Our Mission:

The overarching goal of the Population Health Data Science Program is to advance the development and quality of health data science research.  This program will generate new research synergies by fostering interactions and collaborations between researchers across the multiple scientific disciplines encompassed within data science, provide community members with resources and opportunities for training in data science, and ultimately facilitate the generation of new knowledge from large data sources to improve population health.


Our Objectives: 

1) Generate Research Synergies by Fostering Collaborations Across the Multiple Disciplines Engaged in Health Data Science

2) Provide Training and Continuing Education Opportunities in Population Health Data Science

3) Stimulate and Support the Development of Novel Health Data Science Research


What We Do:

Seminars and Workshops- We convene and promote seminars and workshops aimed at increasing population health data science literacy and knowledge. Topics include: Machine Learning, Artificial Intelligence, Natural Language Processing, Data Visualization Techniques, Wearable Medical Technology, Deep Learning, Medical Imaging Analysis, Data Management and Reproducible Research.

Student and Trainee Engagement- The PHDS Program engages students and trainees in population health data science research by serving as a bridge to potential data science faculty mentors for mentored projects and internships.

Support for Early-Career Faculty- We host research-in-progress seminars to support the development of data science research in population health, particularly for junior faculty who wish to pursue grant funding and lead manuscripts.

Seed Funding Opportunities- The PHDS Program provides flexible seed funding for population health data science projects to help position faculty to be more competitive when applying for external funding. The goal is to catalyze a broad range of novel data science research, e.g. data-driven collaborative research studies, methodological research, development of new tools to address big data analytics, etc.