Ka Ming Tam

Ka Ming Tam is a Researcher at Louisiana State University's Department of Physics & Astronomy, College of Science. His work spans condensed matter physics, quantum many-body systems, and machine learning applications in physics. He has contributed to advanced computational methods, including nonequilibrium dynamical mean-field theory, functional renormalization group, and parallel tempering algorithms for studying disordered systems.

His research includes

• Quantum materials with disorder and correlation
• Hybrid quantum-classical algorithms for phase transitions
• Epidemiological modeling of social physics
• Tensor formulations for Anderson-Hubbard models
• Machine learning in critical phenomena

The 15 most recent publications highlight his focus on strongly correlated systems, Anderson localization, quantum computing, machine learning in statistical mechanics, and epidemiological dynamics. Key methodologies include DMFT, RG analysis, and GPU-accelerated simulations. No awards or student advisement details are explicitly mentioned in the provided text.