Matthew Parno

Research Physical Scientist
US Army Cold Regions Research and Engineering Laboratory


PhD (2014), Computational Science and Engineering, MIT
Postdoctoral Research (2014-2015), MIT

DiaMonD Research

Uncertainty quantification, Inverse Problems

DiaMonD Collaborations

Youssef Marzouk (MIT)


Matt Parno is a researcher at the Cold Regions Research and Engineering, which is part of the US Army Corps of Engineers Engineer Research and Development Center (ERDC). Matt’s research is focused in computational tools and the application of Bayesian techniques to large scale geophysical problems, with a particular emphasis on the use of optimal transport for efficiently tackling Bayesian problems. His application areas include sea-ice modeling, structural health monitoring, and the sensing of hydrologic systems.

Impact of DiaMonD

Matt was a Ph.D. student and later a Postdoctoral Associate with DiaMonD PI Youssef Marzouk at the Massachusetts Institute of Technology. Matt’s work with DiaMonD led to several advancements involving optimal transport maps in Bayesian inference, including a new Markov chain Monte Carlo algorithm and a probabilistic decomposition of multiscale inverse problems. Matt is now using these techniques on real-world applications at the Cold Regions Research and Engineering Laboratory. He is also continuing to develop transport map approaches for near real-time Bayesian computation. Through DiaMonD, Matt has also developed the MIT Uncertainty Quantification (MUQ) software package, which has become an important tool for many users. Matt continues to develop this package and maintains close ties with DiaMonD PIs at MIT.