Professor Frazier works in sequential decision-making under uncertainty and machine learning, focusing on problems where information is acquired over time. Behaving optimally in such problems is also known as optimal learning. He works on applications in simulation, e-commerce, medicine, and biol...
Professor Frazier works in sequential decision-making under uncertainty and machine learning, focusing on problems where information is acquired over time. Behaving optimally in such problems is also known as optimal learning. He works on applications in simulation, e-commerce, medicine, and biology. Within simulation, he views the design of simulation optimization algorithms as an optimal learning problem, and is developing new simulation optimization algorithms with optimal average-case performance. This work uses Bayesian statistics and dynamic programming to make better decisions about which simulations we should perform, to solve simulation optimization problems more quickly.