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Kane has diverse research interests within mathematics and theoretical computer science, particularly in the areas of combinatorics, number theory, derandomization and Boolean functions. He has published papers on topics such as algorithms for big data, results on writing numbers as sums of primes and the structure of polynomials in many variables. He has won best paper or best student paper awards in the Conference on Computational Complexity, Foundations of Computer Science and the symposium on Principles of Database Systems. He also has experience as an instructor at Stanford and Harvard University and has helped train students for various mathematics contests, including as an instructor at the United States Math Olympiad Summer Program. He will split his teaching responsibilities between the departments of computer science and mathematics. He expects to teach classes on discrete mathematics, combinatorics, algorithms and number theory, and teach graduate courses on topics in these areas in addition to various areas of complexity theory such as the analysis of Boolean functions.

Capsule Bio:

Daniel Kane graduated from the Harvard University mathematics department in 2011. His thesis work covered a number of topic areas in number theory and theoretical computer science. After completing a three-year postdoc in the mathematics department at Stanford University with the support of an NSF postdoctoral research fellowship, he began his appointment as an assistant professor with a joint appointment in Mathematics and CSE at UCSD.