Shashank Subramanian
Computer Systems Engineer 3
National Energy Research Scientific Computing Center (NERSC)
Science Engagement & Workflows Dept.
Data & AI Services Group
Shashank Subramanian is a deep learning engineer at NERSC 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.
Recent Publications
Ensemble Inversion for Brain Tumor Growth Models With Mass Effect.
Authors: Subramanian, S; Ghafouri, A; Scheufele, K; Himthani, N; Davatzikos, C; Biros, G
April 2023, IEEE Transactions on Medical Imaging
Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan
Authors: Scheufele, K; Subramanian, S; Biros, G
January 2021, IEEE Transactions on Medical Imaging
Where did the tumor start? An inverse solver with sparse localization for tumor growth models
Authors: Subramanian, S; Scheufele, K; Mehl, M; Biros, G
April 2020, Inverse Problems
Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect
Authors: Subramanian, S; Gholami, A; Biros, G
August 2019, Journal of Mathematical Biology