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Ashalynn Davis

Institution/Organization: Southern University And A&M College

Department: Sci. & Math Ed/ Electrical Engineering

Academic Status: Graduate Student

What conference theme areas are you interested in:

Adaptive control, optimal control, and estimator design;
Artificial Intelligence (AI) and Machine Learning (ML) for science and engineering;
Applications in science, engineering, and industry;
Computation with discrete structures and graphs;
Data assimilation, challenges in data science, math of AI and ML;
Education and interdisciplinary programs in CSE;
Emerging software infrastructure for CSE, sustainability of numerical software;
High-order methods, novel discretizations, and scalable solvers;
High-performance computing, emerging architectures and programming paradigms;
Inverse problems, optimization, and uncertainty quantification;
Model and dimensionality reduction;
Multiscale, multiphysics, and multilevel methods;
Quantum algorithms, quantum computation, and quantum information science


Hello: My name is Ashalynn Davis. I am a Science and Mathematics Education(SMED) Ph.D student and a Graduate student of Masters of Engineering(Computer Networking and Telecom.) at Southern University and A&M College. My current research interest are as follows: using computational and mathematical optimization to replicate Electrical Engineering methods in the VR/AR Environment and analyzing the improvement of student learning by using Electrical Engineering methods in the VR/AR Environment. 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 volunteer tutoring for my community, at Food banks with my church , and mentor students in my community to help with the transition from secondary education to higher education