Matt J. Kusner was a visiting researcher at Cornell University under the supervision of Kilian Q. Weinberger and received his Ph.D. in Machine Learning from Washington University in St. Louis. His work is in the areas of privacy, budgeted learning, model compression, and Bayesian optimization. He is from Iowa City, Iowa, USA and is married to the wonderful Sonia Rego.
Matt's research aims to address the disconnect between state-of-the-art machine learning models, and models that are often used to solve real-world problems. Frequently, in real-world settings the modeller is confronted with a trade-off between maximising an objective (e.g., returning more accurate results) and minimising a budget (e.g., producing predictions in under a millisecond). His research considers three specific types of budgets: time, space, and privacy. These feature in recommendation, face recognition, bankruptcy prediction, stock market modelling, and real-time machine translation. Directly addressing these real-world trade-offs at an optimisation level results in algorithms that are simultaneously practical and accurate.