Arash Sarshar

Arash Sarshar is an Assistant Professor at the Computer Engineering and Computer Science Department of California State University, Long Beach. Previously, he served as a postdoctoral associate in the Department of Computer Science at Virginia Tech, where he also earned his Ph.D. His academic background includes Master's and Bachelor's degrees in Electrical Engineering.

• Education: Ph.D. in Computer Science (Virginia Tech), M.S. and B.S. in Electrical Engineering.

Sarshar's research focuses on the intersection of machine learning and numerical methods for scientific simulations. He explores physics-informed neural networks, deep operator learning, and time-stepping strategies to enhance computational models, particularly for partial differential equations (PDEs) and stiff systems. His work also extends to applying data science techniques for public health and policy analysis, including studies on diabetes lifestyle interventions.

His recent publications highlight trends in PDE-constrained optimization, Bayesian parameter estimation, multirate integration schemes, and parallel computing for numerical methods. Key topics include physics-informed machine learning, time-stepping algorithms, and computational modeling for multiphysics problems.