About Me (CV here)

I am a fourth-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, theoretical machine learning, and random matrix theory. In June 2022, I completed my B.S. in Pure Mathematics and B.S. in Statistics at UCLA.

Outside of research, I am interested in lots of different things! I love playing the guitar & piano, cooking, making pottery, shooting film photography, tea culture, 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 really anything involving mathematics or statistics. Also check out my Instagram page, Razi ba Riazi, for cool mathematics videos, PhD advice, and more!

Theorem of the Month, October 2025

Since it is my birthday, I’ll mention the famous birthday problem: What’s the smallest number of people you need in a room to ensure that there is greater than a 50% chance two of them have the same birthday? It’s only 23 people!

Song of the Month, October 2025

Dark Sweet Lady (George Harrison): All the instrumentation in this song is just so gorgeous, and I love thinking about George writing it about his wonderful wife Olivia!

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.” In Proceedings of Thirty Eighth Conference on Learning Theory (pp. 1799–1918). PMLR.

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

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.