Become a Core Member at the Data Science Initiative

Renewable two-year appointment. Nominated (or self-nominated).

Submit your Nomination
Become a Core Member at DSI

All UMN full-time faculty with a demonstrated commitment to data science research — including both methodologically oriented and applications-oriented research — or teaching can be considered to be a core member. 

Non-members may nominate themselves or others yearly to become a core member.

Core-Members are part of one of the DSI’s ‘working groups’ which will work to define the goals associated with the group's focus. Working group topics are defined yearly by the members and leadership and core-members can select which of the groups best aligns with their interests and expertise. Current working groups are: Education and Outreach, Partnerships, and Facilitation of interdisciplinary grant opportunities. Working groups define their own schedule but are expected to meet at least twice a semester.

Membership Perks

  • Priority access to staff (communication specialist, grants manager,...)
  • Priority and free access to all events 
  • Newsletter
  • Separate Funding opportunities letter
  • Ability to define future programs at the DSI 
  • Opportunities for engagement and collaboration with External Partner Members 
  • Free access to grant help (grant writing organization, management, writing help, red teaming)
  • Name and picture on website
  • Staff support for coordinating and marketing events (lectures, workshops, etc.) branded or co-branded with DSI
  • Official DSI mug

Catchup on the Latest News at DSI

WiADS 2024 Conference: A Day of Inspiration and Connection for Women in Data Science

On November 4th, 2024, the University of Minnesota hosted the highly anticipated Women in AI and Data Science (WiADS) Conference, organized in partnership with MinneAnalytics at the McNamara Alumni Center. This sold-out event brought together over 1,000 registrants, including over 550 in-person attendees, and showcased the work of women, non-binary, and gender-diverse voices in data science.