I'm a New York City-based researcher studying the overlap between machine learning and theoretical computer science. My research is broadly motivated by a desire to improve the interpretability, transparency, and accountability of neural networks by understanding their mathematical properties. My Ph.D. research was funded by an NSF GRFP fellowship, which I received in March 2021.

Before starting my PhD at Columbia, I studied applied math and computer science as an undergrad at Brown and worked as a data scientist at LinkedIn. I've completed internships at LinkedIn (data science), Lumi Labs (engineering at a 15-person startup), Allen Institute for AI (climate modeling), Microsoft Research (transformer theory), and Google Research (transformers and graph reasoning).

My work

Most of my research consists of mathematical results about the capabilities and limitations of neural networks and other machine learning algorithms. In particular, I study:

• the fundamental limitations of architectures like random feature models, deep neural networks, and self-attention units;
• the generalization and optimization properties of machine learning models like maximum-margin linear classifiers, neural nets trained with gradient descent on low-dimensional data, randomly initialized recurrent neural networks, and minimum-norm interpolating neural networks;
• the empirical and theoretical abilities of transformer models trained to solve combinatorial planning tasks (work in progress from a Summer 2023 internship at Microsoft Research NYC).

I applied machine learning techniques to climate modeling research as an internship at the Allen Institute for AI (AI2) during Summer 2022. My contributions led to my recognition as an Outstanding Intern.

As an undergraduate, I completed a variety of research projects on mathematical modeling (as a winner of the COMAP Interdisciplinary Contest for Modeling), dynamical systems (with Bjorn Sandstede), molecular biology (with William Fairbrother), and machine learning theory (with Eli Upfal).

I primarily code in Python and am experienced with core data science and deep learning packages (e.g. Pytorch, Tensorflow, Sklearn, and Pandas). I programmed in Java and Scala during my undergraduate years and when I worked at LinkedIn and Lumi Labs. The climate modeling repository I contributed to while interning at AI2 is publicly available, and my Microsoft Research code will be published once we upload our preprint.

Teaching experience

I TA'd five different courses at Brown: accelerated intro to CS, discrete math, CS theory, intro to dynamical systems, and algorithms, for which I was Head TA.

I was a graduate TA for courses on computational learning theory with Rocco Servedio, natural and artificial neural networks with Christos Papadimitriou and John Morrison, and ML and climate with Alp Kucukelbir. Natural and artificial neural networks was a new course, for which I developed a lab component from scratch in collaboration with Sam Deng; all materials are available online.

As a graduate student, I coordinated undergraduate seminars on deep learning theory during Summer 2021, Fall 2021, and Spring 2023.

Departmental and academic service

I served as a reviewer for ICLR 2024, NeurIPS 2023, JMLR, SODA 2023, and STOC 2022.

I was currently a PhD representative alongside the great Tim Randolph. We represented computer science PhD student interests and concerns to CS faculty and administrators.

In the Columbia CS theory group, I coordinated the student retreat for fall 2021 and 2022 and started (but no longer run) the CS Theory Student Seminar.

I was the leader of qSTEM, an organization for LGBTQ+ students in the Columbia School of Engineering and Applied Sciences.

Find me online:

[linkedin] [github] [arxiv] [google scholar] [dblp]

I am indebted to many fantastic mentors over many years among my teachers, professors, advisors, coworkers, and friends. I am always happy to pass it forward and chat with anyone interested in learning more about PhD programs in ML and/or theory, Columbia CS, NYC living, or niche neighborhood concerns about Morningside Heights.