I am an Assistant Professor of Statistics at Harvard.
I received my PhD in mathematics from Stanford, where I was fortunate to be advised by Andrea Montanari and Sébastien Bubeck.
My research is at the interface of probability, mathematical physics, theoretical computer science, and statistics.
I am especially fascinated by computational barriers and algorithmic thresholds in random complex systems.
Recent and Selected Papers
See my research page for a full list of papers with some accompanying videos and slides.Y. Polyanskiy and M. Sellke. Nonparametric MLE for Gaussian Location Mixtures: Certified Computation and Generic Behavior
B. Huang and M. Sellke. Strong Low Degree Hardness for Stable Local Optima in Spin Glasses
M. Sellke. Localization of Random Surfaces with Monotone Potentials and an FKG-Gaussian Correlation Inequality
R. Bazaes, C. Mukherjee, M. Sellke, and S.R.S. Varadhan. Effective mass of the Fröhlich Polaron and the Landau-Pekar-Spohn conjecture
M. Sellke. The Threshold Energy of Low Temperature Langevin Dynamics for Pure Spherical Spin Glasses
Comm. Pure. Appl. Math., Vol. 77 (2024), no 11, 4065-4099
M. Sellke. Almost Quartic Lower Bound for the Fröhlich Polaron's Effective Mass via Gaussian Domination
Duke Math Journal, Vol. 173 (2024), no. 13, 2687-2727
A. E. Alaoui, A. Montanari and M. Sellke. Sampling from the Sherrington-Kirkpatrick Gibbs measure via Algorithmic Stochastic Localization
FOCS 2022
B. Huang and M. Sellke. Tight Lipschitz Hardness for Optimizing Mean Field Spin Glasses
Comm. Pure. Appl. Math., Vol. 78 (2025), no 1, 60-119
Conference version in FOCS 2022
S. Bubeck and M. Sellke. A Universal Law of Robustness via Isoperimetry
JACM, Vol. 70 (2023) no. 2, Article 10, 1-18
Conference version in NeurIPS 2021. Awarded Outstanding Paper.
M. Sellke. Cutoff for the Asymmetric Riffle Shuffle
Ann. Probab., Vol. 50 (2022) no. 6, 2244-2287
M. Sellke. Chasing Convex Bodies Optimally
GAFA Seminar Notes, 2023
Conference version in SODA 2020. Awarded Best Paper and Best Student Paper.