Biography

Biography

Ruichen Xu (Bill Xu) is a Ph.D. candidate in Computational Applied Mathematics at Stony Brook University, advised by Dr. Yuefan Deng. His research focuses on scientific machine learning, including neural operators for partial observations and inverse problems, predictive representation learning, physics-aware generative models, and LLM-guided optimization.

He received an M.S. in Mathematics from the Courant Institute at New York University, an M.S. in Statistics from the University of California, Davis, and a bachelor’s degree in Financial Mathematics from Beijing University of Chemical Technology.

Alongside research, he teaches applied mathematics and mentors AI4Science projects at Stony Brook University. His work is supported by reproducible training and evaluation pipelines for large-scale experiments.

Curriculum vitae (PDF) · Email

Education

  • Stony Brook University

    Ph.D., Computational Applied Mathematics

    GPA 4.00/4.00 · Stony Brook, New York

    2022–present
    Expected 2027

  • New York University · Courant Institute

    M.S., Mathematics

    GPA 4.00/4.00 · New York, New York

    2020–2022

  • University of California, Davis

    M.S., Statistics

    GPA 3.94/4.00 · Davis, California

    2019–2020

  • Beijing University of Chemical Technology

    Bachelor’s degree, Financial Mathematics
    Minor in Commercial Management

    Beijing, China

    2015–2019

Research & teaching experience

  • REU research mentor · AI3

    Stony Brook University. Mentored AI4Science projects, including experiment design, implementation, reproducible workflows, and presentations.

    June–July 2026

  • Instructor & teaching assistant

    Department of Applied Mathematics & Statistics, Stony Brook University.

    Courses and appointments

    2022–present

  • Recitation leader & grader

    Courant Institute, New York University.

    2020–2022

Reviewing & academic service

ICML 2026 Gold Reviewer · May 2026
Recognized by the program chairs for the quality of submitted reviews.

Conference reviewer

Journal reviewer

Workshop reviewer

Talks at IACS

  • AI for PDEs — IACS Student Seminar, 2025.
  • Diffusion4PDE — IACS lightning talk, 2025.
  • Active learning for neural operators — IACS lightning talk, 2024.