I'm an Aerospace Engineer!
I am a Master's Student in Aerospace Engineering at West Virginia University, welcome to my website.
I am currently learning front end development, and I am creating this website as a portfolio.
It is still in development, but I would like to showcase the steps taken towards creation.
Today's date is Monday, February 16, 2026.
My thesis work is on parameter estimation for Lithium-Ion Battery Cells to drive pack level design optimization.
My research group has long worked with Gaussian processes (GPs) to model physical quantities of a system. As a
continuation of this work, and in collaboration with NASA's Prognostics Center of Excellence at NASA Ames Center,
I have created a method of using GPs to model battery parameters, such as diffusivity coefficents, and exchange
current densities as a function of the state of the cell. These well parameterized cell models, which are fast evaluating
can be used in pack-level design optimization problems, for thermal or electrical parameters.
If you are interested in my work, please reach out to me and I will be happy to discuss it further!
1. Uncertainty quantification, Bayesian methods allow for the propagation of uncertainty in models.
This means that the bounds and limitations of models in comparison to the input space, are further understood.
2. Interpretability, unlike Neural Networks and other machine learning techniques,
Bayesian methods are interpretable and the results can be easily mapped to the inputs.
This provides insight in how inputs and outputs of the system are related.
Using Direct Collocation and a physics discritization to create a parallel method for estimating both the state and parameters of a physical system. This methodology is trained similar to a Physics Informed Neural Network (PINN) and is able to be used to build out embedded GPs into physical systems.
I am the primary developer of FoKL GPs. A unique method for fast bayesian inferencing and evaluation. Learn more about FoKL GPys on our repo.
Email: derek.slack.001@gmail.com
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