The 2025 CMLH Fellowships in Sensing Data for Public Health Call for Proposals is open!

The Center for Machine Learning and Health is pleased to announce a 2025 call for fellowship proposals. 

We invite applications for the Center for Machine Learning and Health (CMLH) Fellowships in Sensing Data for Public Health. Each fellowship provides full support for one year for a Ph.D. student at Carnegie Mellon University who is pursuing cutting-edge research in this area.

Submissions should propose a project that is within the following theme:

Sensing Data for Public Health

This year's digital health theme refers to the application and collection of sensing data and continuous monitoring of various health needs to improve public health and patient safety. Through the use of sensing devices, such as wearables, patient monitors, and other medical related technologies, the collection of data can be used to assist in the analysis and applications of public health and patient care.

Proposals must be aligned with or similar to the following focus areas: glucose monitoring, heart disease, fall detection, stroke, medication monitoring, physical fitness and activity.

Advanced sensing data will improve community wellness programs with deeper insights to targeted interventions; assist patient decision-making for improved health monitoring and disease prevention; and advance surveillance of public health. In addition, educational programing and detection could further improve the health and wellbeing of individuals and communities.

Projects should propose some use or development of sensor data or monitoring of data (personal, community or other) to advance healthcare while also ensuring patient data privacy. 

Data collection, data storage and data retrieval are elements essential for advancing technologies in this target area. 

Any data collected must be made available under reasonable terms (evaluated as part of the proposal) for others to use. Proposals for the fellowship are expected to follow university intellectual property (IP) and research policies.

Fellowship Award Details

Each fellowship will provide support for one year for a Ph.D. student at Carnegie Mellon whose research, in collaboration with a faculty member, advances generative AI in healthcare. It provides:

• One year of tuition and stipend support (including summer stipend) for the student (nontransferable).
• $3,000 in funding to support the underlying research, including conference travel for paper presentation, equipment and human-subject experiments. 

ITP: Intent to Propose

This call includes a preliminary step with an Intent To Propose (ITP) submission. ITPs that are selected will advance to a full proposal submission. 

Important Dates

• Wednesday, March 19, 2025 — End of Day (midnight EDT): Intent to Propose (ITP) document due. No late submissions. CMLH will select relevant, high-impact projects from among these ITPs to submit a full proposal.

  Tuesday, March 25, 2025: Applicants selected to submit full proposals will be notified.

  Monday, April 14, 2025 — End of Day (midnight EDT): Full applications due from invited applicants. Full applications will be accepted only from those invited based on their ITP submission. 

Criteria and Eligibility

Who Is Eligible?

• Full-time, currently enrolled Ph.D. students at Carnegie Mellon University.
• Students with a primary research project related to the selected theme in digital health.

Criteria

• Students must be enrolled in the Ph.D. program for a full academic year for the duration of the fellowship.
• We welcome projects at any stage, both initial and mature. 
• No other fellowship should support the student throughout the duration of the award. The CMLH Fellowship cannot be combined with any other fellowships. 

Format of Intent to Propose Applications

All ITP submissions should follow the format described below. Submissions that do not follow the format will not be accepted.

The application should be submitted via email attachment to cmlh@cs.cmu.edu. It should consist of a single-page PDF file, separated into the three sections described below. Text should be single-spaced, 11pt font. Please mark the page as "Confidential."

Bibliographies, CVs, images or charts should not be included in the ITP. If the ITP is selected to advance to a full proposal, additional information and documents — including a CV and a longer proposal — will be required.

Section I: Student and Research Information

1. Student name, CMU email, college/school and department.
2. Name of Ph.D. program in which the student is currently enrolled at CMU.
3. Faculty adviser(s).
4. Proposal title.
5. Focus area (see list above)

Section II: Pitch Statement — ONE SENTENCE

This is an elevator pitch for your project — how you'd describe the project if you had just a few seconds in an elevator with a funder. It should be a brief, persuasive sentence that you would use to create excitement around your project. It should be interesting, memorable and succinct. 

Section III: The Research — 1/2 PAGE MAXIMUM

This abstract should describe your proposed project; its importance and potential impact; and why it is a novel, innovative or unique approach to the problem. You should be sure to answer the following questions (though you do not need to have separate sections for each question, and the answers should be integrated into the half-page narrative).

Why is it important in healthcare?

• What is the problem and why is it important?
• What is your proposed solution?

What is the innovation?

• What new technique, idea, approach or method is proposed to solve the stated problem? 
• How does it relate to the theme?

What will the impact be on healthcare?

• What is the goodness/impact that your innovation will provide? 
• How will it deliver better outcomes or more efficiency or both? 

 Visit our Frequently Asked Questions for more information about the fellowships! 

Contact the CMLH with any questions.