hongjie dot chen at inf dot ethz dot ch

I am a PhD student in the Department of Computer Science at ETH Zurich, advised by David Steurer.

I am broadly interested in trustworthy machine learning and theoretical computer science, with a focus on high-dimensional statistical estimation under (differential) privacy and robustness considerations.

Education

  • 2022-now: ETH Zurich, Ph.D. Computer Science
  • 2019-2021: ETH Zurich, M.S. Computer Science
  • 2015-2019: Shanghai Jiao Tong University, B.E. Computer Science

Publications

  • On efficient robust regression with subquadratic samples with Deeksha Adil, Jaroslaw Blasiok, Deepak Narayanan. COLT 2026.

  • Fast and optimal algorithms for private hypothesis selection with Hilal Asi. ICML 2026.

  • Improved robust estimation for Erdős-Rényi graphs: the sparse regime and optimal breakdown point with Jingqiu Ding, Yiding Hua, Stefan Tiegel. NeurIPS 2025. arxiv

  • Outlier-robust mean estimation near the breakdown point via sum-of-squares with Deepak Narayanan, David Steurer. SODA 2025. arxiv proceedings

  • Private edge density estimation for random graphs: optimal, efficient and robust with Jingqiu Ding, Yiding Hua, David Steurer. NeurIPS 2024 (spotlight). arxiv proceedings

  • Private graphon estimation via sum-of-squares with Jingqiu Ding, Tommaso d’Orsi, Yiding Hua, David Steurer, Chih-Hung Liu. STOC 2024. arxiv

  • Private estimation algorithms for stochastic block models and mixture models with Vincent Cohen-Addad, Tommaso d’Orsi, Alessandro Epasto, Jacob Imola, David Steurer, Stefan Tiegel. NeurIPS 2023 (spotlight). arxiv

  • On the well-spread property and its relation to linear regression with Tommaso d’Orsi. COLT 2022. arxiv

Teaching