Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa


Geoff Pleiss is a postdoc in the department of statistics and Zuckerman Institute at Columbia University. He received his Ph.D. from Cornell University under the supervision of Kilian Q. Weinberger, and his undergraduate degree in engineering from Olin College. His research interests intersect deep learning and probabilistic modeling, with an emphasis on how to make learning algorithms more scalable, robust, and reliable. In particular, his work focuses on uncertainty quantification, detecting anomalous training and test data, and speeding up Gaussian processes for extremely large datasets.