You learn new tools that enable you to solve optimization problems with uncertain data in an efficient and scalable manner. Uncertainty often arises in practice from bid-ask spreads, estimation errors, and the like. You gain understanding of modern numerical optimization methods for quantitative finance applications, ranging from portfolio selection and model calibration to asset pricing. You also learn to handle estimation errors and discuss how these methods can be applied in the context of e-commerce, in areas from personalization to ad targeting. This course is intended for portfolio managers, traders, risk management officers, corporate managers, and professionals interested in finance and e-commerce issues.
Enrollment is limited and advance enrollment is required.
Some knowledge of statistics, linear algebra, and basic optimization such as linear programming. His area of expertise is convex optimization and decision making under uncertainty. He has been a consultant for several banks and companies involved in e-commerce.