Health Data Science & Systems

A UC Davis DataLab Research and Learning Cluster

The Health Data Science and Systems Research and Learning Cluster (HDSS RLC)’s focus is on computing systems and approaches, data security technology, and analytic drivers to advance research on  health data. Examples of foci areas include secure virtual computing platforms for advancing machine learning capabilities and analysis on protected health information (PHI) data.

April – 9Health Data Science ExpressOffice Hours
April – 16H.D.S. Brown Bag TalkDr. Sergey Buduchin
April – 23Health Data Science ExpressOffice Hours
April – 30H.D.S. Brown Bag TalkDr. Jason Adams
May – 7Health Data Science ExpressOffice Hours
May – 14H.D.S. Brown Bag TalkDr. Titus Brown
May – 21Health Data Science ExpressOffice Hours
May – 28H.D.S. Brown Bag TalkDr. Chen-Nee Chuah
June – 4Health Data Science ExpressOffice Hours
June – 11H.D.S. Brown Bag TalkDr. Xin Liu

Meeting Time and Location: Please submit a request to join the group’s announce list ( to receive Zoom login credentials and additional meeting information.

This new cluster will serve as a hub for the community with bi-weekly meetings, short lectures and thematic discussion groups, and will serve as a home for the interdisciplinary scholars in computer science, health informatics, and data science to develop next-generation systems for health data science  research. Membership is open all members of the UC Davis Health and UC Davis communities.

Reasons to join include:

  • Learning the challenges and opportunities in systems engineering and applied data sciences on health data;
  • Design, develop and use scalable and powerful computing systems that allow for data integration, analysis and sharing of protected data;
  • Discuss models and systems for advancing research computing and data sharing in health informatics;
  • Access DataLab and other health and research computational resources;
  • Connect with and receive advice from diverse peers across UC Davis and UC Davis Health campuses;
  • Open source and reproducible data enthusiasts and health-hackers welcome.