Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
 Gaurav Dhir

Gaurav Dhir - Research Assistant

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supervisor Dr. Mike Kirby

Background

Educational Background:
  • PhD in Scientific Computing, University of Utah, Salt Lake City, USA: August 2022 - Present
  • MSE in Aerospace Engineering, University of Michigan, Ann Arbor, USA: Sept 2017 - May 2019
  • BE in Aerospace Engineering, Punjab Engineering College, Chandigarh, India: August 2013 - May 2017

Experience:
  • Research Assistant, Department of Computing, University of Utah: Aug 2022 - Present
  • Member of Technical Staff, Cohesity: Feb 2022 - May 2022
  • Software Verification Engineer, Joby Aviation: March 2021 - Jan 2022
  • Software Engineer in Test, MathWorks: May 2019 - Dec 2020
  • Scribe, Department of Climate and Space Science, University of Michigan: June 2018 - Aug 2018
  • Research Assistant, Department of Mechanical and Industrial Engineering, University of Toronto: May 2016 - August 2016
  • Research Assistant, Department of Aerospace Engineering, Indian Institute of Technology Bombay: Jan 2016 - May 2016
  • Research Assistant, Department of Applied Mechanics, Indian Institute of Technology Delhi: May 2015 - July 2015

Current Responsibilities

  • Pursuing PhD in Scientific Computing, Engaging in Research Projects and Coursework
  • Gathering, organizing and analyzing data through literature searches, code development, recording reviews and using related non-lab methods for studies, publications and other research related areas.

Research Interests

Topics in Computational Math involving the following areas:
  • Decomposition Methods, Probabilistic ML with interests in Neural Networks and applicability within Uncertainty Quantification
  • Topics in Numerical Analysis involving High Order Spectral Element Methods, Multigrid methods and Hanging Nodes
  • Reduced Order Modeling Techniques involving Kernel PCA, Gaussian Processes and Spectral POD
  • Topics in High Performance GPU Computing involving Matrix-Free Finite Element Solvers and the Lattice Boltzmann Method

Other interests involving studying mathematical analysis (real and functional analysis) and skills in software test automation with languages like Python and Julia.