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publications
Better to marry renewables than to burn fossil fuels in border states
Published in The Journal of Undergraduate Mathematics and its Applications, 2018
Recommended citation: Aimee Maurais, Arianna Krinos, "Better to marry renewables than to burn fossil fuels in border states." The Journal of Undergraduate Mathematics and its Applications, 2018.
Computational Tools for Bayesian Inverse Problems with Python Implementations
Published in Undergraduate Honors Thesis, 2019
Recommended citation: Aimee Maurais, "Computational Tools for Bayesian Inverse Problems with Python Implementations." Undergraduate Honors Thesis, 2019.
Nuggets of Wisdom from Destinations Doomed Due to Dragon Dominion
Published in The Journal of Undergraduate Mathematics and its Applications, 2019
Recommended citation: Aimee Maurais, Arianna Krinos, "Nuggets of Wisdom from Destinations Doomed Due to Dragon Dominion." The Journal of Undergraduate Mathematics and its Applications, 2019.
Parameter and Uncertainty Estimation for a Model of Atmospheric CO$_2$ Observations
Published in SIAM Undergraduate Research Online, 2019
Recommended citation: Aimee Maurais, Arianna Krinos, "Parameter and Uncertainty Estimation for a Model of Atmospheric CO$_2$ Observations." SIAM Undergraduate Research Online, 2019.
Multifidelity Covariance Estimation Three Ways
Published in Master's Thesis, 2022
Recommended citation: Aimee Maurais, "Multifidelity Covariance Estimation Three Ways." Master's Thesis, 2022.
Multi-fidelity covariance estimation in the log-Euclidean geometry
Published in International Conference on Machine Learning, 2023
Recommended citation: Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef Marzouk, "Multi-fidelity covariance estimation in the log-Euclidean geometry." In International Conference on Machine Learning. PMLR, 2023.
Adaptive algorithms for continuous-time transport: homotopy-driven sampling and a new interacting particle system
Published in NeurIPS Workshop on Optimal Transport and Machine Learning, 2023
Recommended citation: Aimee Maurais and Youssef Marzouk, "Adaptive algorithms for continuous-time transport: homotopy-driven sampling and a new interacting particle system." NeurIPS Workshop on Optimal Transport and Machine Learning, 2023.
Sampling in Unit Time with Kernel Fisher-Rao Flow
Published in International Conference on Machine Learning, 2024
Recommended citation: Aimee Maurais and Youssef Marzouk, "Sampling in unit time with kernel Fisher-Rao flow." In International Conference on Machine Learning. PMLR, 2024.
Multifidelity covariance estimation via regression on the manifold of symmetric positive definite matrices
Published in SIAM Journal on Mathematics of Data Science (accepted), 2024
Recommended citation: Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef Marzouk, "Multifidelity covariance estimation via regression on the manifold of symmetric positive definite matrices." SIAM Journal on Mathematics of Data Science (accepted), 2024.
talks
Parameter and Uncertainty Estimation for a Model of Atmospheric CO2 Observations
Published:
Arianna Krinos and I gave a talk on our atmospheric CO2 modeling work as part of the Department of Mathematics' annual Layman Undergraduate Research Competition. We had a fun time presenting together and won first place!
Better to Marry Renewables than Burn Fossil Fuels with Passion
Published:
Arianna Krinos and I were invited to present on our winning submission to the 2018 Mathematical Contest in Modeling. We enjoyed being in Denver and meeting Ben Fusaro, the founder of the MCM.
Multifidelity Covariance Estimation Three Ways
Published:
I gave an overview presentation about my master's work on multifidelity covariance estimation.
Riemannian Multifidelity Covariance Estimation from Statistically Coupled Observations
Published:
I gave a talk about our recent work on Riemannian multifidelity covariance estimation (preprint forthcoming!) while visiting some colleages at Dartmouth.
Riemannian Multifidelity Covariance Estimation from Statistically Coupled Observations
Published:
I gave a talk about our work on Riemannian multifidelity covariance estimation as part of the “Recent Advances in Data Assimilation and Uncertainty Quantification” minisymposium at SIAM CSE23. My thanks to the organizers for the invite!
Riemannian Multifidelity Covariance Estimation from Statistically Coupled Observations
Published:
I gave a talk about our work on Riemannian multifidelity covariance estimation as part of the Spring 2023 ACSEL Seminar Series at MIT.
Sampling in Unit Time with Kernel Fisher-Rao Flow
Published:
I spoke about our Kernel Fisher-Rao Flow sampling algorithm as part of the Computational Transport minisymposium at SIAM UQ24. My thanks to the organizers for the invite!
Sampling in Unit Time with Kernel Fisher-Rao Flow
Published:
I spoke about our Kernel Fisher-Rao Flow sampling algorithm during a visit to the Department of Mathematics and Statistics at UMass Amherst. My thanks to my hosts in the department for the invite and the enjoyable visit!
Dynamic Transport for Sampling: Kernel Fisher-Rao Flow and Beyond
Published:
I had a lovely time visiting the Centre for Data Science at QUT and spoke about our work on dynamic transport for sampling while there. Many thanks to my hosts there and to the seminar audience for great questions and feedback!
Dynamic Transport in High Dimensions: How do we get it to work?
Published:
I enjoyed discussing and learning about challenges in high-dimensional uncertainty quantification at this workshop in Jervis Bay, Australia. My thanks to the organizers for including me!
Less interaction in interacting particle systems: random Fourier features for Kernel Fisher-Rao Flow
Published:
teaching
CMDA Computing Consultant
Undergraduate, Virginia Tech Division of Computational Modeling and Data Analytics, 2019
Assisted fellow students with MATLAB, Java, Python, R, and C programming during weekly office hours (2017-2019).
Discovering Mathematics
Undergraduate, Virginia Tech Department of Mathematics, 2019
Served as a teaching assistant for three years (2016-2019) for the VT Math Department’s course designed to teach first-year students how to be a math major.
Polymath Jr. Program
Undergraduate Research Mentoring, Online, 2022
Designed and co-led a project in random matrix theory for undergraduates as part of the NSF-funded Polymath Jr. online summer research program for undergraduates.
Numerical Methods for Stochastic Modeling and Inference
Graduate, MIT Department of Aeronautics and Astronautics, 2022
Teaching assistant for graduate course on numerical methods for treating uncertainty in computational simulation.