Andres Muñoz got his bachelor degree in mathematics from the National Autonomous University of Mexico. He then went to pursue a Ph.D. in mathematics at the Courant institute in New York. Dr. Muñoz now works as a research scientist in Google NY doing research in revenue optimization and privacy. Dr. Muñoz’s main area of research is in learning algorithms applied to revenue optimization problems. These problems appear often in online advertisement where billions of auctions happen every day, and we can, therefore, learn how much advertisers value the right to display their ad. This information can then be used to set optimal prices. Learning from these agents, however, poses a much more interesting question: How will advertisers react to these price changes? If they do react, then an algorithm will learn from data provided by the advertiser which, in turn, will depend on the outcome of the learning algorithm. Understanding and exploiting this feedback loop is the other main focus of Dr. Muñoz’s research.