Generate a tailored SOP for Dr. Risi Kondor. Improve your application with a focused, well-structured draft.
Risi Kondor’s work is centered on basic machine learning methodology, inspired by ideas in algebra and computational harmonic analysis. In recent years, the work in Risi’s lab has focused on the rapidly growing intersection of machine learning and science, including novel graph neural network architectures for chemistry, machine learning approaches for learning molecular force fields, and neural network approximations for quantum states. An integral part of the group’s work is the development of high-performance, open-source software libraries. Risi is a member of the Computer Science Department and the Computational Applied Mathematics group of the Department of Statistics. He received a BA in Mathematics from Cambridge, a PhD in Computer Science from Columbia University, and followed postdoctoral appointments at the Gatsby Unit (University College London) and the Center for Mathematics and Information at Caltech. He also holds a diploma in computational fluid dynamics from the Von Karman Institute and an MS in Machine Learning from Carnegie Mellon University. Risi previously worked with the deep learning team at Amazon Web Services and the Center for Computational Mathematics at the Flatiron Institute.
Department of Philosophy