Institution/Organization: Virginia Tech
Academic Status: Undergraduate Student
What conference theme areas are you interested in:
Artificial Intelligence (AI) and Machine Learning (ML) for science and engineering;
Applications in science, engineering, and industry;
Data assimilation, challenges in data science, math of AI and ML;
Emerging software infrastructure for CSE, sustainability of numerical software;
High-performance computing, emerging architectures and programming paradigms;
Inverse problems, optimization, and uncertainty quantification
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 enjoy working with elementary school children to introduce them to different STEM topics to get them interested at a young age. In my free time, I enjoy spending time outdoors and doing activities like hiking and kayaking.