About

My background

I graduated in 2021 with a PhD in theoretical physics from the University of North Texas, where I studied complex systems, stochastic processes, and nonlinear dynamics.

I advanced and optimized entropy based time series analysis methods to study swarm intelligence and collective behavior, achieving a 100x efficiency increase over earlier prototypes while improving signal resolution.

I built adaptive network models using reinforcement learning dynamics to simulate polarization and synchronization on networks experiencing information diffusion, and applied those models to study echo chamber formation and disruption strategies.

My expertise is in applying theory 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 deploying the next generation of disease modeling software for the Global Burden of Disease project. I build new models, run terabyte-scale data pipelines, produce data visualizations for presenting crucial results to senior leadership and key stakeholders, and publish articles in top-tier journals, such as The Lancet.

I developed, integrated, and maintained IHME’s first automated quality assurance pipeline, ensuring that estimates consistently achieve world-leading accuracy, rigor, and explain-ability, gaining recognition from senior leadership.

I maintain several open-source Python packages for simulating complex systems and time-series analysis techniques.

Programming languages

• Python :: 6 yrs
• R :: 4 yrs
• SQL :: 4 yrs
• C/C++ :: 1yr
• Rust :: 1 yr

Research interests

Complex systems, stochastic processes, reinforcement learning, numerical modeling and simulation, and solving real-world problems that defy simplification.