DSI Faculty Fellowships are intended to promote, catalyze, accelerate, and advance UMN-based collaborations in data science and AI research so that UMN faculty and staff are better prepared to compete for external funding opportunities. Faculty with research in data science and AI in all disciplines and across all campuses, including interdisciplinary collaborations intersecting with Data Science and AI are encouraged to apply for this fellowship.
The DSI faculty fellow will pursue 1-2 specific grant opportunities as a result of their participation in the program. The DSI faculty fellows are expected to nurture and lead interdisciplinary teams to pursue large-scale proposals where DS/AI is central to their success. Of special interest are projects that may require long-term planning and leverage unique UMN strengths (as reflected in faculty expertise, facilities, or data sets) and priorities. This initiative seeks to lower barriers to multidisciplinary collaborations and to accelerate the advancement of data science and AI research by providing faculty with financial support (see below) and grant organization and writing assistance. Assistance will be tailored to help identify funding opportunities that require or prioritize interdisciplinary collaborations. The DSI is prepared to assist in facilitating these multidisciplinary collaborations at all levels including organizing meetings and facilitating introductions to other experts needed to strengthen a team, as well as pre- and post-grant administration and technical writing. At least one funding opportunity must be identified in the proposal.
Requirements:
The faculty fellow will have regular check-ins with DSI leadership to ensure they are receiving the needed support. A final report of no more than 5 pages will be delivered to the DSI upon completion of the fellowship term detailing the outcomes of the fellowship, the funding proposals submitted and their current status, future plans, and the budget details. Grantees will be expected to notify the DSI when a proposal is submitted and the outcome of the proposal (won/lost) when it is known. Five percent of the ICR for all award applications submitted during the fellowship period will be retained by DSI to support core activities. The DSI will work with the appropriate leadership in the applicants' department or college for any paperwork required. Finally, the faculty fellow agrees to provide the DSI with an abstract and optional image which will be used for promotional purposes including in the annual Seed Grant, Faculty Fellowships, and Data Sets Showcase (annually in December).
Funding Details
Funds can be requested up to $75,000 to help build and nurture interdisciplinary teams and preparation of the proposal. Examples include support for graduate research assistants to get early results in a collaborative setting, workshops to bring team members together for ideation and community-building activities, course buyouts for the lead PI to focus on the preparation of large-scale interdisciplinary proposals that require enormous time commitment, and so on. Contact umn-dsi@umn.edu with questions concerning qualified fellowship expenses.
Professional organizational and grant-writing assistance will be provided
Workspace will be provided at the DSI office
Recognition on the DSI website, including name and headshot.
Eligibility
Regular (Tenured and Tenure-Track) Faculty with their primary appointment at the University of Minnesota with a research area related to, encompassing, or using data science or AI throughout the UMN system are eligible to apply to be a faculty fellow. Adjunct, Affiliated, or Term Faculty are not eligible for funding under this program.
Application Requirements:
Interested faculty members are invited to submit a proposal of no more than 5 pages, addressing the following:
Introduction:
A brief overview of the research which will be the basis for the terms proposal(s)
Identification of targeted funding opportunities and identification of team (specific persons or expertise needed).
Relevance to one of the five MnDrive areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials, if applicable
Interdisciplinary Nature:
Explanation of how the proposed project bridges multiple disciplines.
Anticipated Impact:
Discussion of the potential impact of the research on data science and related disciplines. Background on the current research in this area at the UMN and elsewhere.
Budget and Timeline:
Itemized budget detailing how the funds will be utilized.
Proposed timeline and milestones for project execution.
Applicant Information:
Brief biography highlighting relevant expertise and experience.
Statement of commitment to collaboration and engagement with the DSI.
The DSI is committed to fostering innovative research and collaboration in data science. We look forward to supporting faculty members in pursuit of impactful interdisciplinary projects.
About DSI and MnDRIVE
The DSI has the current topic focus areas, but funding is not restricted to them.
- Foundational Data Sciences: Topics that are foundational to data science applications including data-intensive and data-informed topics and applications along with methodological research in areas such as signal processing, data mining, statistics, machine learning, and artificial intelligence (including GenAI and AI literacy) as well as topics in ethics and privacy. Fundamental methods dealing with data storage, archiving, sharing, acquisition, compression, or transmission are included. This includes but is not limited to, disciplines that underlie data science and AI such as statistics, mathematics, computer science, philosophy, and social and behavioral sciences.
- Digital Health and Personalized Health Care Delivery: The broad scope of digital health includes disciplines such as mobile health (mHealth), real-world observational healthcare data, public health, health information technology (HIT), wearable devices or technology, virtual care, and personalized healthcare and medicine. It includes enhancements to patient and consumer health and healthcare delivery through capacity-building activities and continuous, personalized, predictive, participative, and preventive approaches.
- Agriculture and the Environment: Agriculture and the environment are closely intertwined, topics that touch either or both areas of interest. The agriculture sector faces the challenge of feeding a growing global population while minimizing environmental impact and preserving natural resources for future generations. Research challenges in this area can reduce the consequences of climate and pest risks on agricultural production, lessen the impact of pollution, soil degradation, and water contamination or trapping greenhouse gasses, and mitigating flood risks.
Applications to all funding opportunities should be relevant to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials. While it is not required, it will improve the chances of success.
Acknowledgment Statements
Please include the following acknowledgment in any resulting publications:
The authors acknowledge the Data Science Initiative (DSI) at the University of Minnesota for providing seed funds that contributed to the research results reported in this paper.
For questions about this program, please email dsi-grants@umn.edu
DSI Faculty Fellowships
DSI Faculty Fellowships are intended to promote, catalyze, accelerate, and advance UMN-based collaborations in data science and AI research so that UMN faculty and staff are better prepared to compete for external funding opportunities. Faculty with research in data science and AI in all disciplines and across all campuses, including interdisciplinary collaborations intersecting with Data Science and AI are encouraged to apply for this fellowship.
The DSI faculty fellow will pursue 1-2 specific grant opportunities as a result of their participation in the program. The DSI faculty fellows are expected to nurture and lead interdisciplinary teams to pursue large-scale proposals where DS/AI is central to their success. Of special interest are projects that may require long-term planning and leverage unique UMN strengths (as reflected in faculty expertise, facilities, or data sets) and priorities. This initiative seeks to lower barriers to multidisciplinary collaborations and to accelerate the advancement of data science and AI research by providing faculty with financial support (see below) and grant organization and writing assistance. Assistance will be tailored to help identify funding opportunities that require or prioritize interdisciplinary collaborations. The DSI is prepared to assist in facilitating these multidisciplinary collaborations at all levels including organizing meetings and facilitating introductions to other experts needed to strengthen a team, as well as pre- and post-grant administration and technical writing. At least one funding opportunity must be identified in the proposal.
Requirements:
The faculty fellow will have regular check-ins with DSI leadership to ensure they are receiving the needed support. A final report of no more than 5 pages will be delivered to the DSI upon completion of the fellowship term detailing the outcomes of the fellowship, the funding proposals submitted and their current status, future plans, and the budget details. Grantees will be expected to notify the DSI when a proposal is submitted and the outcome of the proposal (won/lost) when it is known. Five percent of the ICR for all award applications submitted during the fellowship period will be retained by DSI to support core activities. The DSI will work with the appropriate leadership in the applicants' department or college for any paperwork required. Finally, the faculty fellow agrees to provide the DSI with an abstract and optional image which will be used for promotional purposes including in the annual Seed Grant, Faculty Fellowships, and Data Sets Showcase (annually in December).
Funding Details
Funds can be requested up to $75,000 to help build and nurture interdisciplinary teams and preparation of the proposal. Examples include support for graduate research assistants to get early results in a collaborative setting, workshops to bring team members together for ideation and community-building activities, course buyouts for the lead PI to focus on the preparation of large-scale interdisciplinary proposals that require enormous time commitment, and so on. Contact umn-dsi@umn.edu with questions concerning qualified fellowship expenses.
Professional organizational and grant-writing assistance will be provided
Workspace will be provided at the DSI office
Recognition on the DSI website, including name and headshot.
Eligibility
Regular (Tenured and Tenure-Track) Faculty with their primary appointment at the University of Minnesota with a research area related to, encompassing, or using data science or AI throughout the UMN system are eligible to apply to be a faculty fellow. Adjunct, Affiliated, or Term Faculty are not eligible for funding under this program.
Application Requirements:
Interested faculty members are invited to submit a proposal of no more than 5 pages, addressing the following:
Introduction:
A brief overview of the research which will be the basis for the terms proposal(s)
Identification of targeted funding opportunities and identification of team (specific persons or expertise needed).
Relevance to one of the five MnDrive areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials, if applicable
Interdisciplinary Nature:
Explanation of how the proposed project bridges multiple disciplines.
Anticipated Impact:
Discussion of the potential impact of the research on data science and related disciplines. Background on the current research in this area at the UMN and elsewhere.
Budget and Timeline:
Itemized budget detailing how the funds will be utilized.
Proposed timeline and milestones for project execution.
Applicant Information:
Brief biography highlighting relevant expertise and experience.
Statement of commitment to collaboration and engagement with the DSI.
The DSI is committed to fostering innovative research and collaboration in data science. We look forward to supporting faculty members in pursuit of impactful interdisciplinary projects.
About DSI and MnDRIVE
The DSI has the current topic focus areas, but funding is not restricted to them.
- Foundational Data Sciences: Topics that are foundational to data science applications including data-intensive and data-informed topics and applications along with methodological research in areas such as signal processing, data mining, statistics, machine learning, and artificial intelligence (including GenAI and AI literacy) as well as topics in ethics and privacy. Fundamental methods dealing with data storage, archiving, sharing, acquisition, compression, or transmission are included. This includes but is not limited to, disciplines that underlie data science and AI such as statistics, mathematics, computer science, philosophy, and social and behavioral sciences.
- Digital Health and Personalized Health Care Delivery: The broad scope of digital health includes disciplines such as mobile health (mHealth), real-world observational healthcare data, public health, health information technology (HIT), wearable devices or technology, virtual care, and personalized healthcare and medicine. It includes enhancements to patient and consumer health and healthcare delivery through capacity-building activities and continuous, personalized, predictive, participative, and preventive approaches.
- Agriculture and the Environment: Agriculture and the environment are closely intertwined, topics that touch either or both areas of interest. The agriculture sector faces the challenge of feeding a growing global population while minimizing environmental impact and preserving natural resources for future generations. Research challenges in this area can reduce the consequences of climate and pest risks on agricultural production, lessen the impact of pollution, soil degradation, and water contamination or trapping greenhouse gasses, and mitigating flood risks.
Applications to all funding opportunities should be relevant to one of the five MnDRIVE areas: Robotics, Global Food, Environment, Brain Conditions, and Cancer Clinical Trials. While it is not required, it will improve the chances of success.
Acknowledgment Statements
Please include the following acknowledgment in any resulting publications:
The authors acknowledge the Data Science Initiative (DSI) at the University of Minnesota for providing seed funds that contributed to the research results reported in this paper.
For questions about this program, please email dsi-grants@umn.edu