Princeton University

Udith Haputhanthri

I am an Electrical and Computer Engineering PhD student at Princeton University, working with Jonathan D. Cohen and Taylor Webb. I study reasoning and learning in foundation models, drawing inspiration from cognitive science and neuroscience. (Read more about the questions behind my research)

My current work focuses on sequential visual reasoning in VLMs and efficient long-context reasoning in LLMs.

I earned my B.Sc. in Biomedical Engineering from the Department of Electronic and Telecommunication Engineering at the University of Moratuwa, Sri Lanka, in 2022, with First Class honors.

Outside academia, I enjoy chess, music, and playing instruments.

Udith Haputhanthri

Research Experience

Princeton University

August 2024 – present

Graduate Researcher · Department of Electrical and Computer Engineering, Princeton Neuroscience Institute, and Department of Psychology

Advisors: Jonathan D. Cohen and Taylor Webb.

I study how biological and artificial systems learn, reason, and process information over time, combining ideas from cognitive science, neuroscience, and machine learning.

My current research focuses on understanding and improving visual reasoning in vision-language models (VLMs). In my recent work, I investigated the binding problem in VLMs, showing how spatially grounded reasoning induces serial attention routines that reduce interference between object representations and improve generalization. I am extending this work to develop recurrent and video-generative VLM architectures capable of more general forms of sequential visual processing.

In parallel, I investigate efficient long-context reasoning in foundation models with in-place test-time training, episodic memory, and KV-cache compaction.

Stanford University

January – June 2024

Visiting Student Researcher · Department of Biology

I worked in Professor Mark Schnitzer’s lab on building machine learning models that replicate activity recorded from the mouse brain (i.e., digital twin brains) during visual decision-making tasks.

Further, using dynamical systems theory, I analyzed how these learned neural activity patterns evolve over time to achieve specific behaviors and interact with other brain regions.

Harvard University

July 2022 – December 2023

Post-Baccalaureate Fellow · Faculty of Arts and Sciences

I worked on understanding learning dynamics in autoregressive machine learning models (e.g., recurrent neural networks), and how biologically inspired neural computation allows them to have stable convergence dynamics.

I also developed computational models of high-throughput imaging systems combining machine learning, signal processing, optics, and low-power electronics.

Questions Behind My Research

How do the brain’s circuits give rise to complex behavior, and how could we replicate that? My research has followed this question through engineering , imaging , neuroscience , and cognitive science .

At Harvard, I worked with Dr. Dushan Wadduwage on computational microscopy , combining my machine learning experience and biomedical background to develop new imaging systems. Building these systems made me think more deeply about what it would take to observe the brain well enough to recreate its circuits and behavior, and how imaging limitations constrain our understanding. In parallel, I worked with Dr. Hidenori Tanaka on NeuroAI , exploring what we could learn from the limited knowledge and data we already had about the brain. I drew on biological learning and population dynamics to understand stability and convergence issues in artificial neural networks, and ways to mitigate them.

This work made me want to study those dynamics in actual brain recordings . At Stanford, I joined Dr. Mark Schnitzer’s lab to build artificial neural networks that model mouse neural activity and reproduce behavioral patterns (i.e., digital twin brains). Using dynamical systems theory, I studied how these learned activity patterns evolve over time and relate to behavior .

Having worked across machine learning, imaging, NeuroAI, and computational neuroscience, I came to think that AI and neuroscience had drifted somewhat apart, despite their history of driving one another forward. When I began my PhD at Princeton, I wanted to work where the two could inform each other: taking inspiration from the biological brain to advance AI , and using AI to understand the brain better . This led me to work with Jonathan Cohen and Taylor Webb. My current work draws on cognitive science and neuroscience to explain how machine learning works and build more human-like, trustworthy ML systems.

Publications

Google Scholar ↗

* Equal contribution. † Equal supervision. Preprints are labeled below.

Brain-Inspired Research

Computational Microscopy

Earlier Work: Applied Machine Learning and Computer Vision