Mark Sellke

Mark Sellke is an Assistant Professor of Statistics at Harvard University's Department of Statistics. He is affiliated with the Faculty of Arts and Sciences and is based at the Science Center in Cambridge, MA. His research interests focus on spin glasses, machine learning theory, and high-dimensional statistics, with contributions to optimization theory, statistical physics, and algorithmic analysis.

Dr. Sellke's work bridges theoretical computer science, probability, and mathematical physics. His recent publications explore algorithmic hardness in combinatorial optimization, energy landscapes in spin glasses, and foundational questions in machine learning. Notable areas include computational lower bounds for partitioning problems, stability analysis of local optima, and universality phenomena in spin glass models.

• Education: Advanced degrees in Statistics/Computer Science (details not explicitly listed)

His research has been recognized through publications in top venues such as FOCS, NeurIPS, and JASA. Collaborative projects include work on multi-species spin glasses and algorithmic approaches to complex systems. Current research trends emphasize the intersection of optimization, stochastic processes, and high-dimensional data analysis.

Dr. Sellke maintains an active research group and contributes to the academic community through courses on statistical theory and machine learning. His lab is part of Harvard's broader efforts in data science and probabilistic modeling.