Dmitriy Kunisky

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Data Science and AI Institute, Department of Mathematics, and Algorithms and Complexity Group. Prior to JHU, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from New York University’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the interplay between probability theory, mathematical statistics, and computational complexity, particularly in high-dimensional data and algorithmic limitations. He has held roles in software engineering (Google) and academic advising (ETH Zurich).

Education:

• Bachelor’s in Mathematics, Princeton University
• PhD in Mathematics, Courant Institute, NYU

Research Interests:

His work examines computational thresholds in statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores the mathematical foundations of algorithmic performance, including information-computation gaps and pseudorandomness. Recent topics include nonlinear Laplacians, random circulant graphs, and low-coordinate-degree algorithms.

Teaching & Outreach:

In Fall 2025, he teaches Random Matrix Theory in Data Science. Past courses include Sum-of-Squares Optimization (Yale) and Mathematical Statistics (NYU). He advises graduate students like David Opalic and Nikolaus Doppelbauer on topics like Sherrington-Kirkpatrick Hamiltonians and mutually unbiased bases.

Upcoming Engagements:

• BIRS Workshop on Combinatorics (June 2025)
• TTIC Workshop on Information-Computation Tradeoffs (June 2025)
• INFORMS Applied Probability Conference (July 2025)
• COLT Conference (July 2025)

Labs/Teams:

Active in JHU’s Data Science and AI Institute, collaborating on projects involving high-dimensional statistics, convex optimization, and algorithm design.