Little girl smiling with her head in her hands

Minn-LInK Fellowship Program Goal: To prepare future researchers for cross-system research using integrated data to better understand child well-being.

About the Fellowship Program

2018 Homework Starts with Home Minn-LInK Fellowship Program

Through funding provided by the University of Minnesota’s Provost’s Grand Challenges Research Initiative, the 2018 Homework Starts with Home (HSWH) Minn-LInK Fellowship Program will focus on ending student homelessness and its consequences for individuals, families, communities, and society. Students will have the opportunity to use cross-systems data to:

  • examine the educational trajectories of MN students experiencing homelessness,
  • understand the cost-effectiveness of stabilizing homeless and highly mobile students, and
  • consider additional risk factors for sub-populations of homeless and highly mobile students.

2015-2016 Minn-LInK Doctoral Fellowship Program

Through funding provided by the National Science Foundation we created and piloted the Minn-LInK Doctoral Fellowship Program – a program designed to prepare future researchers for cross-system research using integrated data to better understand child well-being. Funding also allowed for the expansion of Minn-LInK infrastructure and capacity through the integration of additional statewide administrative data and development of ready-to-use data sets and tools.

Application Process for the Minn-LInK Fellowship

Thank you for your interest in the 2018 Homework Starts with Home Minn-LInK Fellowship. The application period is now open for the 2018 Homework Starts with Home Minn-LInK Fellowship. Applications will be accepted until April 18th. Applicants will be notified of their admission to the Fellowship program by April 27th.

The Fellowship Program will accept 7 to 10 students from a variety of disciplines to spend the summer executing a cross-systems research project focusing on student homelessness using Minn-LInK’s integrated data.


This material is based upon work supported by the National Science Foundation under Grant No. SMA1338489.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.