Dr. Frongillo's research interests lie in the interface of theoretical machine learning and economics (encompassing topics such as information elicitation, crowdsourcing, learning, and markets), as well as dynamical systems. Below are broad questions describing his current focus. What is the expressive power of empirical risk minimization? How can we design better incentives for crowdsourcing? Can we understand markets in terms of optimization algorithms? How can we prove rigorous results in dynamics using computational topology?
algorithmic economics, theoretical machine learning, information elicitation, dynamical systems