Rate My Professor Paresh Date

Date's research specializations center on nonlinear state estimation, with applications in tracking and finance, alongside financial portfolio optimization and derivative pricing. His academic interests extend to algorithms for latent state estimation or filtering in nonlinear time series, particularly for forecasting and risk measurement in mathematical finance, financial modeling in microfinance, and optimization problems in power systems. He is the author of the book 'Nonlinear estimation: methods and applications with deterministic sample points,' published by Taylor & Francis in 2019. Key publications include 'A Machine Learning Approach for Micro-Credit Scoring' (Risks, 2021, with A. Ampountolas et al.), 'Pricing and hedging wind power prediction risk with binary option contracts' (Energy Economics, 2023, with J. Thakur et al.), 'Stabilization and Optimal Control for Discrete-time Markov Jump Linear System with Multiplicative Noises and Input Delays: A Complete Solution' (IEEE Transactions on Automatic Control, 2023, with C. Han et al.), 'Polynomial Chaos Kalman Filter for Target Tracking Applications' (IET Radar, Sonar and Navigation, 2022, with K. Kumar et al.), and 'Modelling and forecasting of exchange rate pairs using the Kalman filter' (Journal of Forecasting, 2024, with J. Maunthrooa). He has secured research funding, including an EPSRC overseas travel grant (EP/L019477/1, 2014), a London Mathematical Society UKFin+ grant (2021), and a recent grant for news sentiment analysis for sustainable investing (2025).