معرفی
Deep Ray is an Assistant Professor of Mathematics at the University of Maryland, College Park (UMD), with a joint appointment at the Institute for Physical Science and Technology (IPST). His research focuses on the intersection of numerical analysis and machine learning, particularly in developing robust numerical methods for partial differential equations (PDEs) and enhancing computational efficiency through AI integration. He has expertise in entropy-stable schemes, shock-capturing algorithms, and operator learning for surrogate modeling.
Education and Teaching: Ray has taught advanced courses in computational methods, data science, and numerical analysis at institutions including UMD, University of Southern California (USC), and École Polytechnique Fédérale de Lausanne (EPFL). He has also developed specialized courses like Machine Learning and Computational Physics and contributed to workshops on scientific computing and deep learning.
Research Interests: His work spans scientific machine learning, hyperbolic conservation laws, and physics-informed AI. Recent projects include fusion of wildfire models with satellite data, entropy-stable schemes for fluid dynamics, and Bayesian inference via generative adversarial networks (GANs). He actively collaborates on open-source tools, including a GitHub repository for deep learning tutorials.
Labs and Teams: Ray leads a research group exploring AI-driven numerical methods and interdisciplinary computational challenges. His lab emphasizes practical applications in fluid dynamics, inverse problems, and reduced-order modeling.
