Chen Mu

Physics · Generative Modeling · Physics Inspired AI System

Chen Mu

Physics undergraduate at Sun Yat-sen University interested in generative modeling and the training and development of AI systems inspired by & aligned with physics, real-world data and human values.

CV (PDF) Email GitHub
Institution
Sun Yat-sen University
Program
Physics undergraduate
Location
Shenzhen, China
Period
Sept. 2024–Present
Expected June 2028
Research interests

Generative Modeling, the Alignment Problem, and Real-World- and Physics-Inspired AI Systems

My current interests include diffusion and flow matching, controllable generation, sampling dynamics, ODE/SDE probability transport, the alignment problem, scientific machine learning, and physics- and real-data-inspired AI training and system building. I am particularly enthusiastic about algorithms and operator-based methods, while continuing to build deeper theoretical and practical foundations in these areas.

Selected research

Independent coursework project · 2026

Diffusion, Flow Matching, and CRH-CFG

I independently completed the publicly available coursework and assignments from CMU 10-799, Diffusion and Flow Matching.

Poster (PDF) Paper (PDF) Code / repository
CRH-CFG architecture: a reliability gate chooses between bypass and a constrained search over classifier-free guidance scales, followed by Heun predictor-corrector execution and receding-horizon replanning.
CRH-CFG performs training-free, inference-time search and control over classifier-free guidance scales.
Implemented
DDPM, straight-path Flow Matching, DDIM, Euler and Heun samplers, and conditional classifier-free guidance.
Experiment setting
CelebA 64×64; Modal L40S; independent ResNet-18 evaluator.
Abstract
CRH-CFG adapts classifier-free guidance online through constrained receding-horizon search, reusing frozen conditional and unconditional fields without extra generator evaluations. On CelebA, it modestly improves target adherence at low overhead; preservation and conditional-quality gains are not yet supported.

Engineering disclosure: Codex assisted engineering; I retained research design, interpretation, and validation.

Scientific modeling

Physics and research engineering projects

Darcy-coupled tumor drug transport

A Mathematica finite-element framework coupling Darcy flow with convection–diffusion–reaction transport for intratumoral drug simulation.

Read project paper (PDF)

Bird-vocalization acoustic features

An automated acoustic feature-extraction workflow developed around bird-vocalization data from the Shenzhen–Hong Kong border.

Read project paper (PDF)

Mathematica-based agent harness

Research engineering for AI-assisted scientific workflows, connecting symbolic computation, experiment execution, and auditable outputs.

Private repository

Latest Blog

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Profile

Education and skills

Education

Major in Physics
Sun Yat-sen University
Sept. 2024–Present
Expected June 2028 · Shenzhen

Technical skills

  • Python, C++, C, Wolfram Language
  • PyTorch and scientific ML
  • Server administration and Unix-like systems
  • Docker, LaTeX, and BibTeX
  • TOEFL iBT 104; CET-6 596

Selected recognition

First Prize, 2nd “Illuminating the Future” Optics Science Communication Writing Competition

UCAS Education Foundation / Wiley · “Chirped Pulse Amplification: Born from Playing with Fire”

USACO Gold

Spring 2026

Contact

Research assistant opportunities

I am seeking research assistant opportunities in computer science and AI, especially projects where a physics background can inform modeling, dynamics, control, or scientific reasoning.

chenm369@mail2.sysu.edu.cn · GitHub · CV (PDF)