Dmitriy (Tim) Kunisky is an assistant professor in the Department of Applied Mathematics and Statistics. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms.
In particular, his work seeks to identify the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs. To that end, he has drawn on a range of fields including asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. He has studied the performance of convex relaxation algorithms on combinatorial optimization problems, computational intractability and information-computation gaps in high-dimensional statistics, pseudorandomness in statistics and random matrix theory, and experimental approaches to questions in number theory and combinatorics.
He received his bachelor’s degree in mathematics from Princeton University, worked as a software engineer for Google, and received his PhD in mathematics from the Courant Institute of Mathematical Sciences at New York University. Afterwards, he was a postdoctoral associate in computer science at Yale University until joining Johns Hopkins.
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New assistant professor shares insights on probability theory and computational complexity, highlighting both practical applications and the challenges of handling massive, high-dimensional datasets.