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. This Summer 2026, I will be a Quantitative Research Intern at Virtu Financial.

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, April 2026

Consider the integer equation $n! + 1 = m^2$. The only known solutions, up to $n = 10^{11}$, are $(n, m) = (4, 5), (5, 11), (7, 71)$.

Song of the Month, April 2026

Send Me An Angel (Real Life): For some reason I’ve been getting back into the music I hated when I was a kid that my dad played non-stop. This song was one I remember very well and honestly am digging now!

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 175: Statistics and Data Science of Networks, Dr. Morgane Austern. Harvard University, Spring 2026.

• 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.

Talks & Presentations

• Stat 300: Research in Statistics Seminar. Harvard University, April 2025.

• Conference on Learning Theory (COLT) 2025. Lyon, France, July 2025.

• Princeton Machine Learning Theory Summer School. Princeton, NJ, August 2025.

• Joint Symposium on Statistics, Applied Math & Beyond. Harvard University, March 2026.

• Stat 300: Research in Statistics Seminar. Harvard University, April 2026.

• Joint Statistical Meetings (JSM) 2026. Boston, MA, August 2026.