11/3/16 | 4:15pm | E51-315 
Reception to follow.

Abstract: Online ads are delivered in a real-time fashion under uncertainty in an environment with strategic agents. Making such real-time (or online)  decisions without knowing the future results in challenging stochastic optimization problems for ad selection and dynamic mechanism design problems for repeated auctions. In this talk, I will present a number of recent theoretical models and results in this  area inspired by applications in reservation and exchange markets in display advertising.

In particular, after a short introduction, I will first highlight the practical importance of considering ³hybrid² models that can take advantage of forecasting for stochastic models and at the same time, are robust against  adversarial changes in the input such as traffic spikes and discuss our recent results combining stochastic and adversarial input models from recent

Vahab Mirrokni

Technical Research Director
Google Research

Bio: Vahab Mirrokni is a principal scientist (and technical research director), heading the algorithms research groups at Google Research in New York. The groups at Google Research New York include market algorithms, graph mining, and large-scale optimization groups. He also teaches Algorithms and Economics of the Internet as an adjunct associate professor at the Courant Institute at NYU.

Prior to Google, he spent a couple of years in the Theory Group at Microsoft Research, the Theory of Computation Group at MIT CSAIL, and the strategic planning and optimization group at Amazon.com. 

He received his PhD from MIT in 2005 under supervision of Professor Michel Goemans, and his B.Sc. in Computer Engineering from Sharif University of Technology.