Biography

I am an incoming PhD student in Cognition & Perception at New York University. I recently completed my undergraduate degree in Electronics and Communication Engineering from BITS Pilani, India.

I am currently a research intern at Harvard University and Massachusetts Institute of Technology, working with Prof. Sam Gershman. Previously, I did my undergraduate thesis at CCBR, IIT Madras advised by Prof. Partha Mitra. During my undergrad, I was advised by Prof. Veeky Baths and Prof. Basabdatta Sen Bhattacharya under whom I was fortunate to work in collaboration with Max Planck Institute for Psycholinguistics, University of Manchester, and the Human Brain Project. In 2019, I was selected for Google Summer of Code program working for INCF and GeNN Team, University of Sussex.

I am also a core member of the Society for Artificial Intelligence and Deep Learning (SAiDL) as part of which I have been involved in teaching, open-source and organizing community events.

Outside of research, I am a tennis player and have represented my high school and college at various tournaments. I also enjoy watching basketball, hiking and reading about history & philosophy.

Interests
  • Deep Learning
  • Reinforcement Learning
  • Vision
  • Cognition
Education
  • Ph.D. in Cognition & Perception, 2021-2026

    New York University

  • B.E. in Electronics & Communication Engineering, 2017-2021

    BITS Pilani

Experience

 
 
 
 
 
Research Intern
Jun 2020 – Present
Human-level learning in Atari-like video games using a model-based reinforcement learning approach inspired by theories of human cognition.
 
 
 
 
 
Research Intern - Undergraduate Thesis
Jun 2020 – Dec 2020
Developed a deep learning based cellular segmentation model for gigapixel resolution neuroanatomical images.
 
 
 
 
 
Research Assistant
Aug 2019 – Dec 2020 Goa, India
  1. Deep learning to understand how spoken words are visually represented in the brain. Collected EEG data from human subjects listening to spoken audio of numerical digits, and employed deep generative models to construct images of digits purely from EEG signals.
  2. Review paper on the neural and psychological basis for reinforcement learning algorithms.
 
 
 
 
 
Research Assistant
Aug 2019 – Jun 2020 Goa, India
Using a SpiNNaker spiking neural network model to validate experimental results relating to synchrony, periodicity and luminance response of the Lateral Geniculate Nucleus (LGN) in a mouse brain.
 
 
 
 
 
Google Summer of Code Intern
May 2019 – Aug 2019
Developed TensorGeNN, an open source Python library to convert trained deep neural network models to spiking neural networks with minimal losses in performance. Library was benchmarked on MNIST and CIFAR-10 datasets.