

News


2024

  • May 1: Our paper "Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms" (joint work with Haolei Weng and Yang Feng) was accepted at ICML 2024.

  • 2023

  • Oct 24: I will attend NUS-IMS Young Mathematical Scientist Forum -- Statistics and Data Science and give a talk during Nov 20-23, 2023 in Singapore.
  • Jun 10: I'm working as an applied scientist intern with the PayStation Intelligence (PI) team at Amazon this summer.
  • Jun 10: I'm thrilled to receive the NESS Student Research Award sponsored by MassMutual at New England Statistics Symposium (NESS) 2023.
  • Apr 15: I will present my recent work Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness in a 20-min talk at 2023 Berkeley–Columbia Meeting in Engineering and Statistics on Apr 20 afternoon. A 15-min version will be presented at 2023 Minghui Conference held by Columbia stats department on Apr 28.
  • Apr 2: Our new paper Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness came out! I will give a poster presentation and a lightning talk about this project at Columbia Statistical Machine Learning Symposium on Apr 7.
  • Apr 2: I will present a poster at the conference on "Statistical Foundations of Data Science and their Applications" about my paper Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models on May 8.
  • Mar 31: I will chair the session "Statistical Learning Theory" on Aug 10, 2023, at JSM 2023. There will be wonderful talks given by seven different speakers. Welcome to join us!
  • Mar 30: I am honored to receive 2023 IMS Hannan Graduate Student Travel Award.

  • 2022

  • Oct 8: I presented my recent paper Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models at AISC 2022.
  • Oct 1: I'm happy to receive the student travel award provided by ICSDS 2022. I will present my recent paper Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models on Dec 16, 2022 in Florence, Italy.
  • Jul 5: I'm currently teaching [S1201: Calculus-based Introduction to Statistics] at Columbia. All materials can be found at the course website.

  • 2021

  • Nov 8: My new work on Neyman-Pearson multi-class classification with Yang has been posted on arXiv.
  • May 3: I'll help run the student seminar with Arnab at Columbia stats department during 2021-2022 acedamic year.
  • May 1: Our new package "glmtrans" has been launched on CRAN. The related paper is coming!
  • Mar 22: I am happy to present my work with Yang, Random Subspace Ensemble Classification (RaSE), at 2021 Minghui Conference held by Columbia stats department.
  • Mar 13: I will give a poster presentation about my recent work with Yang, Random Subspace Ensemble Classification (RaSE), at 2021 WISC Graduate Research Symposium on Mar 26 (online). The poster can be found here: [poster PDF]. (postponed to April 2)
  • Service

    I have reviewed papers for Journal of the Royal Statistical Society Series B, Biometrika, Technometrics, Bernoulli, AISTATS 2023, ICLR 2023, Statistical Papers, Journal of the American Statistical Association, Journal of Machine Learning Research, Statistics and Computing, Neural Networks, and FODS 2020.

    Contact

    Email: [email protected]

    Address: Room 901, School of Social Work Building, 1255 Amsterdam Ave, New York, NY 10027