My current research focuses on machine learning, particularly on diffusion-based models and related topics such as flow matching and stochastic interpolants in both continuous and discrete spaces.
Previously, I worked in the fields of probability theory and analytic combinatorics. I was especially interested in studying various lattice models and combinatorial statistics, with an emphasis on scaling limits.
Exceptional points of discrete-time random walks in planar domains [with Y. Abe and M. Biskup], accepted to Electron. J. Probab. (2023).
Central limit theorem for peaks of a random permutation in a fixed conjugacy class of S_n [with J. Fulman and G. Kim], accepted to Ann. Comb. (2022).
On the joint distribution of descents and signs of permutations [with J. Fulman, G. Kim, and T. Kyle Petersen], accepted to Electron. J. Comb. (2021).
A central limit theorem for descents and major indices in fixed conjugacy classes of S_n [with G. Kim], accepted to Adv. in Appl. Math. (2021).
Central limit theorems for descents in conjugacy classes of S_n [with G. Kim], J. Combin. Theory Ser. A (2020).
Limit theorems for random walk local time, bootstrap percolation and permutation statistics, Defended in December 2019.