I am a fifth-year PhD candidate in Computer Sciences at the University of Wisconsin–Madison. My research lies at the intersection of optimization and machine learning theory. I study the geometric and statistical structures that make challenging learning and optimization problems efficiently solvable, and design algorithms that exploit these structures. I am very fortunate to be advised by Prof. Jelena Diakonikolas and to have collaborated with Prof. Ilias Diakonikolas and many other wonderful researchers. Before coming to Madison, I earned my B.S. in Mathematics from Shandong University.

I am on the job market for fall 2027 -- seeking postdoctoral and research positions in academia or industry!

Publications

1. Efficient Optimization on the Euclidean Sphere: Riemannian Gradient Alignment as a Unifying Principle

   Puqian Wang, Nikos Zarifis, Jelena Diakonikolas

   In submission · 2026 arXiv

2. ARO: A New Lens on Matrix Optimization for Large Models

   Wenbo Gong, Javier Zazo, Qijun Luo, Puqian Wang, James Hensman, Chao Ma

   In submission · 2026 arXiv

3. Robustly Learning Monotone Single-Index Models

   Puqian Wang^*, Nikos Zarifis^*, Ilias Diakonikolas, Jelena Diakonikolas

   NeurIPS 2025 arXiv

4. Robustly Learning Monotone Generalized Linear Models via Data Augmentation

   Nikos Zarifis^*, Puqian Wang^*, Ilias Diakonikolas, Jelena Diakonikolas

   COLT 2025 arXiv

5. Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models

   Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

   NeurIPS 2024 arXiv

6. Robustly Learning Single-Index Models via Alignment Sharpness

   Nikos Zarifis^*, Puqian Wang^*, Ilias Diakonikolas, Jelena Diakonikolas

   ICML 2024 arXiv

7. Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise

   Ilias Diakonikolas, Jelena Diakonikolas, Daniel Kane, Puqian Wang, Nikos Zarifis (alphabetical)

   NeurIPS 2023 arXiv

8. Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise

   Ilias Diakonikolas, Jelena Diakonikolas, Puqian Wang, Nikos Zarifis (alphabetical)

   COLT 2023 arXiv

9. Robustly Learning a Single Neuron via Sharpness

   Puqian Wang^*, Nikos Zarifis^*, Ilias Diakonikolas, Jelena Diakonikolas

   ICML 2023, Oral Presentation arXiv

10. Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization

    Jelena Diakonikolas, Puqian Wang (alphabetical)

    SIAM Journal on Optimization 2022 arXiv

^* Equal contribution.

Talks

• When Is Nonconvexity Not a Problem -- Benign Landscapes and Hidden Curvature in Learning on the Sphere, MOPTA, August 2026, Lehigh University
• Robustly Learning Single-Index Models, INFORMS Annual Meeting, October 2025, Atlanta, GA
• Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models, IFDS Forum, October 2024, UW-Madison
• Robustly Learning a Single Neuron via Alignment Sharpness, 60th Allerton Conference, September 2024, Urbana-Champaign, IL
• Robustly Learning a Single Neuron via Alignment Sharpness, International Symposium on Mathematical Programming, July 2024, Montreal, Canada

Experience

Research Intern, Microsoft Research Cambridge, July-September 2025.

Teaching

Teaching Assistant, UW-Madison

• Fall 2021 CS577 Introduction to Algorithms
• Fall 2022 CS726 Nonlinear Optimization I