Saswata completed his M.Sc. in Chemistry at the Indian Institute of Technology Madras, where he first developed an interest in theoretical and computational chemistry. In 2014, he moved to The Ohio State University for his Ph.D. studies with Prof. John M. Herbert, focusing on the development of quantum chemistry methodologies. During graduate school, he formulated ab initio approaches for vibrational spectroscopy techniques such as resonance Raman and femtosecond stimulated Raman spectroscopy, enabling insights into the vibrational dynamics of complex systems. In addition, he introduced geometry optimization techniques for enzyme cluster models with soft potentials and developed optimized integration grids for modern density functionals, ensuring accurate yet efficient DFT calculations.

After completing his Ph.D., he joined Prof. Francesco Paesani’s group at the University of California San Diego, as a postdoctoral researcher. There, he combined his background in quantum chemistry with machine learning and data-driven many-body techniques to develop advanced methodologies for multiscale simulations. His postdoctoral work led to machine-learning models trained on data beyond traditional Kohn-Sham DFT, achieving quantitative agreement with experimental results across diverse aqueous phases. These advancements opened new avenues for modeling non-reactive and reactive processes in aqueous-phase chemistry, both in bulk and at interfaces.

Saswata continues to be passionate about the development of accurate and efficient theoretical methods, particularly those that bridge fundamental quantum mechanics with multiscale chemical simulations.

Outside of research, Saswata is passionate about soccer and chess, and enjoys exploring a variety of cuisines.