MDST Mentors

By October 30, 2019 News
The Michigan Data Science Team, sponsored by MIDAS and comprised of students of all levels from various schools/colleges at UM, is looking for additional faculty or post-doc mentors that could help guide student projects.  If you are interested in providing domain-specific expertise or contributing to any of the following topics, please contact MIDAS Education Program Manager, Trisha Fountain (, and she will connect you with the appropriate members of the team.
Student projects for this semester include:
  1. Education Deserts: Education deserts are geographic areas removed from post-secondary educational institutions. The presence of these institutions have a pretty big impact not only on educational access of people in their vicinity, but also on local economies and demographics. Take U of M and Ann Arbor as one outstanding example of this type of relationship. We would like to examine what features about these educational institutions have what type of impact on local socioeconomic factors.
  2. Oscar Winners: How can we predict which movies will win the 2020 Academy Awards? Features students are currently considering include IMDB reviews, ratings, and potentially even Twitter responses.
  3. Music Generation: This team is working on generating music (MIDI files) using deep learning with a transformer model.
  4. r/rateme analysis: rateme is a subreddit where people post pictures of themselves and ask to be rated on appearance. We’re more interested in: What are the demographic distributions (age/gender) of posters and commenters? How do these differ, and how do they interact? How predictive are age/gender in predicting ratings? How does the rating-seeking language affect the ratings on a post (i.e. if you display less confidence in posting, are people less likely to rate you harshly?)
  5. Congestion Pricing: Some large cities have implemented congestion pricing policies in which they charge a price for vehicles which enter the city center during peak traffic hours. The idea is that this will incentivize public transportation usage and decrease traffic during rush hours. Students are looking at London traffic data to see how effective this policy has been (London is one of the cities with this type of policy).
  6. Blood Pressure Estimation: We are working with Dr. Byrd from the medical school on this project, so mentors are less necessary, but I figured I’d include this just to be comprehensive. Blood pressure tends to be in flux, so a single sample is less informative than an average over the course of a day. We’ll be looking at clinical trial data and data from the UM hospital clinical warehouse to see if lab results (such as complete blood count) can be used as a good predictor of average blood pressure.