Jonathan Holt

Jonathan Holt, PhD, is a data scientist whose research is at the intersection of social science, public health, and environmental science, increasing understanding on critical issues and driving significant advancements in technology. His diverse background in research and academia has led to partnerships with multiple federal agencies, including the National Institutes of Health (NIH), Department of Defense (DoD), Environmental Protection Agency (EPA), National Institute of Justice (NIJ), and the Department of Education (ED).  

Dr. Holt recently collaborated with public health researchers on an NIH-funded grant to boost mental health among adolescents. For this project, he engineered a data pipeline that fetched raw physiological data from smart watches. The wearables data were processed using a tailored decision framework, which triggered personalized feedback to participants based on their activity and sleep patterns, helping to mitigate known risk factors for depression. Similarly, with support from the DoD, Dr. Holt innovated predictive algorithms that harness wearable device data to preemptively identify signs of respiratory illnesses—all by analyzing deviations from baseline heart rate variability. 

Additional noteworthy research includes a partnership with endocrinologists on an EPA-funded project to develop a tool for automated systematic review of scientific literature. The tool leverages large language models (LLMs) to categorize research effectively, facilitating deeper insights into chemical exposure impacts. In addition, Dr. Holt led an effort funded by the NIJ to estimate Census-level demographics using machine learning and synthetic populations. This approach not only served to impute unknown demographic profiles, but also incorporated a Monte Carlo method for quantifying model uncertainty. 

Beyond his project work, Dr. Holt’s passion for data science literacy is demonstrated by a commitment to education and mentorship. He is an adjunct professor at Duke University, where he teaches a cornerstone course for graduate-level engineering students. As a mentor for the ED Data Science Training Program, he provided expert guidance for 34 ED employees who sought to advance their analytic skillset. Dr. Holt also advises participants of the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Research Diversity (AIM-AHEAD) Program, an 8-month training course in advanced analytics that increases researcher diversity in AI and machine learning by leveraging the NIH All of Us database. 

Dr. Holt has also published peer-reviewed publications that advance the scientific community's understanding of key issues ranging from invasive species’ impacts on forestry to the valuation of green spaces in urban settings.