My background

I graduated in 2021 with a PhD in theoretical physics from the University of North Texas, where I studied complex systems, nonlinear dynamics, and stochastic processes. I built a computational model of echo chamber formation on social networks undergoing information diffusion coupled with reinforcment learning dynamics; investigating formation conditions and disruption strategies. I've also built agent-based models and studied swarm intelligence. My expertise is in applying the theory of complex systems, stochastic processes, nonlinear dynamics, and reinforcement learning to real-world problems by building numerical software, reinforcement learning models, and simulations.

My experience

Since 2021 I've worked at the Institute for Health Metrics and Evaluation at the University of Washington, developing and applying DisMod-AT, the next generation of disease modeling software for the Global Burden of Disease project. There, I've contributed to cutting-edge global estimates of disease by developing and integrating analytics software run on terabyte-scale data pipelines, producing data visualizations for presenting crucial results to senior leadership and key stakeholders, and published articles in top-tier journals, such as The Lancet. I developed, integrated, and maintained a first of its kind automated quality assurance pipeline, now used by all organizational levels, to ensure that global disease estimates are consistently achieving world-leading accuracy, rigor, and explainability. I've also developed documentation and guidance for junior researchers, including producing and giving organization-wide trainings in-person and asynchronously.

My programming

I'm proficient in several programming languages, including 6 years of experience with Python, 4 with R, 4 with SQL, and 1 with Rust. I maintain several open-source Python packages for simulating complex systems and time-series analysis techniques.

My interests

My research interests focus on complex systems and stochastic processes, building numerical and simulation modeling software and reinforcement learning models, and applying them to answer real-world questions that defy simplification.