EnKF Workshop 2023: Adaptive localization in nonlinear ensemble transport filtering

EnKF Workshop 2023Ramgraber, M., and Marzouk, Y. (2023)

This talk complements my poster at the EnKF Workshop 2023 of the same name. In brief, we have developed an efficient map adaptation strategy which automatically identifies parsimonious triangular maps for ensemble transport filtering. This adaptation strategy can also detect conditional independence properties, which permits an efficient form of localization. Here, I explain the idea behind this method, and discuss preliminary results for nonlinear ensemble transport filtering in the chaotic Lorenz-96 system.

SIAM Annual 2022: An Introduction to Sampling with Measure Transport

2022 SIAM Annual Meeting, PittsburghParno, M., Baptista, R., Ramgraber, M., Rubio, P.B., and Marzouk, Y. (2022)

This is a recording of the presentation I gave at SIAM Annual 2022, as part of the session MS65 Tutorials for Students: Accessible Introductions to Active Research Areas. I provided the theoretical introduction, and Matthew Parno guided students through a hands-on exercise using the newly released MPart toolbox afterwards. The notebooks for the exercises can be accessed here. This talk discusses some of the applications of triangular transport methods, and covers most of the basic concepts, from the properties of the triangular structure, across different optimization strategies, to methods which allow us to overcome the curse of dimensionality.

ISDA 2022: Nonlinear Ensemble Transport Smoothing

International Symposium on Data Assimilation 2022, Fort CollinsRamgraber, M., Baptista, R., McLaughlin, D., and Marzouk, Y. (2022)

In this talk - a shortened version of my presentation at SIAM UQ - I present our research on nonlinear ensemble transport smoothing at the International Symposium for Data Assimiliation 2022 in Fort Collins, Colorado, USA. Since this was one of the first conferences completely in-person again, no talks were recorded at the conference venue. This is a re-recording of the presentation.

SIAM UQ 2022: Ensemble smoothing with transport maps

SIAM Uncertainty Quantification 2022, AtlantaRamgraber, M., Baptista, R., McLaughlin, D., and Marzouk, Y. (2022)

In this talk, I advocate for the use of transport maps for ensemble smoothing, providing a brief introduction to transport methods, then demonstrating their application for sequential smoothing. Due to technical difficulties on-site, the live talk was unfortunately not recorded. This is a re-recording of my presentation after the conference.

AGU Fall Meeting 2021: Transport Methods for Nonlinear Smoothing

American Geophysical Union Fall Meeting 2021, New OrleansRamgraber, M., Baptista, R., McLaughlin, D., and Marzouk, Y. (2021)

This hybrid poster presents intermediate progress of my work on nonlinear smoothing with transport methods. We discuss two different smoothing strategies (joint-analysis and backward smoothers), often applied as Kalman-type algorithms. We then introduce transport methods as a pathway for nonlinear generalization of these smoothers. We demonstrate the efficacy of the resulting algorithm with the Lorenz-63 test case.

Interview for the Prix Léon Du Pasquier et Louis Perrier 2021

University of NeuchâtelRamgraber, M. (2021)

My dissertation has been nominated by my thesis committee and ultimately won the Prix Léon Du Pasquier et Louis Perrier. This is an award bestowed anually to the best dissertation within the faculty of science at the University of Neuchâtel, Switzerland.

 The award is endowed with a prize money of 2000 CHF. In the corresponding interview, I was asked to answer the questions of what motivated me to pursue a PhD, how I would explain my research to laymen, and what I am currently doing during my postdoc at MIT.

Doctorate: Open Defense

University of NeuchâtelRamgraber, M. (2020)

My open doctoral defense at the University of Neuchâtel, recorded during the COVID-19 quarantine in December 2020. My attempt to explain non-Gaussian parameter inference and data assimilation to a general audience through the use of space rabbits.