About Me

I'm a Ph.D. scholar at the Centre for Atmospheric Sciences, IIT Delhi, where my research revolves around understanding and predicting extreme weather events — especially tropical cyclones and the Indian Summer Monsoon — through the lens of machine learning and climate dynamics. With a background in mechanical engineering and a master's in climate science from IIT Bhubaneswar, I’ve always been intrigued by the powerful, chaotic systems that drive our weather. My current work aims to improve the forecasting of tropical cyclones and monsoon variability by integrating physical principles with data-driven models.

By combining reanalysis data, theoretical frameworks, and AI-based tools, I aim to improve our ability to simulate and forecast cyclone structure, track, and intensity. I’m particularly interested in how global machine learning models represent (or fail to represent) key dynamic features like warm-core structure, asymmetric inflow, and eyewall processes — and how we can correct or enhance these models through physics-informed approaches.

Whether it’s tracing the life cycle of a cyclone over the Bay of Bengal or exploring how AI can strengthen early warning systems, I’m fascinated by the challenge of unraveling the science behind extreme weather. From experimenting with neural networks to interpreting reanalysis datasets or watching storms evolve in real time — I find excitement in every piece of the puzzle.

Outside the lab, I enjoy hitting the gym, exploring new places, relaxing with music, and never saying no to a strong cup of coffee — especially if the conversation’s about gym or crazy weather stories!