About Me (CV here)

I am currently a third-year PhD student in the Department of Statistics at Harvard University, advised by Dr. Morgane Austern. I am graciously supported by the NSF Graduate Research Fellowship. Broadly, I am interested in the intersection of mathematics with statistical methods, such as universality, free probability, and random matrix theory. In June 2022, I completed my B.S. in Pure Mathematics and B.S. in Statistics at UCLA.

Outside of class, I am interested in lots of different things! I love playing the guitar & piano, cooking, pottery, shooting film photography, playing chess, learning languages, and more. My favorite artists are the Beatles, the Smiths, Clairo, Nick Drake, Mac DeMarco, and Chopin.

I do private tutoring for both statistics and mathematics. Please feel free to reach out to me by email if you would like tutoring for courses in the Harvard Statistics department, or anything else more general. Also check out my Instagram page, Razi ba Riazi, for cool mathematics videos, PhD advice, and more.

Theorem of the Month, May 2025

Let us recursively define a sequence by letting $S_1 = 4$ and $S_{n+1} = S_{n}^2 - 2$. Then for every prime $p\geq3$, the Mersenne Number $M_p = 2^p-1$ is prime if and only if $M_p$ divides $S_{p-1}$. For example, $M_3 = 2^3-1 = 7$ is prime, and $7$ divides $S_{3-1} = S_2 = 4^2-2 = 14$.

Song of the Month, May 2025

Still Beating (Mac DeMarco): I love Mac’s music, especially the album that this song is on! I wanted tickets to his Boston show but it was only resellers for hundreds of dollars :(

Publications

Esmaili Mallory, M.*, Huang, K. H.*, & Austern, M. (2025). “Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation.” arXiv preprint. arXiv:2502.15752. (Accepted to COLT 2025).

Esmaili Mallory, M., Brown, J. & Glickman, M. (2025). “Come Together: Analyzing Popular Songs Through Statistical Embeddings.” (In Progress).

Yu, A., Becquey, C., Halikias, D., Esmaili Mallory, M., & Townsend, A. (2021). “Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks.” arXiv preprint. arXiv:2109.11354.

(* indicates equal contribution)

Teaching

• Stat 149: Introduction to Generalized Linear Models, Dr. Mark Glickman. Harvard University, Spring 2024.

• Stat 104: Introduction to Quantitative Methods for Economics, Dr. Kevin Rader. Harvard University, Fall 2023.

• Math 115A: Linear Algebra, Dr. Will Conley. UCLA, Winter 2022.

• Math 115A: Linear Algebra, Dr. Christy Hazel. UCLA, Spring 2021.