Jack Xin is a Chancellor's Professor at the University of California, Irvine, specializing in Applied and Computational Mathematics. His research focuses on machine learning, deep learning, optimization, and fluid dynamics, with applications in neural networks, turbulence modeling, and computational biology. He leads interdisciplinary projects involving stochastic algorithms, particle methods, and numerical analysis of partial differential equations. His work integrates advanced mathematical techniques with computational tools to address challenges in data science, combustion theory, and disease modeling. Notable contributions include developing efficient attention mechanisms for transformers, analyzing turbulent burning velocity laws, and creating particle-based algorithms for chemotaxis systems. Jack Xin has secured grants such as the NSF ATD program for robust spatio-temporal forecasting and collaborative research on deep learning algorithms. His research emphasizes computational efficiency, theoretical rigor, and real-world applications in fields ranging from epidemiology to climate science. His team explores cutting-edge methods in compressed sensing, neural network quantization, and graph-based models, aiming to bridge gaps between mathematical theory and practical machine learning systems.
