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Posts by Collection
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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
Less interaction in interacting particle systems: random Fourier features for Kernel Fisher-Rao Flow
Published:
Dynamic transport for sampling: Kernel algorithms for ``Wasserstein-izing’’ Fisher-Rao paths
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.
16.940: 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.
18.085: Computational Science & Engineering I
Graduate, MIT Department of Mathematics, 2025
Kate Fisher and I are co-teaching MIT’s 18.085 this summer. I’ll be covering numerical linear algebra, initial value problems, and time-stepping.