Portrait of Prof. Kyle Mandli  

Kyle T. Mandli

Research Scientist

288 Engineering Terrace
Mail Code 4701

Tel(212) 854-4485
Fax(212) 854-8257

Research Interests

Numerical analysis, computational science, hyperbolic partial differential equations, uncertainty quantification, geophysical fluid dynamics, shallow mass flows such as storm surge and tsunamis.

This research 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.

Dr. Mandli’s research is primarily concerned with how to apply finite volume methods, adaptive mesh refinement, and other computational science approaches to a variety of geophysical flow problems, including storm surges and tsunamis. These flows all have shallow water characteristics, which allow us to apply the same general methods to many different flows. His research specifically revolves around two main ideas: the first is to adapt current models so they can easily be solved in a depth averaged context, and the second is to implement robust and efficient solvers for the simulation of these flows. Additionally, he works to ensure that the solvers are accessible to the people who need them, e.g. debris flow modelers, field geologists, and others who are responsible for hazard preparation and response. Consequently, he adheres to good software development practices, such as literate programming and to design frameworks that are easy to extend and maintain.

Mandli received a BS in applied mathematics, engineering and physics from the University of Wisconsin in 2004 and a PhD in applied mathematics from the University of Washington in 2011. Prior to Columbia, he was a research associate at the Institute for Computational and Engineering Sciences at the University of Texas at Austin, working in the computational hydraulics group.


  • Research associate, University of Texas at Austin, 2013-2014
  • JTO Fellow, University of Texas at Austin, 2012-2013
  • ICES Postdoctoral Fellow, University of Texas at Austin, 2011-2012


  • Assistant/Associate professor of applied physics and applied mathematics, Columbia University, 2014-2023


  • All citations
  • “Baysian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate.” Giraldi, L., Le Maˆıtre, O. P, Mandli, K.T., Dawson, C.N., Hoteit, I., Knio, O.M. Comput Geosci 50, 117 (2017).
  • “Clawpack: building an open source ecosystem for solving hyperbolic PDEs”, Kyle T. Mandli, Aron J. Ahmadia, Marsha J. Berger, Donna A. Calhoun, David L. George, Yiannis Had-jimichael, David I. Ketcheson, Gray I. Lemoine, and Randall J. LeVeque. PeerJ Comput. Sci. 2, e68 (2016).
  • “Visualizing Uncertainties in a Storm Surge Ensemble Data Assimilation and Forecasting Sys-tem”, Thomas Hllt, M. Umer Altaf, Kyle T. Mandli, Markus Hadwiger, Clint N. Dawson, and Ibrahim Hoteit. Natural Hazards 120 (2015).
  • “Uncertainty quantification and inference of Mannings friction coefficients 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 Refinement for Storm Surge”, Kyle T. Mandli, Clint N. Dawson, Ocean Mod-elling, 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 architec-tures”, 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 refinement”, 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.