Biography
Alexander Terenin is an Assistant Research Professor at Cornell. He is interested in machine learning, particularly for problems where the data is not fixed, but is gathered interactively by the learning machine. His work focuses on data-efficient interactive decision-making algorithms such as Bayesian optimization, and uncertainty-aware probabilistic models that power such algorithms, including Gaussian processes. His technical contributions to this area have won multiple best-paper-type awards at top machine learning conferences. More recently, he is also interested in non-probabilistic approaches to decision-making, particularly in non-discrete problems, including for multi-armed bandits, online learning, and reinforcement learning.