Here's a quick overview of some projects I've worked on or currently maintaining:

Ever want vector embeddings for any place on Earth? Here they are! This data product was produced entirely on AWS with land cover data sourced from the Copernicus Earth Observation program. I'm keeping the repositories for this project private, but am happy to show them personally to anyone interested in this project.

This project involves the automatic generation of children's stories using AWS Amplify, Lambda, and OpenAI's GPT API. The focus is to ensure these stories are safe and suitable for kids.

My personal site that houses various technical posts. This site functions as a platform for thoughts and insights that may be too succinct for formal papers or repositories.

I'm currently an application architect at NVIDIA, focusing on AI. My previous research focused on creating scalable models for inference and prediction in geospatial settings. I have a particular interest in latent variable models and Bayesian methods.

My academic background includes degrees in physics from Macalester College (BA, 2013), and from Duke in statistics (MS, 2020), and civil/environmental engineering (PhD, 2020).

I've also worked as a consultant for firms utilizing Bayesian methods in marketing analytics, and have experience in the healthcare sector as a database engineer. Part of my career was spent at the GeoAI research group at Oak Ridge National Lab, working on problems related to security and geospatial modeling. I've taught as a university instructor for graduate-level classes in applied statistical modeling as well. More details can be found on my curriculum vitae linked on this webpage.

My past research often dealt with observational or structural uncertainty, requiring probabilistic analysis. Many of the projects involved data with an inherent spatial aspect, thus suitable for spatial statistical methods. These projects include:

• Statistical inference for noisy / sparsely observed physical systems
• Integrating expert knowledge in posterior simulation for geospatial categorical data
• Dimension reduction for high-dimensional data
• Bayesian inference for engineering models
• Leveraging graphical structure in deep generative models

Please feel free to explore these projects and contact me if you're interested in any of them.