Ruichen Jiang

Postdoctoral Researcher, Google Research E-mail: ruichen [dot] opt [at] gmail [dot] com

About me

I am a postdoctoral researcher at Google Research in New York City. My research develops optimization algorithms using ideas from online learning, with current interests in convex and nonconvex optimization. I received my Ph.D. in ECE from UT Austin, where I was advised by Prof. Aryan Mokhtari.

During my Ph.D., I worked as a Student Researcher at Google Research and as an Applied Scientist Intern at Amazon Web Services. Before UT, I received a B.E. in Electronic Engineering and a B.S. in Mathematics both from Tsinghua University in 2020.

Selected Works

• Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization
  (α-β order) Ruichen Jiang, Zakaria Mhammedi, Mehryar Mohri, and Aryan Mokhtari
  COLT 2026

• Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods
  (α-β order) Ruichen Jiang, Aryan Mokhtari, and Francisco Patitucci
  STOC 2025

• Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization
  Ruichen Jiang and Aryan Mokhtari
  NeurIPS 2023 (Spotlight)

• Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence
  Ruichen Jiang, Qiujiang Jin, and Aryan Mokhtari
  COLT 2023

• A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem
  Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, and Erfan Yazdandoost Hamedani
  AISTATS 2023

• Generalized Optimistic Methods for Convex-Concave Saddle Point Problems
  Ruichen Jiang and Aryan Mokhtari
  SIAM Journal on Optimization, 2025

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