Michael C. Fu Source Confirmed

OverviewAI-generated summary

Michael C. Fu's research encompasses simulation techniques, queuing theory, multi-objective optimization algorithms, stochastic processes, and statistical methods. His work delves into both theoretical advancements and practical applications across diverse domains. Recent research includes studies of Monte Carlo tree search, dynamic sampling allocation, and risk-sensitive reinforcement learning. Fu's optimization research extends to quantile optimization and the convergence rate of stochastic approximation for gradient-based stochastic optimization.

Metrics

• h-index: 49
• Publications: 611
• Citations: 10,416