About me
I am a Schmidt Science postdoctoral fellow in neuroscience. I am broadly interested in the multiple ways we learn - combining observations and guesses, as specific rules or general concepts, motivated by reward or internal goals. This learning is ultimately enabled and implemented via circuits in our brain - how does the architecture and activity of relevant brain regions allow us to exploit the structure of “the world as it is”, while remaining enormously flexible, allowing us to learn to use new tools and concepts that human creativity continually produces?
Contact: hg3206(at)nyu.edu
Research interests
How do we adapt quickly and flexibly in complex environments, leveraging prior knowledge? I investigate error-driven learning that relies on three features: internal models for prediction and planning, feedback for continuous refinement, and probabilistic reasoning to infer hidden structure and deal with uncertainty.
I link computational descriptions of learning to mechanistic and algorithmic accounts of how related neural dynamics support the rapid and flexible updating of internal representations on slow (hours) and fast (seconds) timescales.
My approach integrates theoretical and statistical approaches (latent variable modeling, deep learning, network models, dynamical systems) with large-scale neural & behavioral data and circuit perturbations. I am also interested in data-driven systems identification and control, which are critical for closed-loop interrogation of brain-and-behaviour (e.g. with optogenetics or electrical stimulation) by better accounting for the system dynamics.
Experience
Postdoctoral Fellow | New York University, NYC (Current)
Working with Cristina Savin & Christine Constantinople
Investigating multi-region dynamics supporting flexible value-guided decision making in nonstationary environments.
Schmidt Science Fellow | University of Washington, Seattle
Working with Bing Brunton
Studied the role of prior dynamics and feedback controllability in fast motor adaptation. This may helps us better dissect the mechanisms of short-term adaptation versus long-term learning, and guide the design of more learnable brain-computer interfaces as rehabilitative devices.
PhD in Neuroscience | University College London, London
Working with Prof. Angus Silver
Examined dynamical regimes of electrically-coupled inhibitory networks as well as representational transformations of cortico-pontine inputs in the cerebellar cortex, that aid learning in the cortico-cerebellar pathway.
Before that, I studied Biology and Mathematics as an undergraduate at the Indian Institute of Science, Bangalore.
News
Feb 2026: I visited my alma mater after nearly a decade! And gave seminars on my postdoctoral work at Indian Institute of Science and National Centre for Biological Sciences in Bangalore. Thanks Abhilasha and Rishi for the invite!
Nov 2025: Juncal Arbelaiz (at Princeton) and I got a second Catalyst Grant from Schmidt Sciences to develop control-theoretic approaches for neural systems.
Aug 2025: I mentored Jessica, a high-school student, over 6 weeks through the GSTEM program at NYU. Jessica analyzed behavior over millions of trials and hundreds of rats making value-guided decisions to show structured variability across indvidual rats!
May 2025: I joined the labs of Cristina Savin and Christine Constantinople at NYU as a postdoctoral fellow and moved to the wonderful city of New York!