The mission of the lab is to build predictive models of exercise-training adaptations to improve fundamental understanding of exercise biology and to optimize exercise training programs for health, fitness, rehabilitation or performance goals.

A central theme of the research is developing and applying mathematical models to diverse data sources, including published literature, biological experiments, and wearable-sensor or tracking data.

The lab's research is organized into the following themes:

Our interdisciplinary approach

Our research lies at the interface of the exercise physiology, systems biology, and biomedical engineering disciplines.

Our cellular-level experiments involve systematically perturbing cultured cells with exercise-relevant stimuli, followed by measuring the time courses of cell signaling using antibody-based proteomic techniques (multiplexed bead-based assays, ELISA, immunoblotting).

Human performance and physiological data are collected using field-based measurements from portable or wearable sensors.

We also perform systematic review and meta-analysis of published data for our work in evidence-based exercise programming. 

We then apply mathematical and statistical models to the data for estimation, hypothesis testing, and prediction.