Andrej Risteski is an Assistant Professor at the Machine Learning Department at Carnegie Mellon University. Before joining Carnegie Mellon, Andrej served as a Norbert Wiener Research Fellow jointly in the Applied Math department and IDSS at MIT. He completed his PhD in the Computer Science Department at Princeton University, where he was advised by Sanjeev Arora.

Andrej's research interests encompass the intersection of machine learning, statistics, and theoretical computer science. His work spans a wide range of topics, including generative models, representation and self-supervised learning, out-of-distribution generalization, and neural language models.

In a broader context, the primary objective of Andrej's research is to attain a principled and mathematical understanding of the statistical and algorithmic challenges that emerge in modern machine learning paradigms.

In recognition for his work, he has received 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"). Andrej is also partly supported by NSF award IIS-2211907 ("Foundations of Self-Supervised Learning Through the Lens of Probabilistic Generative Models").