Yuwen Huang’s homepage
Information Theory · Statistical Physics · Optimization · Quantum Information
Structured inference and optimization with rigorous guarantees
Research assistant positions open now; PhD inquiries are also welcome
I am building a research group on graphical models, inference, optimization, machine learning, and quantum information, with an emphasis on mathematically rigorous ideas that lead to scalable algorithms.
For now, I am primarily looking for research assistants; exceptional PhD candidates are also encouraged to reach out.
How to apply
Please send a concise email including a short motivation note, and attach your CV and transcript.
[RA Application] Your Name - Current Institution [PhD Inquiry] Your Name - Current Institution- Brief motivation in the email body
- CV attached
- Transcript attached
I am a postdoctoral researcher at CUHK developing provable and scalable methods for inference, counting, and optimization in structured systems. My work brings together probabilistic graphical models, Bethe and graph-cover methods, combinatorics, tensor-network representations, and distributed quantum computation to turn mathematical structure into practical algorithms with rigorous guarantees.
Current work
Structure-aware inference, combinatorial counting, optimization, and distributed quantum computation.
Future direction
Bringing structure-aware inference and optimization into machine learning, learning theory, and quantum information processing.
Theory to Impact
Theory
information theory · statistical physics
Algorithms
Inference, counting, and optimization with structure and guarantees
Systems
distributed quantum platforms
Impact
machine learning and efficient computation
Research Directions
Selected directions and representative papers:
Probabilistic graphical models
Bethe approximations, graph covers, and message passing for principled inference and counting.
Optimization and decision-making
Structure-exploiting optimization and permanent bounds with provable guarantees.
Tensor networks and quantum-enabled methods
Tensor-network representations and quantum-enabled methods for compact, high-dimensional computation.
Distributed quantum computing and networks
Distributed quantum architectures for large-scale optimization, inference, and data-intensive analytics.