Leah Childers
I'm a computational scientist and applied mathematician who loves building cool things. I'm finishing a Mathematics PhD at Penn State (expected by or before Dec 2026), working in mathematical biology on antibiotic dosing and resistance. My flagship contribution is the Minimum Inhibitory Dose (MID), a novel, dynamic analogue to the MIC for time-varying antibiotic dosing that I derived using Floquet theory and validated on real surveillance data, which unifies previously distinct efficacy metrics for different antibiotics. I have first-author work published in the Journal of Biological Dynamics and other papers being submitted in various avenues soon. For my research, I built custom numerical machinery (stochastic PK/PD simulators, ODE solvers, matrix-Floquet stability solver, etc) in Python and Julia, optimized to run tens of millions of simulations on a laptop. Some of my work is public on GitHub, but as the papers are at varying submission statuses, I am unable to release everything yet.
I also ship software beyond my thesis: LearnFlow, a deployed full-stack learning-management platform (Next.js/Supabase), and NeurAllegro, an expression-native tokenizer and data pipeline for sheet-music notation benchmarked against all other tokenizers I could find (with ML training as the next step). I love numerical programming, performance optimization, and machine learning, and I care a lot about communicating technical ideas clearly. Outside of tech, I'm a classically trained musician (I compose orchestral music and play flute and piccolo) and an outdoor enthusiast who loves climbing!
