Sudhakar Chelikani has extensive experience in Stochastic Modeling, Pattern Recognition, Bayesian Methods, Genetic Algorithms, and Monte Carlo Methods. He has used Machine Learning algorithms to model prepayment in mortgage valuations and perform sentiment analysis on information derived from Twitter. The algorithms were programmed in Matlab and Python.
He supports RiskSpan clients performing value at risk (VaR) analysis for portfolios of financial assets using historical data and Monte Carlo simulations. Sudha also developed and implemented models to perform risk analysis and construct Volatility Surfaces from stocks and options data. He has experience with Machine Learning methods and variance reduction techniques on large datasets to make computations more efficient and robust.
Prior to RiskSpan, Sudha was an Associate Research Scientist Yale University.
Sudha earned a PhD in Physics from Yale University and holds an MPhil in Applied Physics and a BS in Mechanical Engineering.