Visiting Assistant Professor of Exercise Science in the Department of Kinesiology at the University of Connecticut, with research in the Human Performance Laboratory and the Korey Stringer Institute. I work on statistical analysis and machine learning for human physiology research: wearable sensor time-series, hierarchical Bayesian and mixed-effects modeling of within-person variability, and reproducible analysis pipelines. Marine Corps veteran.
Portfolio: jacobbowie.com · ORCID: 0000-0002-6055-8220
Bowie JS et al. Heat tolerance classification criteria require population-specific thresholds for accurate assessment of acclimation state in adults. Physiological Reports (2026). doi.org/10.14814/phy2.70745
- Bayesian hierarchical fitness-fatigue modeling against an open resistance-training cohort.
- rtSD Explorer: an interactive system-dynamics model of resistance-training adaptation (MEDv4) with a seven-test physiological-plausibility validator, running in the browser via marimo WASM. Live explorer.
- literature-pipeline: open-source biomedical paper acquisition + citation-graph snowballing toolkit. Cascades Unpaywall to PubMed Central to preprint mirrors; indexes in DuckDB; includes a MathML-to-LaTeX rendering pass for JATS XML.
- The Banister Constellation: a 175-paper citation graph of fifty years of fitness-fatigue model literature. Interactive viewer.
- Surface-EMG fatigue dynamics during knee extension to failure (manuscript in preparation).
Research partnerships, and longer-term research and industry roles at the intersection of applied ML, wearables, and human physiology. Email: jacob.bowie2 at gmail.
Public repositories on this profile are work-in-progress excerpts from larger private research projects. Case studies, reproducible analysis pipelines, and reviewer-ready code are in active migration from internal repositories. The portfolio site is the up-to-date front door.