Research
i'm an undergraduate at mit studying electrical engineering with computing (6-5)
and mathematics (18). i work as a student researcher in the
regina barzilay group at mit csail,
and i'm broadly interested in the intersection of deep learning, physics, and the
life sciences — stochastic processes, condensed matter theory, graph neural
networks, and molecular dynamics.
A Statistical Physics of Language Model Reasoning
READ THE PAPER
modeling transformer reasoning as a stochastic dynamical system
also at
arXiv:2506.04374 /
OpenReview
Transformer language models reason in ways that have largely resisted mechanistic
understanding. With Amir Reisizadeh, I introduce a statistical-physics framework that
treats chain-of-thought reasoning as a continuous-time process: sentence-level hidden
states evolve as a low-dimensional stochastic dynamical system, decomposing each
semantic trajectory into deterministic drift and stochastic fluctuation. Across eight
open-source models and seven reasoning benchmarks, a rank-40 drift manifold captures
roughly 50% of the variance, and the trajectories sort into four distinct latent
reasoning regimes. We formulate and validate a switching linear dynamical system
(SLDS) that reproduces these features, letting us simulate reasoning cheaply and
anticipate critical transitions — misaligned states, adversarially-induced belief
shifts, and other inference-time failures.
Presented at the 2nd Workshop on Reliable and Responsible Foundation Models
(R2-FM) at ICML 2025.
Foundation Models for Multi-Omics
BARZILAY GROUP, MIT CSAIL — in progress
In the Barzilay group I build foundation models over multi-omics data —
jointly modeling genomics, transcriptomics, and metabolomics — toward
personalized identification of therapeutic targets. The goal is representations
that integrate heterogeneous biological measurements well enough to surface
candidate targets that single-modality models miss. Work ongoing; writeup to come.