I am a 3rd-year CS PhD student at the University of Wisconsin - Madison. My research interests primarily revolve around the intersection of optimization and computational learning theory. I am very fortunate to be advised by Prof. Jelena Diakonikolas. Additionally, I have the privilege of collaborating with Prof. Ilias Diakonikolas and other amazing collaborators. Prior to joining Madison, I completed my B.S. degree in Mathematics at Shandong University, where I had the opportunity to be advised by Prof. Guanghui Wang. I also worked with Prof. Congying Han at UCAS during my undergraduate study.

Publication

• Robustly Learning Single-Index Models via Alignment Sharpness

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

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

  ($\alpha\beta$) Ilias Diakonikolas, Jelena Diakonikolas, Daniel M Kane, Puqian Wang, Nikos Zarifis, NeurIPS, 2023, arxiv

• Information-Computation Tradeoffs for Learning Margin Halfspces with Random Classification Noise

  ($\alpha\beta$) Ilias Diakonikolas, Jelena Diakonikolas, Daniel M Kane, Puqian Wang, Nikos Zarifis, COLT, 2023, arxiv

• Robustly Learning a Single Neuron via Sharpness

  Puqian Wang* , Nikos Zarifis* , Ilias Diakonikolas, Jelena Diakonikolas, ICML, 2023, Short Presentation, arxiv

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

  Jelena Diakonikolas, Puqian Wang, SIAM Journal on Optimization, 2022, arxiv

Teaching

TA@UW Madison

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