About Me

I am a PhD candidate in Electrical & Computer Engineering at Georgia Tech, largely interested in characterizing learning and intelligence in artificial and biological systems. Towards these ends, I spend my time studying both machine learning and theoretical + computational neuroscience, with a healthy smattering of many topics in applied mathematics. I am primarily advised by Dr. Hannah Choi and co-advised by Dr. Chris Rozell.

Ongoing Research

My research broadly leverages structure (geometrical and topological) in the representations and architectures of artificial & biological neural networks so as to render them more interpretable and thereby discover their governing principles. Some ideas that I actively think about in these contexts are:

• Sparse, low-rank, and low-dimensional approximations of data.
• The effects of regularization & architecture on representations learnt by neural networks.
• Disentangling of data manifolds, the quantification of representations that live on them, their evolution across space-time, and the changes that different perturbations bring about in them.
• Generalizability and potential for transfer of representations across different tasks and domains.

My long-term goals of understanding learning + intelligence combined with my facility for math & engineering have led to the following (somewhat) more tangible goals that I try to actively contribute to with my research:

• Development of mathematical models and computational methods that effectively characterize as well as elucidate changes in structure, organization, and information processing in the brain across different stages of neurological age, health, and development,
• Development of better ML/AI systems that require less supervision and/or data, learn progressively, and are more amenable to changes in task structure (along with supporting foundational theory or guarantees whenever possible!), and more recently,
• Tackling AI Alignment, i.e., trying to ensure deep learning models as and when deployed, do what we want/intend for them to do.

Recent-ish News

• May 2024: The preprint of our work studying the architectural biases of the canoncial cortical microcircuit is now up on bioRxiv!
• March 2023: Our work on characterizing the architectural biases of the canonical cortical microcircuit was a chosen for a talk at COSYNE ‘23 in Montréal!
• July 2022: Our paper on leveraging persistent homology to theoretically explain the empirical successes of iterative magnitude pruning has been accepted to the TAG in ML workshop at ICML 2022, with a spotlight presentation! It will be published in the PMLR volume Topology, Algebra, and Geometry in Learning, and is also being presented at the Sparsity in Neural Networks Workshop 2022. This work is a joint effort with my good friend, Jakob Krzyston.
• June 2022: I will be spending my summer in NYC at the Flatiron Institute’s Center for Computational Neuroscience, working with Dr. SueYeon Chung!
• October 2021: New preprint on detecting attacks on DNNs using robust statistics up on arXiv! Joint work with Chandramouli Amarnath.

Interests & Hobbies

Machine Learning: Unsupervised/self-supervised learning, dimensionality reduction & manifold learning, metric/similarity learning, and learning with structured sparsity.
Mathematics: Matrix & tensor decompositions, column subset selection, low-rank approximation, metric embeddings, convex geometry, optimization, group & representation theory, differential geometry & topology, and information geometry.
Neuroscience: Neural (i.e., population and sparse) coding, predictive coding, synaptic plasticity & learning rules, models of brain structure & organization, and connectomics.

Outside of academic and scientific pursuits, my hobbies include reading 📖, listening to + studying classical music 🎼, watching + playing racquet sports 🎾, trying (and often failing) to keep up with cool movies + TV shows 🎥, and solving every Rubik’s cube variant 🎲 I can get my hands on.

I am incredibly fond of cats 🐈, enjoy history of almost any kind 📜, still identify as an ardent Federer fan 💜, and remain a Bombay kid 🌏🏠👶 at heart for life. 🌈.

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