Du & Mandli: New Applied Math Faculty Members
Qiang Du is the Fu Foundation Professor of Applied Mathematics. He is also an affiliated member of the Institute for Data Sciences. Professor Du earned his Ph.D. in Mathematics (1988) from Carnegie Mellon University, after which he has held faculty positions at University of Chicago, Michigan State University, Iowa State University, and Hong Kong University of Science and Technology. Dr. Du was most recently the Verne M. Willaman Professor of Mathematics and Professor of Materials Science and Engineering at Penn State University. Recognitions for Dr. Du’s work include the Frame Faculty Teaching Award (1992) at Michigan State University, the Liberal Arts and Sciences Award for outreach/extension (2000), the Feng Kang prize in scientific computing (2005), the Eberly College of Science Medal (2007) from Penn State University, and his selection as a 2013 SIAM Fellow for contributions to applied and computational mathematics with applications in materials science, computational geometry, and biology. His research interests are numerical analysis, mathematical modeling and scientific computation with selected applications in physical, biological, materials, data and information sciences. Personal homepage: http://www.columbia.edu/~qd2125/
Kyle Mandli is a new Assistant Professor of Applied Mathematics. He comes to Columbia from the University of Texas at Austin where he was a Research Associate at the Institute for Computational and Engineering Sciences working in the computational hydraulics group. He received his Ph.D. in Applied Mathematics in 2011 from the University of Washington studying multi-layered flow as it applies to storm-surge simulation. His research interests involve the computational and analytical aspects of geophysical shallow mass flows such as tsunamis, debris-flow and storm-surge. This also includes the development of advanced computational approaches, such as adaptive mesh refinement, leveraging new computational technologies, such as accelerators, and the application of good software development practices as applied more generally to scientific software.