Guanyang Wang

Guanyang Wang is an Assistant Professor in the Department of Statistics at Rutgers University, New Brunswick. His research focuses on Monte Carlo methods, generative AI, quantum computing, and probability, supported by NSF grants (DMS-2210849, FET-2403007) and an Adobe Data Science Award. He earned his Ph.D. in Mathematics (with a Statistics minor) from Stanford University (2020) and B.S. in Mathematics from USTC (2015). He co-organizes a weekly Monte Carlo methods seminar and advises several Ph.D. students in Statistics and Computer Science.

Notable research includes quantum computing integration with classical simulation, antithetic noise in diffusion models, and spectral analysis of Gibbs samplers. Recent publications span topics like constrained sampling via diffusion models and phase transitions in Restricted Boltzmann Machines. His Erdős number is 2, reflecting his collaborative network in mathematics and computer science.

• Education: Ph.D., Stanford University (2020); B.S., USTC (2015)
• Grants: NSF DMS-2210849, NSF FET-2403007, Adobe Data Science Award
• Labs/Teams: Co-organizer of weekly Monte Carlo methods seminar