In the Department of Computational and Applied Mathematics at Rice University, research ranges from the design and analysis of mathematical optimization algorithms to modeling complex phenomena in areas such as climate science and real-time surgery to operations research and industrial engineering. Applications include large-scale problems and their applications to science and engineering, compressed sensing, deep learning, and machine learning, addressing problems in porous media and fluid mechanics, and geophysical modeling. We apply this research to a wide variety of fields, including business and finance, oil and gas, energy and healthcare. Recent research projects include cancer detection, modeling the optimal flu vaccine, policy and policing studies, convection modeling and many more.
Our key research areas include: Optimization • Numerical methods for partial differential equations • Numerical linear algebra • Computational neuroscience • Scientific computing • Graph theory • Inverse problems • Data analytics • Physics-based modeling