- 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.
- Python for general purpose programming and ML software development.
- C++ for high performance scientific computing and simulations.
- 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.
- 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.
- Fortran, Matlab, PyTorch, Scikit-learn
- Fluidlearn: A python based package to solve fluid flow PDEs using deep learning techniques.
- Hands on practical ML projects:
- [Space-time-DD](https://github.com/mjayadharan/MMMFE-ST-DD: A C++ based fluid flow simulator using multiscale space-time domain.
- Poroelastic flow simulator: C++ based poroelastic fluid flow simulator using MPI.
- 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.
- 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.
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