News & Updates

Info Sessions

The M.S. in Health Informatics & Data Science (HIDS) at Georgetown University is designed to help students develop core competencies in data science, health-related data, predictive analytics, machine learning (ML), artificial intelligence (AI) and other advanced technologies. Through the program, students will be prepared to successfully take on a wide range of opportunities in health care organizations and related industries including health technology developers, device manufacturers, pharma/biotech, academic medical centers and management consulting firms. We help our students build critical skills for this growing sector to lead the evolution of health care.

Are you interested in a career in Health Informatics & Data Science? Join us for our virtual information sessions to learn more about our STEM-based Masters program and the application process.

Register for HIDS Virtual Information Sessions

All times in Eastern Standard or Daylight Time (EST/EDT)

Program News

Our Georgetown University Health Informatics and Data Science (HIDS) program has been listed among the top 20 Health Data Science Degrees by the Healthcare Management Degree Guide (new window).

We are proud to announce that the Georgetown Health Informatics and Data Science (HIDS) program is featured among the best value schools for masters degrees in Health Informatics & Data Science by BestValueSchools in 2021 and 2022 !

Our YouTube channel is up for our Georgetown University Health informatics & Data Science (HIDS) master’s program. Check out our short infomercials as well as longer videos of our STEM-based program, info session and our SevenBridges webinar.

Read our latest paper in Nature Communications (new window) published in December 2022 regarding a large scale consortium effort to develop a federated learning approach for AI models based on brain cancer imaging. The segmented labels from our REMBRANDT MRI dataset are part of a world-wide federated platform Federated Tumor Segmentation (FeTS) (new window)  that allows training specific machine learning models by leveraging information gathered from brain cancer datasets residing in collaborating sites without ever exchanging the data. Such a world wide platform enables very large multi-site machine learning models in an effort to accelerate discovery. In this paper, we describe how this Federated Learning model enabled big data for Rare Cancer Boundary Detection. This work was published with our collaborators Sypiron Bakas, Sarthak Pati, Ujjwal Baid et al at UPenn. Read a short summary of the paper here (new window) .