Parikshit Pareek

I am an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Roorkee, where I lead the \(\mathcal{P}^2-\)LAB. We work on the probabilistic and computational challenges of operating electrical energy grids, combining \(AI+Optimization+Physics\) to build learning-based surrogates and optimization proxies, targeting methods that hold up under limited data while staying physically feasible and quantifying their own confidence and uncertainty. Our toolkit spans Gaussian Processes, Bayesian Neural Networks, and Prior-Data Fitted Networks, and we increasingly study how and why these models work, along with emerging uses of LLMs in engineering. In parallel, we `sometimes’ examine where quantum computing genuinely can, and cannot, accelerate computations for power grids and broader engineering problems.

Before joining IIT Roorkee, I was a Postdoctoral Research Associate at Advanced Network Science Initiative (ANSI), Applied Mathematics Division (T-5), Los Alamos National Laboratory, USA. I collaborate closely with Sidhant Misra and Deepjyoti Deka on developing applied machine learning tool for power grids and networked systems. I completed my Ph.D. at the School of Electrical and Electronic Engineering, Nanyang Technological University Singapore, advised by of Hung D. Nguyen. Prior to NTU, I earned my M.Tech in Energy Studies from the Department of Energy Science and Engineering at the Indian Institute of Technology Delhi (IITD), where I worked with Ashu Verma. I was also an Affiliate Guest Researcher with the Applied Mathematics Division (T-5) at Los Alamos National Laboratory (LANL), USA.More on my group’s research focuses can be found on research page.

For even more details, please see my CV.