NeuroAI Scholar at CSHL

⚠️ On the job market

I am on the faculty job market this fall! I am primarily looking for electrical and/or computer science departments with collaborations in neuroscience. If my research interests you, please reach out!

Welcome! I'm Kyle, a NeuroAI Scholar at Cold Spring Harbor Lab in Long Island, NY. I completed my PhD studying non-von Neumman computing under Dr. Mikko Lipasti at University of Wisconsin-Madison. My research is broadly organized into three areas:

  1. Applying ML models to neuroscience datasets: Neuroscience data is complex and high-dimensional but contains far fewer and noiser samples than standard ML benchmarks. I believe using existing ML methods in these contexts can help answer scientific questions while also providing unique insight into model failure modes. Currently, I collaborate with the Hou Lab at CSHL to apply 3D pose-tracking models to study facial expressions in mice.

  2. Building ML models inspired by neuronal development and evolution: Deep neural networks are trained on billions of data samples, while biological networks are able to learn within a few training examples by leveraging innate priors encoded in an organism’s genome via evolution. The importance of structural priors is well-known in ML, but a scalable mechanism for learning useful priors does not exist. Biology makes use of two processes for finding innate structure—neuronal development, which translates the information encoded in the genome into a functional network of neurons, and evolution, which mutates the genome to produce better networks. I study these processes through a computational lens to build AI models endowed with prior structure. In turn, these models are more sample-efficient and consume less energy to train.

  3. Studying neural processing using computer science tools: Even in a single brain, various sub-circuits employ different computing paradigms, memory systems, information encoding schemes, and signaling strategies. This diversity allows organisms to balance functional specialization and adaptability under various resource constraints. I study these circuits through the lens of a computer scientist to understand how to build heterogenous and distributed computing systems. I believe such systems will be necessary in the post-Moore's law era of computing.

Quick links: [ CV | Research Statement ]

Contact info: [ daruwal /at/ cshl.edu | GitHub | "butterfly" Bluesky | Mastodon ]

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