I am a computational researcher with extensive background in applied mathematics, optimization, mechanistic modeling, numerical & stochastic simulations, and data analysis & machine learning. • Developed new algorithms and large scale production-level codes for physics-based simulations. • Used predictive modeling and data analytics to estimate optimal system parameters that lead to the discovery of new materials. • Analyzed tons of data obtained from simulations and wrote stand-alone post-processing tools to compute statistical properties such as autocorrelation functions in time series data. • Developed multiple Monte Carlo simulation frameworks (MCMC and Importance Sampling), and implemented advanced methods such as Parallel Tempering (Simulated Annealing) to improve the sampling efficiency. • Extensive experience in collaborating on multi-disciplinary projects and building codes on Github platforms.