Our Vision: 

Data Science for the Health of All.

Our Mission:

To integrate high-quality data science into the core elements of our school’s purpose: Think, Teach, and Do, ultimately facilitating the generation of new knowledge from large data sources to improve population health.  We will achieve this through various strategies, including:

  • Fostering new research synergies across multiple disciplines engaged in health data science, through activities like seminars, workshops, and seed funding opportunities.
  • Building and strengthening connections with health data scientists across the University to catalyze innovative health research.
  • Providing community members with resources and opportunities for training and continuing education as well as serving as a bridge between students and faculty and staff for mentored projects and internships.

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.