Research Overview

I’m passionate about research at the intersections of Machine Learning, Natural and Artificial Intelligence, Biology and Medicine. For a complete list of my publications and patents, visit my Google Scholar profile.

Here’s a reverse-chronological summary of my work:

Post-doc at Harvard Medical / Kempner Institute (current)

Working with Kanaka Rajan at the intersection of DeepRL-Agents and Neuroscience.

Post-doc at Baylor College of Medicine/Rice University (2023)

Worked with Ankit Patel at the intersection of AI and Biology/Medicine

  1. Understanding Gene Regulatory Network (GRN) evolvability through evolutionary simulations. See extended abstract “Binding affinity distributions drive adaptation in GRN evolution” at ALIFE 2023
  2. Using (SOTA) LLMs to analyze Semi-Structured Healthcare interviews. (Nearing pre-print stage)
  3. Characterizing the evolution of molecular markers of Myelodysplastic Syndrome (MDS) in multi-omic liquid biopsies. (In progress)

Post-PhD Industry (2022)

Research Scientist in Machine Learning in the Probability team team at Meta. Worked on team proojects involving:

PhD (9/2017 - 12/2021)

Project 1: Using deep recurrent reinforcement-learning agents to understand insect plume tracking

Singh et al, Nature Machine Intelligence, Jan 2023. Issue cover feature (artwork by Bingni Brunton) NMI Jan 2023 Cover

#tweeprint:

Presented at:

(More behavior+neural animations) (Preprint)

Project 2: Naturalistic motor neuroscience using long-term human video and neural recordings

Singh et al (Journal of Neuroscience Methods, Apr 2021) (Preprint) (Code, data & more videos)

Right Wrist

#tweeprint on this paper:

This paper’s data generating pipeline and dataset were the foundation for additional papers:

Before PhD and side-projects