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.

Institution
Sun Yat-sen University
Program
Physics undergraduate
Location
Shenzhen, China
Period
Sept. 2024–Present
Expected June 2028

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.

Independent research · 2026

Validation Geometry for Reward Optimization under Proxy Misspecification

A finite validation protocol can miss precisely the proxy errors that an optimizer amplifies. This project develops an optimizer-conditioned geometry for certifying a specified reward-optimization endpoint.

Validation geometry diagram showing optimizer sensitivity decomposed into measured and validation-invisible components, with the invisible projection determining sharp worst-case proxy bias.
Finite validation controls only the component of optimizer sensitivity inside its measured subspace; the orthogonal component determines the sharp undetectable bias.
Core result
For any fixed optimizer endpoint, the centered density ratio relative to the reference distribution is the exact sensitivity direction for proxy overstatement.
Design insight
Average risk, restricted worst-case coverage, and feasible measurement selection require distinct validation-design objectives.
Evidence and scope
Experiments verify sharp kernel witnesses, identifiability transitions, noisy certificates, and transfer failures under shifted or unseen optimizer directions. The result certifies a declared endpoint rather than universal safety.

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.

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.

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

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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

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)