Resume — ouroborOS Consulting

Education

\textbf{University of Minnesota} \hfill \textbf{2023 -- 2025}\\ Graduate Research (NIH T32 Fellow) | Biostatistics
Focus: Bayesian Models, Brain Imaging, Causal Inference, Psychopathology, Spatiotemporal Modeling

\textbf{North Carolina State University} \hfill \textbf{August 2023}
Master of Statistics

The Pennsylvania State University} \hfill \textbf{May 2021}
Bachelor of Science, Economics

Experience

Software Engineer at ABC Corp, 2020 - Present

Skills

Languages

Python JavaScript C++ \item[Languages:] Python (\textit{NumPy, SciPy, pandas}), R (\textit{tidyverse}), C++, LaTeX.

Web Development

HTML CSS React

Statistics & ML

Causal Inference, Bayesian Modeling, A/B Testing, Power Calculations, Spatiotemporal Modeling, Anomaly Detection, NLP, Classification. MySQL MongoDB

\item[Platforms \& Tools:] \textbf{Palantir Foundry}, SQL, PySpark, Git/GitHub, ArcGIS Pro, FedRAMP-compliant cloud environments.

Projects

Bayesian Spatiotemporal Modeling of Neuroimaging Data \hfill \textbf{Jan 2025 -- Aug 2025} \\ \textit{Lead Researcher (T32 Training Grant)} \begin{itemize}[noitemsep] \item Developed a novel Bayesian ML method for modeling high-dimensional anatomical sensor data (MRI), accounting for complex spatiotemporal correlations. \item Identified outlier trajectories in pediatric brain development related to mental health conditions through statistical and algorithmic validation. \end{itemize} \textbf{Bayesian Integrative Analysis of Multiomics Data} \hfill \textbf{Sep 2023 -- Dec 2024} \\ \textit{Algorithm Lead (T32 Training Grant)}

Developed a novel Bayesian ML method for modeling high-dimensional anatomical sensor data
Designed and optimized C++ code for a Metropolis Hastings sampler to perform simultaneous inference on high-dimensional clinical and genomic datasets.
Built a multinomial ML classifier to diagnose pulmonary sarcoidosis, using PCA to reduce dimensionality in patient protein data.

% --- APPLIED RESEARCH PROJECTS --- \section*{RESEARCH PROJECTS} \textbf{Bayesian Spatiotemporal Modeling of Neuroimaging Data} \hfill \textbf{Jan 2025 -- Aug 2025} \\ \textit{Lead Researcher (T32 Training Grant)} \begin{itemize}[noitemsep] \item Developed a novel Bayesian ML method for modeling high-dimensional anatomical sensor data (MRI), accounting for complex spatiotemporal correlations. \item Identified outlier trajectories in pediatric brain development related to mental health conditions through statistical and algorithmic validation. \end{itemize} \textbf{Bayesian Integrative Analysis of Multiomics Data} \hfill \textbf{Sep 2023 -- Dec 2024} \\ \textit{Algorithm Lead (T32 Training Grant)} \begin{itemize}[noitemsep] \item Designed and optimized C++ code for a Metropolis Hastings sampler to perform simultaneous inference on high-dimensional clinical and genomic datasets. \item Built a multinomial ML classifier to diagnose pulmonary sarcoidosis, using PCA to reduce dimensionality in patient protein data. \end{itemize}