Andrej Risteski 

I am an Assistant Professor at the Machine Learning Department in Carnegie Mellon University. Prior to that, I was a Norbert Wiener Research Fellow jointly in the Applied Math department and IDSS at MIT. I received my PhD in the Computer Science Department at Princeton University under the advisement of Sanjeev Arora.

My research interests lie in the intersection of machine learning, statistics, and theoretical computer science, spanning topics like (probabilistic) generative models, algorithmic tools for learning and inference, representation and self-supervised learning, out-of-distribution generalization and applications of neural approaches to natural language processing and scientific domains.
More broadly, the goal of my research is principled and mathematical understanding of statistical and algorithmic problems arising in modern machine learning paradigms.

I am the receipient of an Amazon Research Award ("Causal + Deep Out-of-Distribution Learning") and an NSF CAREER Award ("Theoretical Foundations of Modern Machine Learning Paradigms: Generative and Out-of-Distribution"). I am also in part supported by NSF award IIS-2211907 ("Foundations of Self-Supervised Learning Through the Lens of Probabilistic Generative Models").

The easiest way to reach me is email. My address is aristesk at andrew.cmu.edu

Cinque Terre

Generative Models


Forest

Representation Learning


Northern Lights

Neural Language Models


Mountains

Out-of-Distribution Generalization


Northern Lights

AI for Scientific Applications


Northern Lights

Sampling and Optimization


Papers on Generative Models (sorted by date)

Papers on Out-of-Distribution Generalization (sorted by date)

Papers on Representation Learning (sorted by date)

Papers on AI for Science (sorted by date)

Papers on Sampling and Optimization (sorted by date)

Papers on Neural Language Models (sorted by date)

All papers (sorted by date)

Current Students

Alumni