Resume / CV
Last updated 06/14/2026
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Leah Childers
Computational Scientist · PhD Candidate, Mathematical Biology
San Francisco, CA · leahchilders6745@gmail.com · LinkedIn · GitHub · Google Scholar · Portfolio
Computational scientist and applied mathematician completing a math PhD in mathematical biology. Develops numerical and analytic methods for real biomedical problems, including the Minimum Inhibitory Dose (MID), a novel mechanism-aware metric for time-varying antibiotic PK/PD, validated on real individual and population data, with the goal of guiding antibiotic dosing to slow the escalation of antimicrobial resistance. Experience shipping high-performance scientific code, data pipelines, and ML models, with deep applied-math foundations. Seeking computational / research-scientist roles in bio, health, or pharma.
SF Bay Area; PhD expected Dec 2026, available to start Q4 2026.
Research
Doctoral Researcher, Pennsylvania State University, Dept. of MathematicsAug 2022 – present
Advisor: Dr. Jessica Conway; primary collaborator: Dr. Pia Abel zur Wiesch. Thesis: antibiotic dosing from a general analytic perspective.
- Introduced the Minimum Inhibitory Dose (MID): a novel antibiotic efficacy metric generalizing the MIC for real time-varying dosing; on real US (GISP) and Norwegian surveillance data it recovers the documented CDC/WHO ceftriaxone dose-escalation history
- Developed methods for analytic and numerical calculation via Floquet theory
- Validated general behavior across 5 distinct antibiotic classes and 3 bacterial species
- Unifies classically "time-dependent", "concentration-dependent", and "exposure-dependent" antibiotics under one metric
- Extended to drug combinations (MID isoboles), resistance frameworks, and sophisticated drug-target binding models
- Built the supporting numerical machinery from scratch in Python and Julia
- Stochastic PK/PD simulators, ODE solvers, custom root finders, matrix Floquet stability solver (power iteration on sparse operators)
- For simulations: validated results with Gillespie/Alfonsi simulations and analytic branching process curves
- Optimized the methods with Numba, JAX, and the Julia ecosystem; best performance achieved with Julia JIT/multiple dispatch/multithreading reaching millisecond-scale per simulation, over 10× speedup from parallelized Numba JIT
- Ran over 50 million simulations across hundreds of dosing regimens with a huge parameter space
- Most code is open-source on GitHub; also curates a public database of ceftriaxone vs. N. gonorrhoeae PD data
Publications
- L. Childers, P. Abel zur Wiesch, J. M. Conway. "A General Analytic Approach to Predicting the Best Antibiotic Dosing Regimen." Journal of Biological Dynamics (2026). doi:10.1080/17513758.2026.2641302
- L. Childers, P. Abel zur Wiesch, J. M. Conway. "The Minimum Inhibitory Dose (MID): a dynamic alternative to the MIC for guiding antibiotic treatment." In preparation.
- Two further first-author manuscripts in progress (MID for drug combinations; MID under resistance).
Selected Projects
- Neurallegro (2024 – present): designing the first expression-native tokenizer for sheet-music notation: existing music ML works from MIDI and discards notation semantics (dynamics, articulation, slurs, enharmonic spelling); surveyed 17 prior approaches and built a custom tokenizer from scratch. Implemented the full MusicXML→token encoder with ~600-token vocabulary, tested over a 250k-score corpus; next step is benchmarking against previous tokenizers and training a transformer on the tokenized data. Mostly in Python, with a custom fork of the music21 package maintained for this project.
- LearnFlow (2025): designed and deployed a full-stack learning-management platform (Next.js/React, Supabase/Postgres, OpenAI & Gemini APIs, Vercel) introducing recursive "flows" for course structure and flexible non-numeric grading. Cut course-build time ~90% (measured by rebuilding a real Penn State course in Canvas vs. LearnFlow); designed with Penn State faculty input.
Teaching & Writing
- Instructor of Record, College Algebra, Penn State (Jan 2023 – Aug 2025): full course ownership for 50–100 students/semester: wrote and delivered lectures, wrote and graded quizzes and exams, held office hours.
- Coordinator Assistant, Math 021, Penn State (Aug 2024 – May 2025): wrote homework, worksheets, and exams; ran biweekly instructor meetings.
- Technical Writer, Beam.cloud (YC W22) (Mar – May 2025): wrote developer-facing articles on training, developing, and selecting ML models (e.g., LLM Parameters for Developers).
Presentations
- Contributed talk, "The minimum inhibitory dose (MID): a dynamic analogue to the minimum inhibitory concentration (MIC) for guiding antibiotic treatment," SIAM Conference on the Life Sciences, July 2026.
- Organizer & invited speaker, minisymposium "Mathematical Modelling for Infectious Diseases and Interventions," SIAM NNP Section Conference, 2025.
- Invited talks: Virginia Tech MathBio seminar (Fall 2026), Penn State Math Club (Spring 2025).
- Poster, Penn State Graduate Student Exhibition (Spring 2025).
Skills
- Languages: Python, Julia, MATLAB, R, SQL (PostgreSQL/SQLite)
- Scientific & ML: NumPy, SciPy, JAX, Numba (JIT), PyTorch, scikit-learn, Julia ecosystem
- AI / LLM tooling: LLM CLIs/APIs/agents
- Data viz: matplotlib, Plotly, Dash, ggplot2
- Dev: Linux, Git, Bash, Google Colab, VIM, LaTeX
- Web / full-stack: Next.js, React, TS/JS, Supabase, Vercel
Education
- Ph.D. in Mathematics, Pennsylvania State University — expected Dec 2026
- B.S. in Mathematics, Virginia Tech (2022) — summa cum laude; completed in 3 years
- Graduate coursework & projects: Deep Learning, Bayesian Statistics, Regression Models, PK/PD Modeling, Epidemic Modeling
Honors & Awards
- August and Ruth Homeyer Graduate Fellowship, 2022, Penn State
- SURE "Speakers and Undergraduate Research Engagement" Fellowship, 2022, Virginia Tech
- Ray A. Gaskins Scholarship in Math, 2021–2022, Virginia Tech
- T. W. Hatcher Math Scholarship, 2021–2022, Virginia Tech