Shashank Subramanian is a deep learning engineer with research interests in the intersection of high-performance scientific computing, deep learning, and physical sciences. His current research involves developing large-scale neural network surrogates for high resolution, data-driven weather and climate simulations and on algorithmic developments to understand and improve physics-based neural operators.
Prior to joining NERSC, Shashank received his PhD in Computational Sciences and Applied Mathematics from UT Austin in 2021. His doctoral research focused on integrating mathematical models of cancer growth with patient-specific imaging data using efficient parallel algorithms, optimization methods, and high-performance computing software to improve personalized medicine. Shashank received his Bachelors in Aerospace Engineering from the Indian Institute of Technology Madras in 2016.