• I am currently a PhD student at Columbia University with interests in Machine Learning and Computational Neuroscience.

  • Software Engineering Experience

    MathWorks

    Graduate Intern - Engineering Development Group

    Jun 2016 - Aug 2016

    • Wrote machine learning algorithms to predict seizures in epileptic patients to support MathWorks sponsored seizure prediction machine learning challenge hosted by Kaggle.
    • Solved technical cases involving: Mex (C, C++, .NET, FORTRAN) interface, Image Acquisition Toolbox, Parallel Computing Toolbox, GPU based parallel computing, MATLAB Compiler, Arduino interface, Database Toolbox, Data Acquisition Toolbox as well as MATLAB COM interface (actxserver).

    Howard Hughes Medical Institute

    Software Engineer - Scientific Computing

    May 2013 - June 2015

    • Worked as full-time in-house software engineer and developed biomedical research technology platforms.
    • Consulted on various technical projects involving software and hardware architecture.
    • Worked on 2-photon laser scanning microscopy (2PLSM) software, ScanImage that is used over 200 laboratories.
    • Collaborated with experimentalists, design and fabrication team, and software team to develop novel software and instrumentation tools that have allowed researchers to quantitatively analyze complex neural phenomena such as information processing and storage in neural circuits..
  • Research Experience

    Columbia University

    PhD Student - Laboratory for Intelligent Imaging and Neural Computing

    Jan 2017 - Present

    Machine learning (ML) for predicting, classifying and fusing multiple neural and non-neural data streams to develop advanced brain computer interfaces.

    Johns Hopkins University Biomedical Engineering

    Research Assistant - Vision Dynamics Lab

    Sep 2011 - May 2013

    • Developed bio-inspired algorithms for recognizing human movements in videos using hybrid metrics on dynamical systems.
    • Used Microsoft kinect data to extract surfaces from point clouds to compute structure in motion fragment features for activity recognition in videos.

    Technologies Involved : MATLAB, C, Linux, Sun Grid Engine

    University of Oxford

    Research Intern - Oxford Medical School

    Dec 2011 - Feb 2012

    • Developed a software platform for quantitative measurement of axonal loss in neurodegenerative diseases such as multiple sclerosis.
    • New analysis method allowed physicians to quantitatively analyze axonal loss in the spinal cord across pathologies. Technique reduced tissue analysis time from weeks to a few hours making histological analysis much more efficient.

    Technologies Involved: Java - ImageJ/FIJI, MATLAB

    Johns Hopkins School of Medicine

    Undergraduate Research Assistant

    Jul 2011 - Dec 2011

    Mutations in Fig4 have been observed in both ALS (amyotrophic lateral sclerosis) and CMT 4J (charcot marie tooth disease type 4J) patients. These mutations have been associated with accumulation of large vacuole like structures in CNS and PNS neurons in patients with CMT4J and in ALS, TDP-43 inclusion bodies have been observed in patients. We used drosophila as a model system, to investigate the physiological role of Fig4 and the molecular mechanisms by which these disease-associated mutations cause the accumulation of intracellular vacuoles in CMT4J and ALS patients.

    Mayo Clinic

    Summer Research Fellow

    May 2011 - Aug 2011

    Expanded in-house MRI analysis toolkit; work included developing models of neural atrophy for MRI analysis at Mayo clinic.

    Technologies Involved: MATLAB and freesurfer segmentation toolkit

  • Projects

    AnamneVis

    High costs, lack of speed, non-intuitive interfaces, and inefficient, fragmented display of patient information have hindered the adoption of the Electronic Health Record (EHR). Critical factors inhibiting adoption of the EMR include the time spent by the health care providers (HCP) in accessing and also documenting patient information during clinical encounters. We describe an emerging visual analytics system dedicated to clinical encounters in emergency room scenarios. It unifies all EMR information fragments into a single interactive visual framework, controlled by voice and touch, in which physicians can conduct diagnostic reasoning tasks in a direct data and information centric manner. We illustrate our system by ways of a typical clinical scenario and point out directions for future research and development.

    STAND-Map

    The common neuro degenerative pathologies underlying dementia are Alzheimer’s disease (AD), Lewy body disease (LBD) and Frontotemporal lobar degeneration (FTLD). Aim of this project was to identify patterns of atrophy unique to each of these diseases using antemortem structural-MRI scans of pathologically-confirmed dementia cases and build an MRI-based differential diagnosis system. We created atrophy maps using structural-MRI and applying them for classification of new incoming patients is labeled Differential-STAND (Differential-diagnosis based on STructural Abnormality in NeuroDegeneration).

    ScanImage

    Over the last decade the discovery of fluorescent proteins has revolutionized in-vivo imaging in neuroscience. Fluorescent proteins combined with transgenic techniques have allowed researchers to quantify and analyze behavior of various complex cortical circuits across several organisms at Janelia. 2-photon excitation laser scanning microscopy (2PE) has especially aided this endeavor. 2PE has allowed for acquisition of high-resolution, high-sensitivity fluorescence microscopy image’s in intact neural tissue.

     

    ScanImage (3.x, 4.x) is open source customizable software written for 2PE laser scanning microscopes to meet the agile needs of researchers at Janelia and elsewhere.

    Optogenetics Acquisition System

     

    ReachTask is software for optogenetic stimulation and data acquisition. It delivers a stable, extensible, software solution for optogenetic stimulation and supports options for integration with 2-photon imaging.

     

  • Education

    Johns Hopkins University

    BA Systems Neuroscience

    MS Biomedical Engineering

    MS Computer Science

    2011 - 2016

    Columbia University

    PhD Neuroengineering

    2016 - 2021