- Computational and analytical aspects of geophysical problems dealing with shallow mass
- Development of adaptive mesh refinement strategies for geophysics
- Design and implementation of wave propagation software
Kyle Mandli is 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.
Ph.D., University of Washington, Applied Mathematics, June 2011
M.Sc., University of Washington, Applied Mathematics, June 2005
B.S. Applied Mathematics, Engineering and Physics, University of Wisconsin, May 2004
"Visualizing Uncertainties in a Storm Surge Ensemble Data Assimilation and Forecasting System", Thomas Hllt, M. Umer Altaf, Kyle T. Mandli, Markus Hadwiger, Clint N. Dawson, and Ibrahim Hoteit. Natural Hazards 120 (2015).
"Uncertainty quantication and inference of Mannings friction coecients using DART buoy data during the Thoku tsunami." Sraj, I., Mandli, K. T., Knio, O. M., Dawson, C. N. and Hoteit, I. ,Ocean Modelling 83, 8297 (2014).
"Adaptive Mesh Renement for Storm Surge", Kyle T. Mandli, Clint N. Dawson, Ocean Modelling, Volume 75, March 2014, Pages 36-50.
"Forestclaw: Hybrid forest-of-octrees AMR for hyperbolic conservation laws", Carsten Burstedde, Donna Calhoun, Kyle Mandli, and Andy R. Terrel. Accepted to ParCo 2013.
"A Numerical Method for the Multilayer Shallow Water Equations with Dry States", Kyle T. Mandli. Ocean Modelling 72, 8091 (2013).
ManyClaw: Slicing and dicing Riemann solvers for next generation highly parallel architectures", A.R. Terrel and K. T. Mandli, TACC-Intel Symposium on Highly Parallel Architectures (2012).
"PyClaw: Accessible, Extensible, Scalable Tools for Wave Propagation Problems", David I. Ketcheson, Kyle T Mandli, Aron Ahmadia, Amal Alghamdi, Manuel Quezada, Matteo Parsani, Matthew G. Knepley, and Matthew Emmett. SIAM J. Sci. Comput., 34(4), C210C231, (2012).
"The GeoClaw software for depth-averaged flows with adaptive renement", M.J. Berger, D.L. George, R.J. LeVeque and K. T. Mandli. Advancement in Water Resources Volume 34, Issue 9, Pages 1195-1206, September 2011.
"Finite Volume Methods for the Multilayer Shallow Water Equations with Applications to Storm Surges", Ph.D. Thesis, July 2011.
"PetClaw: A Scalable Parallel Nonlinear Wave Propagation Solver for Python", with Amal Alghamdi, Aron Ahmadia, David I. Ketcheson, Matthew G. Knepley, and Lisandro Dalcin. 19th High Performance Computing Symposium, 2011.