How Network Structure Shapes Epidemics
Comparing random, small-world and scale-free contact networks with SIR simulations
With Dr. Deena Schmidt, Department of Mathematics and Statistics, University of Nevada, Reno · Fall 2026
I simulate outbreaks on 500-person contact networks (random, small-world, and scale-free) to ask which structural properties, beyond the average number of contacts, predict how large and how fast an epidemic gets. Building on Ames et al. (2011) and Pérez-Ortiz et al. (2022), early results suggest that clustering, not the distance between people, keeps outbreaks small, and that global efficiency is the best single predictor. Next: separating clustering from the spread in contacts.
In progress
Fourier Analysis Through the Lens of Linear Algebra
Euler Circle · March 2026
Fourier analysis rests on the claim that a broad class of functions can be written as an infinite sum of sines and cosines. I argue that both the claim and the coefficient formulas become natural once viewed through linear algebra: the integral of a product of two functions behaves like a dot product, the trigonometric functions form an orthogonal set, and the coefficient formulas follow from the same orthogonal projection used for vectors in Rn.
Paper link coming soon
AI and the Future of Work: Analyzing Job Exposure to Generative AI
Inspirit AI Scholars Program · Summer 2026 · Inspirit AI Scholar certificate
In a six-person team, we studied how AI is changing jobs, using occupation data from O*NET and job-posting trends from Indeed. Each of us built an app; mine, the AI Insight Dashboard, is an interactive dashboard showing which jobs AI affects and by how much, and whether it automates tasks or helps people do them better. Our takeaway: AI is changing jobs more than replacing them.
Live demo link coming soon
