I'm a PhD student at UC Berkeley exploring the intersection of computer science and neuroscience (here's my CV). My research interests are in learning algorithms/statistical techniques for (1) neural decoding, (2) mapping the brain, and (3) computational models of learning. I'm also developing a set of notes on AI/neuroscience.

Research

Nov 28, 2017 - Our paper on W-SIMULE: fMRI connectivity analysis will appear at NIPS 2017 Workshop on Advances in Modeling and Learning Interactions from Complex Data: Singh, Wang, & Qi, 2017.

Oct 24, 2017 - Our new paper on the energetic cost of linearizing synaptic integration has been accepted: Morel, Singh, & Levy, 2017.

Sept 13, 2017 - We’ve released our recent work on fMRI connectivity analysis on the ABIDE dataset: Singh, Wang, & Qi, 2017 on arXiv.

Sept 9, 2017 - We’ve released our new work on 3D connectome reconstruction: Funke, Tschopp, Grisaitis, Singh, Saalfeld, & Turaga, 2017 on arXiv.

July 13, 2017 - We’ve published a new paper on the feasibility of interpulse-interval coding: Singh & Levy, 2017.

Work

  • Fall 2017 - Present

    UC Berkeley

    PhD student in Computer Science advised by Bin Yu.

  • Summer intern working on unsupervised pretraining of CNNs for semantic segmentation at Facebook, Menlo Park office.

  • Fall 2016 - Spring 2017

    UVA Yanjun Qi Research Lab

    Contributed to development of novel weighted-L1, multi-task Gaussian graphical models

  • Summer 2015, Winter 2015, Summer 2016

    HHMI Srini Turaga Lab

    Contributed to enhanced ML implementations for neural image segmentation

  • January 2015 - Fall 2016

    UVA William Levy Lab

    Simulated stochastic neurons to determine mutual information, variability, energy efficiency, and threshold

  • Simulated extracellular neural recordings via Neurocube Matlab scripts

  • Summer 2013 - Spring 2014

    RII

    Developed a web application + Android app for simultaneous task coordination + increasing the data storage capacity of QR codes.