- I am an applied mathematician with research interests spanning a wide range of quantitative scientific fields. My interests include developing numerical methods for data-driven inverse problems involving differential equations, high-performance solvers for PDEs, and financial modeling. I have a Ph.D. in Mathematics with a focus on numerical analysis and high-performance computing for PDEs. I worked on Wall Street as a quant after completing my PhD before deciding to pursue a career in research.
- 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.
- Inverse PDE.
- Data Driven Differential Equation Discovery.
- 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
- DaeFinder: DaeFinder is a Python package designed to discover Differential Algebraic Equations (DAEs) from noisy data using sparse optimization framework.
- 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.