Ruichen Xu 徐瑞辰

普通话 · Mandarin (Pinyin): Xú Ruìchén
粤语 · Cantonese (Jyutping): ceoi4 seoi6 san4

Ruichen Xu

Ph.D. Candidate

Computational Applied Mathematics
Department of Applied Mathematics & Statistics
Stony Brook University

Also known as Bill Xu
ruichen.xu@stonybrook.edu

Introduction

I am a Ph.D. candidate in Computational Applied Mathematics at Stony Brook University, advised by Dr. Yuefan Deng. My research lies at the intersection of machine learning and scientific computing, with a focus on AI for Science.

I study how learning systems can use incomplete observations and physical structure to solve scientific problems. My work spans neural operators, predictive representation learning, physics-aware generative models, and LLM-guided optimization. I also teach applied mathematics and mentor student research at Stony Brook.

Research

  • Learning from partial observations. Neural operators and benchmarks for reconstructing PDE fields from sparse and irregular measurements. PartialObs–PDEBench · Discretization mismatch
  • Predictive representations for scientific data. Joint-embedding learning for PDE inference and multi-resolution graph representations. JENO · HP-JEPA
  • Physics-aware generation and optimization. Diffusion, simulated annealing, and LLM-guided search for physical systems and molecular design. APOD · RL-QESA · Molecular optimization

News

All news

Selected publications

All publications
  1. HP-JEPA motivation and overview: multi-resolution graph structure, limitations of fixed partitions, and adaptive resolution weighting.

    HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

    Ruichen Xu, Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Linsey Pang, Ravid Shwartz-Ziv, Yann LeCun, Yuefan Deng

    arXiv:2608.00491 · 2026

    Learns graph representations across multiple resolutions through hierarchical partitioning and joint-embedding prediction.

    arXiv PDF
  2. GAGA overview showing forward noising and a Gaussian approximation that shortens the reverse generative trajectory.

    GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation

    Jingxiang Qu, Wenhan Gao, Ruichen Xu, Yi Liu

    ICLR 2026

    Uses Gaussianity-aware approximations to shorten diffusion sampling for efficient 3D molecular generation.

    OpenReview arXiv PDF
  3. Multistep protein backmapping pipeline, from coarse beads through intermediate structures to atomistic detail using successive generative networks.

    Multistep Generative Backmapping of Coarse-Grained Structures

    Georgios Kementzidis, Erin Wong, John Nicholson, Ruichen Xu, Yuefan Deng

    Computer Physics Communications · 327, 110286 · 2026

    Restores atomistic detail from coarse-grained structures through a sequence of intermediate generative models.

    DOI
  4. CROP pipeline maps an input through lifting, a neural operator on band-limited latent features, and projection to an output at any discretization.

    Discretization-invariance? On the Discretization Mismatch Errors in Neural Operators

    Wenhan Gao, Ruichen Xu, Yuefan Deng, Yi Liu

    ICLR 2025

    Studies errors caused by changing discretization in neural operators and develops a band-limited latent representation.

    OpenReview Conference page PDF
  5. Kolmogorov–Arnold and multilayer-perceptron Hamiltonian neural network architectures, with energy derivatives feeding a shared training loss.

    Kolmogorov–Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks

    Zongyu Wu, Ruichen Xu†, Luoyao Chen, Georgios Kementzidis, Siyao Wang, Yuefan Deng · †Co-first author and project lead

    IJCNN 2025

    Combines Kolmogorov–Arnold representations with Hamiltonian neural networks to learn energy-based dynamical models.

    IEEE DOI arXiv PDF
  6. APOD framework turns noisy initial distributions into samples through diffusion steps guided by observation and PDE terms.

    APOD: Adaptive PDE-Observation Diffusion for Physics-Constrained Sampling

    Ruichen Xu, Haochun Wang, Georgios Kementzidis, Chenhao Si, Yuefan Deng

    ICML 2025 Workshop on Assessing World Models · Poster

    Guides diffusion sampling with observations and PDE constraints to recover physically consistent fields.

    OpenReview PDF
  7. A Fourier layer uses a neural kernel generator to create a convolution kernel from input frequencies before inverse transform, bias and activation.

    Dynamic Schwartz–Fourier Neural Operator for Enhanced Expressive Power

    Wenhan Gao, Jian Luo, Ruichen Xu, Yi Liu

    Transactions on Machine Learning Research · 2025

    Introduces input-dependent Fourier kernels to increase the expressive power of neural operators.

    OpenReview PDF

Academic service

Reviewing experience

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

Conference reviewing & program committees: ICLR (2026, 2027) — 2027 invitation accepted; ICML (2026) — Gold Reviewer; NeurIPS (2026); KDD — AI4Sciences Track (2026, 2027) — Reviewer; 2027 Cycle 1; KDD — Research Track (2027) — Reviewer; Cycle 1; AAAI (2027) — Program Committee member; IJCNN (2025, 2026).

Journal reviewer: TMLR (2026), IEEE TNNLS (2025), and Neurocomputing (2026).

Workshop reviewer: AI for Math @ ICML (2025).