Rupak Chatterjee

Rupak Chatterjee is an Industry Assistant Professor in the Department of Applied Physics at New York University's Tandon School of Engineering. His research bridges quantum information/computation and mathematical physics, with a focus on quantum algorithms for machine learning, optimization, operator algebras, and quantum mechanical systems.

Education:

• Postdoctoral Scholar, Physics (James Franck Institute, University of Chicago)
• Ph.D. & M.S., Physics (Stony Brook University)
• M.Math., Mathematics (University of Waterloo)
• B.Sc., Physics (University of Calgary)

Chatterjee's research explores quantum systems for machine learning, quantum optimization protocols, and mathematical frameworks like C∗-algebras and supersymmetric quantum mechanics. His work integrates theoretical rigor with applications in quantum computing and complex physical systems.

His recent publications (2019–2024) demonstrate a strong emphasis on quantum entanglement, chaos in optomechanical systems, adiabatic quantum optimization, and relativistic quantum mechanics. Several publications also apply quantum methods to finance and diffusion modeling.

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