Generate a tailored SOP for Dr. Geoff Pleiss. Improve your application with a focused, well-structured draft.
Geoff Pleiss is an academic researcher with a focus on artificial intelligence and machine learning. He has significant experience exploring Bayesian optimization, probabilistic machine learning, uncertainty quantification, and reliable deep learning. His academic journey includes notable institutions such as Columbia University, where he held a postdoctoral position at the Zuccerman Institute from 2020 to 2023. He completed his Ph.D. in Computer Science at Cornell University in 2020, where he also earned his M.S. in Computer Science in 2018. Prior to his studies at Cornell, he obtained a B.Sc. in Engineering from Olin College of Engineering in 2013. As an associate member in Statistics, he actively engages in interdisciplinary research that combines concepts from statistics and computer science, emphasizing the importance of reliability and quantification of uncertainty in machine learning models.
Offers course-only and thesis routes. Focus areas include philosophy of science, mind, ethics, and Asian philosophy.