Dr. Srebro’s research encompasses methodological, statistical and computational aspects of machine learning, as well as related problems in optimization. Some of Srebro’s significant contributions include work on learning “wider” Markov networks, introducing the use of the nuclear norm for machine learning and matrix reconstruction, work on fast optimization techniques for machine learning, and on the relationship between learning and optimization. His current interests include understanding deep learning through a detailed understanding of optimization, distributed and federated learning, algorithmic fairness and practical adaptive data analysis.