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  • University of Pittsburgh
  • Pittsburgh

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mjayadharan/README.md

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Hey 👋, I'm Manu


LinkedIn  Gmail  ResearchGate 


My background in a nutshell.

  • I am a computational scientist with expertise in numerical analysis, high performance computing, and scientific machine learning. I am interested in application of computational and stastistical learning methods to a wide range of scientific fields including natural sciences, geosciences, physical systems etc. I currently work as a Quantitative Research Analyst for the Citigroup's Credit Quant group in NYC.
  • I have experience in developing and implementing novel algorithms to solve multiphysics CFD problems, using both data-driven deep learning techniques and classical mixed finite element methods (FEM).
  • Currently, I work as a credit quant specializing in pricing and risk management of credit derrivatives in the risk-neutral framework, specifically in LATAM emerging markets.
  • I love working on new challenging models and implementing them, while collaborating with others.
  • Always looking for new collaborators and interesting projects.

Languages I work with:

  • Python for general purpose programming and ML software development.
  • C++ for high performance scientific computing and simulations.

Mathematical Skills:

  • Numerical Analysis.
  • HPC Parallel computing.
  • Advanced Probability, Statistics.
  • ML,DL.
  • Biocomputing.
  • Math Finance and Stochastic Calculus.
  • Risk Neutral Pricing and Hedging of Credit derrivatives.
  • Bonds, CDS, XCCY swaps.
  • CFD Computational Fluid Dynamics.

Software packages I currently use:

  • ML/ Data Science: Numpy, Pandas, Jupyter, Keras-Tensorflow2
  • Visualization: Matplotlib, ParaView, gnuplot.
  • Scientific computing: deal.II, FreeFem++, phoenix.
  • My favorite editors and IDEs: Eclipse, Jupyter notebook, Colab, Emacs.

Languages and packages I used to work with:

  • Fortran, Matlab, PyTorch, Scikit-learn

Checkout some of my recent projects and preprints:

Machine Learning:

  1. Fluidlearn: A python based package to solve fluid flow PDEs using deep learning techniques.
  2. Hands on practical ML projects:

High performance scientific computing:

  1. [Space-time-DD](https://github.com/mjayadharan/MMMFE-ST-DD: A C++ based fluid flow simulator using multiscale space-time domain.
  2. Poroelastic flow simulator: C++ based poroelastic fluid flow simulator using MPI.

Other open-source contributions:

  1. FEM package deal.II: Most of the HPC packages I have written uses deal.II and I am also one of the contributors to this popular open-source FEM package.

Recent preprint:

  1. Parallel computations to solve poroelastic flow: M. Jayadharan, E. Khattatov, I. Yotov, Domain decomposition and partitioning methods for mixed finite element discretization of the Biot system of poroelasticity, arxiv math.NA, 2010.15353.

Thank You-🙏🏼

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  1. FluidLearn FluidLearn Public

    Software to solve PDEs and estimate physical parameters governing fluid flow using Deep learning techniques.

    Jupyter Notebook 4 4

  2. MMMFE-ST-DD MMMFE-ST-DD Public template

    Fluid flow simulator using MFEM and multiscale space-time sub-domains.

    C++ 3 1

  3. ML_mini_projects ML_mini_projects Public

    Repository containing several mini projects, implementing small scale ML training models using scikit-learn, tensorflow and kern. Mainly for the purpose of education and fun.

    Jupyter Notebook 1 1

  4. BiotDD BiotDD Public

    Repository containing deal.ii implementation of domain decomposition for Biot system of poroelasticity

    C++ 1 1

  5. Biological-VTNNS Biological-VTNNS Public

    Code archive of Variable Topology Neural Network Simulator (VTNNS) based on LIF model.

    Fortran

  6. dealii/dealii dealii/dealii Public

    The development repository for the deal.II finite element library

    C++ 1.4k 757