Mark Sellke
· Assistant Professor of StatisticsHarvard University · Biostatistics
Active 1939–2026
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About
I am an Assistant Professor of Statistics at Harvard. 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.
Research topics
- Physics
- Geometry
- Combinatorics
- Mathematical physics
- Mathematics
- Quantum mechanics
Selected publications
Shattering in Pure Spherical Spin Glasses
Communications in Mathematical Physics · 2025-04-12 · 5 citations
articleSenior authorEarly science acceleration experiments with GPT-5
ArXiv.org · 2025-11-20 · 1 citations
preprintOpen accessAI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We doc…
On Marginal Stability in Low Temperature Spherical Spin Glasses
Communications in Mathematical Physics · 2025-08-06 · 1 citations
article1st authorCorrespondingSampling from mean-field Gibbs measures via diffusion processes
Probability and Mathematical Physics · 2025-07-21 · 1 citations
articleOpen accessSenior authorStable algorithms cannot reliably find isolated perceptron solutions
arXiv (Cornell University) · 2026-03-31
preprintOpen accessSenior authorWe study the binary perceptron, a random constraint satisfaction problem that asks to find a Boolean vector in the intersection of independently chosen random halfspaces. A striking feature of this model is that at every positive constraint density, it is expected that a $1-o_N(1)$ fraction of solutions are \emph{strongly isolated}, i.e. separated from all others by Hamming distance $Ω(N)$. At the same time, efficient algorithms are known to find solutions at certain positive constraint densitie…
Frequent coauthors
- 43 shared
Sébastien Bubeck
- 21 shared
Yuanzhi Li
- 12 shared
Yin Tat Lee
- 12 shared
A. El Alaoui
- 12 shared
Brice Huang
Massachusetts Institute of Technology
- 10 shared
Andrea Montanari
- 10 shared
Yuval Peres
Beijing Institute of Mathematical Sciences and Applications
- 7 shared
Victoria Kostina
California Institute of Technology
Education
Ph.D., Mathematics
Stanford
Awards & honors
- Outstanding Paper Award at NeurIPS 2021
- Best Paper Award and Best Student Paper Award at SODA 2020
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