His ongoing research is to investigate mathematically and numerically a large-scale interval or stochastic optimization problem concerned with uncertainty quantification and their applications in machine learning problems and the optimal control of a control problem in a noisy environment that occurs due to incomplete information (e.g., demand for the product) or unpredictable changes (e.g., changes in the climate) in the system. He also working on the derivation of the concepts of sub derivatives and a technique to solve IOP with non-differentiable objective function. His previous research is based on generalization of derivatives (Directional derivative, Gateaux derivative, and Frechet derivative) for interval-valued functions and their applications in optimization problems with interval-valued objective functions.