Arunesh Mittal
Arunesh Mittal
Industry Experience
Amazon AWS AI
Machine Learning Researcher Intern
Jul 2019 - Dec 2020
- Intern in the AWS AI team at Amazon Research in Seattle and New York.
MathWorks
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
Graduate Research Assistant
Jan 2017 - Present
Working on probabilistic machine learning methods for high dimensional time-series data with applications to neural data.
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.
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.
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.
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/MS/MS Computational Neuroscience, Biomedical Engineering, Computer Science
2011 - 2016
Columbia University
PhD Machine Learning
2016 - 2021
