About Me

I leverage data science and computational science to design and optimize novel technologies. In my work spanning the materials, biomedical and energy industries, I have been applying a combination of machine learning, deep learning, statistical data analysis, data science, and physics-based computational modeling for technology development, using Python, C/ C++ and MATLAB.

I earned my Ph.D. at MIT, where I conducted research on the mathematical modeling and stochastic simulation of complex flows. This work was aimed at optimizing the design and operation of microfluidic devices for DNA processing. Here is a video of DNA dynamics in a microfluidic device from one of my stochastic simulations:

As a postdoc, I developed and solved stochastic and statistical mechanical models of biomolecules and polymers in C++ and MATLAB, and implemented reduced-order Molecular Dynamics (MD) simulations in LAMMPS, an open source C++ MD code. This work guided the design of high-performance materials. Here is a video of polymer dynamics from one of my MD simulations:

More recently, I completed the MITx MicroMasters Program in Statistics and Data Science. Here is a video of particle dynamics that I developed for a project, this time based on a Gaussian process flow simulation in Python: