Efficient Decision-Making and Learning from Big Ranking Data

Decision-making with ranking data is ubiquitous in our life: voters rank candidates in elections, search engines rank websites based on their relevance to input keywords, e-commerce websites recommend items based on users' information and behavior. The fundamental challenge is: How can we make better decisions by learning from big ranking data?
My research tackles this multi-disciplinary challenge by taking a unified approach of statistics, machine learning, and economics. For learning, I will talk about our recent theoretical and algorithmic progresses in efficient learning of mixtures of random utility models, which are arguably the most well-established statistical models for ranking data. For decision-making, I will talk about the design and analysis of decision-making mechanisms w.r.t. computational efficiency, statistical efficiency, and economic efficiency such as fairness and strategy-proofness.