91´ÎÔª

Spring 2026

• February 2 – John Bardsley

  John Bardsley – University of Montana

  Uncertainty Quantification for Inverse Problems with Poisson measurement error

  Inverse problems arise when physics-based models are fit to observational data. The unknown parameters in such models are often spatially distributed and become large-scale after discretization. These parameter estimation problems are typically ill-posed, meaning that solutions are unstable with respect to errors in the data. Regularization is the standard approach to mitigate this instability.

  In this talk, I will focus on inverse problems where the data follows a Poisson distribution, a common scenario in imaging applications. I will demonstrate how to implement regularization in the Poisson noise case. Then, building on the connection between inverse problems and Bayesian statistics, I will reformulate the problem within a Bayesian inference framework. This allows for uncertainty quantification through the posterior distribution. I will then present a Markov chain Monte Carlo (MCMC) method for sampling from the posterior, enabling robust parameter estimation and uncertainty analysis.

  February 2, 2026 at 3:00 p.m. Math 103

• March 9 – WiSEN research team

  WiSEN research team – Researchers from WSU, Gonzaga U, UI, Rochester Institute of Tech and UM

  March 9, 2026 at 3:00 p.m. Math 103

• April 20 – David Ayala

  David Ayala – Montana State University

   

  April 20, 2026 at 3:00 p.m. Math 103

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