Andrew Gordon Wilson

Andrew Gordon Wilson is a Professor at New York University's Courant Institute of Mathematical Sciences and the Center for Data Science. His research focuses on deep learning, Bayesian methods, and machine learning for physics, with applications in scientific discovery, uncertainty representation, and inductive biases. He leads the development of the GPyTorch library for scalable Gaussian processes and has contributed to foundational work in kernel methods and deep kernel learning.

Wilson holds a PhD in Machine Learning from the University of Cambridge (2014). His work bridges theoretical advances and practical applications, including contributions to generalization theory, scalable optimization, and generative models. He has received awards such as the Outstanding Paper Award at ICML 2022 and the Best Student Paper Award at UAI 2011.

Notable collaborations include projects on antibody design, protein engineering, and materials discovery, often leveraging Bayesian optimization and Gaussian processes. His team develops tools like CoLA (Compositional Linear Algebra) for efficient algebraic computations and explores scaling laws in neural networks. Wilson is actively involved in academic outreach, mentoring students, and advancing open-source machine learning libraries.