Alexander Terenin

Alexander Terenin is a Researcher at Cornell University's Cornell Engineering, specializing in machine learning and decision-making algorithms. His work focuses on data-efficient interactive systems, probabilistic models, and non-probabilistic approaches to dynamic learning environments.

Research Interests

Terenin's research centers on interactive machine learning, where algorithms dynamically gather data through sequential decision-making. He develops advanced probabilistic models like Gaussian processes for uncertainty quantification and explores non-probabilistic frameworks in reinforcement learning, multi-armed bandits, and online learning. His technical contributions have earned recognition through multiple best-paper awards at premier machine learning conferences.

Scientific Awards

• Best Paper Award at Top Machine Learning Conferences