Special Award for Criminal Justice and Racial Justice: Microsoft has also agreed to provide Azure computing credits for U-M researchers through their Justice Reform Initiative. The amount that a team can request can be anywhere between a few thousand dollars to $75K. The credits can be used for either new or ongoing projects; but need to be used between now and June 30. This special award does not go through the standard application process. Applicants please send a project summary (under one page) to Jing Liu, MIDAS Managing Director, at firstname.lastname@example.org.
The initiative focuses on reducing racial disparities in the justice system through front-end justice system reform. It has three priorities, including:
- Policing: Improving relationships between law enforcement and communities
- Diversion: Advancing alternatives to arrest and incarceration
- Prosecution: Increasing transparency and accountability in prosecutorial practice
In addition, Microsoft has a broader commitment to advancing racial equity and projects in other topical domains that are centered on advancing racial justice are also welcome.
Research. We seek proposals for research projects in any discipline that fit one of the following categories:
- The research project is new and can take advantage of the Azure cloud computing resource. The project needs to have other necessary resources in place so that they can start as soon as the computing credits are awarded. We will not award credits to projects that are still pending for funding. OR,
- The project is already ongoing, but the research team has not used Azure computing resources in the past. In this case, the team can apply if the original budget for computing can be re-budgeted for other purposes with the permission from the funding source.
The award decisions (see more details below) will be based on both the scientific merit and suitability for cloud computing. Please note: These credits will not support the use of data that has to be HIPAA-compliant.
Teaching. We also seek proposals for teaching projects in any discipline that can take advantage of the Azure credits. These include computing resources for undergraduate and graduate level for-credit courses, other short courses, and extracurricular research projects carried out by student clubs/teams with faculty mentoring. The lead teaching faculty or faculty mentors of student teams will need to be the Principal Investigators on the applications and provide the oversight for awarded projects.
Award information: This award is for one year. Each research project can request up to $20,000 worth of credits. Each teaching project can request up to $10,000 worth of credits. The credits can be used for any Azure service. We will award up to $300,000 worth of credits in total.
Who may apply: Principal Investigators (PIs) and co-PIs should be faculty members at the University of Michigan (Ann Arbor, Dearborn, or Flint campus). An individual may participate as PI/co-PI on only one proposal. Co-investigators, consultants and other personnel are not limited by this restriction.
To schedule office hours with Alex Vasquez (Microsoft University Relations) to discuss proposal support, email email@example.com
- Project duration: 12 months
How to Apply:
- Applications should be submitted online.
- Proposal content:
- Project description, up to 4 pages, with minimum font size 10.
- Research proposals should include specific aims, background, significance and innovation, and methods.
- Teaching proposals should include the description of the class, the curriculum outline, and the computing projects.
- Both research and teaching proposals should include a description of how the computing credit will be used, and proof that other resources needed for this project are in place (for example, grant award notification, balance of discretionary account, or course approval documentation).
- References (no page limit).
- Biosketch in NSF or NIH format for PI, co-PI, and senior personnel.
- Project description, up to 4 pages, with minimum font size 10.
- Suitability for cloud computing resources.
- For research projects:
- The significance and innovation of the proposed research;
- Likelihood of success;
- Impact to the research field;
- Potential for continuation, external funding and/or commercialization.
- For teaching projects:
- The need for this resource;
- The relevance and significance of the projects to the class and the learning outcome;
- The number of students this will benefit.
- All teams will be expected to submit a brief progress report at the midpoint, and a brief closeout report at the end of the award duration. Unused credits will be reassigned to other projects.
For questions, please contact: firstname.lastname@example.org
Congratulations to the following faculty members who received awards for their projects.
Emily Mower Provost, associate professor of electrical engineering and computer science, College of Engineering, will use the credits to study emotional well-being using speech-sensors like speaker-phone microphone on smartphones to regularly sample an individual’s ambient acoustic environment. The goal is to better understand the link between emotion and health, social, and vocational outcomes.
Harsha V. Madhyastha, associate professor of electrical engineering and computer science, College of Engineering, will use the credits to track down web pages whose URLs have changed. The goal is to improve the user experience on the web by finding aliases for up to 10 million broken URLs.
James Omartian, assistant professor of accounting, Stephen M. Ross School of Business, will use the credits to measure consumer responses to sales tax. The project will uncover which types of consumers are most sensitive to sales tax, what types of retail establishments are most susceptible to its adverse effect, and if consumers respond differently to taxes specific to a local region. The project will also assess the prevalence of cross-border travel to avoid paying sales tax in high-rate jurisdictions.
Gengxin Li, associate professor of statistics, College of Arts, Sciences and Letters, UM-Dearborn, and Jennifer Zhao, professor of mathematics, College of Arts, Sciences and Letters, UM-Dearborn, will use the credits for a graduate level statistical analysis course. It is designed to broaden the student’s understanding of the multivariate analysis technique which has become a powerful and popular computational tool in biostatistics, environmental science, engineering and data science.