Aimee E. Maurais
Institution/Organization: Virginia Tech.
Academic Status: Graduate Student.
What conference theme areas are you interested in:
Computational science and machine learning;
Statistical modeling, methods, and computation;
Data science, analytics, and visualization;
Applications in science, engineering, and industry;
Numerical optimization: methods and applications;
Reduced order modeling.
I am interested in the application of computational statistical and numerical analysis methods to problems in science and engineering. Last spring, a colleague and I developed a data-based model for atmospheric carbon dioxide levels using Bayesian inference methods (Markov chain Monte Carlo) to optimize parameters and quantify uncertainty. That work won the Virginia Tech Math Department’s Layman Prize for Undergraduate Research, and we’re in the process of submitting the paper for publication. This semester I’m beginning work on solving an atmospheric sensing inverse problem from two different approaches, hybrid methods from linear algebra and methods from machine learning.
Non-Work Related Activities/Interests:
I greatly enjoy cycling, hiking, singing, knitting, reading, playing sports, and cooking. In particular, I biked 3,900 miles across the US in summer 2017 in support of affordable housing, and I have sung in Carnegie Hall with the Virginia Tech Choirs.