Hey 👋, I'm Manu

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.

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.