Mo Zhou 周墨 Postdoc Institute for Foundations of Data Science (IFDS) University of Washington Email: mozhou17 at cs dot washington dot edu Google scholar

I am a postdoc at the Institute for Foundations of Data Science (IFDS), University of Washington, where I'm hosted by Simon Du and Maryam Fazel at the Paul G. Allen School of Computer Science & Engineering. Previously, I received my Ph.D. in Computer Science from Duke University, where I was fortunate to be advised by Rong Ge. Before coming to Duke, I received B.S. in Statistics from Peking University.

In summer 2023, I worked with Prof. Tengyu Ma at Stanford. In summer 2022, I was an applied science intern at AWS AI. In summer 2018, I was an intern in Industrial and Systems Engineering (ISyE), Georgia Tech, working with Prof. Tuo Zhao.

My research interests are in optimization and theoretical machine learning. Recently, I am particularly interested in deep learning theory.

Publications and Preprints

* denotes equal contribution, (α-β order) denotes alphabetical ordering

• Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures
  Mo Zhou*, Weihang Xu*, Maryam Fazel, Simon S. Du.
  arxiv preprint, 2025.
  


• How Does Gradient Descent Learn Features -- A Local Analysis for Regularized Two-Layer Neural Networks
  Mo Zhou, Rong Ge.
  Conference on Neural Information Processing Systems (NeurIPS), 2024.
  Short version appeared at NeurIPS Mathematics of Modern Machine Learning (M3L) workshop, 2023.


• Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
  Mo Zhou, Rong Ge.
  International Conference on Machine Learning (ICML), 2023.


• Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
  Xingyu Zhu*, Zixuan Wang*, Xiang Wang, Mo Zhou, Rong Ge.
  International Conference on Learning Representations (ICLR), 2023


• Depth-Separation with Multilayer Mean-Field Networks.
  Yunwei Ren, Mo Zhou, Rong Ge.
  International Conference on Learning Representations (ICLR), 2023. Notable-top-25%.


• Plateau in Monotonic Linear Interpolation–A “Biased” View of Loss Landscape for Deep Networks
  Xiang Wang, Annie N Wang, Mo Zhou, Rong Ge.
  International Conference on Learning Representations (ICLR), 2023


• Understanding The Robustness of Self-supervised Learning Through Topic Modeling
  Zeping Luo*, Shiyou Wu*, Cindy Weng*, Mo Zhou, Rong Ge
  International Conference on Learning Representations (ICLR), 2023


• Understanding Deflation Process in Over-parametrized Tensor Decomposition
  (α-β order) Rong Ge*, Yunwei Ren*, Xiang Wang*, Mo Zhou*
  Conference on Neural Information Processing Systems (NeurIPS), 2021.


• A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
  Mo Zhou, Rong Ge, Chi Jin
  Conference on Learning Theory (COLT), 2021.


• Towards Understanding the Importance of Shortcut Connections in Residual Networks
  Tianyi Liu*, Minshuo Chen*, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
  Conference on Neural Information Processing Systems (NeurIPS), 2019.


• Towards Understanding the Importance of Noise in Training Neural Networks
  Mo Zhou*, Tianyi Liu*, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao
  International Conference on Machine Learning (ICML), 2019. Long Talk.

Presentations

• A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
  COLT 2021, Aug. 2021
  Theory of Overparameterized Machine Learning (TOPML) 2021, Apr. 2021
  Duke Deep Learning Reading Group, Apr. 2021
  THEORINET Journal Club/MODL Reading Group, Feb. 2021
  

Teaching

• CPS590.04 Machine Learning Algorithms, 2021 Spring. TA


• CPS330 Design and Analysis of Algorithms, 2020 Fall. TA


• CPS330 Design and Analysis of Algorithms, 2020 Spring. TA

Services

• Reviewer for ICML, ICLR, NeurIPS, JMLR, Mathematical Programming, STOC.

Education

• Duke University, 2019 - 2024
  Ph.D. in Computer Science


• Peking University, 2015 - 2019
  B.S. in Statistics

Last update: Jun. 2025