Variational Inference
Bayesian Learning
Optimal Transport
Score Matching
Fokker-Planck Equation
Stochastic Process
Causal Inference
Manifold Learning
Function Approximation
Generalization Bound
KL Divergence
Entropy Minimization
PAC Learning
Latent Variable Model
Hilbert Space
Symbolic Reasoning
Neural ODE
Jacobian Matrix
Gradient Descent
Expectation Maximization
Backpropagation
Stochastic Optimization
Empirical Risk Minimization
Regularization
Information Geometry
Reproducing Kernel
Rademacher Complexity
VC Dimension
Metric Space
Theoretical Bounds
Neural Networks

Plote

Researcher • Scholar • Thinker

Academic Profile

Research Interests

My research interests lie in the theoretical foundations and structural mechanisms of generative modeling, particularly at the intersection of mathematics and Machine learning. Specifically, I study diffusion models and Autoregressive models from a mathematical perspective, focusing on their connections to Markov processes, stochastic differential equations, and Boltzmann-type distributions. I am intrigued by their capacity for structure-aware generation, and I have explored both token-based and continuous-space implementations. Ultimately, I aim to contribute to the development of mathematically grounded, physically coherent, and structurally expressive generative systems—ones that combine rigorous modeling with creative intelligence.

Biography

I am currently an undergraduate student majoring in Physical Modeling and Intelligent Engineering, with a comprehensive academic background in mathematics, physics, materials science, and computer science. This multidisciplinary training has equipped me with a solid foundation in quantitative analysis and cross-scale system modeling. I am particularly interested in theoretically grounded artificial intelligence, and I approach generative modeling through the lens of mathematical structures, and dynamical processes. I have studied Markov processes, diffusion equations, Boltzmann statistics, Lagrangian mechanics, and ordinary differential equations, and I am currently exploring convex optimization theory and its implications for generalization in deep generative models.

Selected Publications

Affiliations

Peking University (Visiting)
Institute of Automation,Chinese Academy of Sciences (Visiting)
Nanjing University

Academic Columns

笔记

Research notes and methodological insights from my ongoing projects and experiments.

论文介绍

Critical reviews and summaries of influential papers in AI and cognitive science.

关于我

Personal reflections on academic life, research philosophy, and interdisciplinary thinking.