Yuwen Huang’s homepage
Information Theory · Statistical Physics · Optimization · Quantum Information
Structured inference and optimization
with rigorous guarantees
I am a postdoctoral researcher at CUHK working on provable and scalable methods for inference, counting, and optimization. My work combines probabilistic graphical models, Bethe and graph-cover techniques, combinatorics, tensor-network methods, and distributed quantum computation to design algorithms with rigorous guarantees.
Current work
Graphical models, combinatorial inference, optimization with guarantees, and distributed quantum systems.
Future direction
Structured machine learning, learning theory, and quantum information processing. [ICML2025] [Quantum2026]
Theory to Impact
Theory
information theory and statistical physics
Algorithms
Inference, counting, and optimization with structure and guarantees
Systems
quantum networks
Impact
ML applications and efficient computation
Research Directions
Representative directions and related papers:
Probabilistic graphical models
Bethe methods, graph covers, and message passing for inference and counting. [ISIT2020] [ITW2022] [ISIT2024] [TIT-Sub]
Optimization and decision-making
Permanent bounds and structure-exploiting optimization with guarantees. [ISIT2023] [TIT2024] [ICML2025]
Tensor networks and quantum-enabled methods
Compact representations and quantum-enabled methods for high-dimensional computation. [ISIT2021] [Quantum2026]
Distributed quantum computing and networks
Structure-aware quantum architectures for scalable optimization and analytics. [ICML2025] [Quantum2026]