A collaborative community driving interdisciplinary innovation
Data science has the potential to advance the pursuit of knowledge in any discipline. Recognizing this, Yale created the Peter Salovey and Marta Moret Data Science Fellows Program in 2025 to encourage community and interdisciplinary collaboration among graduate students.
Established with a generous endowment, the Peter Salovey and Marta Moret Data Science Fellows Program provides a structured way for any Yale PhD student to complement the training of their academic discipline with activities in data science. Fellows receive mentorship, support, and professional development as they engage with a wider community of scholars to address pressing challenges in science and society.
About the Program
The Peter Salovey and Marta Moret Data Science Fellows Program aims to foster an active interdisciplinary community where graduate students interact with and learn from students, postdocs, and faculty from a variety of academic fields. The program builds on Yale’s strength in traditional data science departments—including Biostatistics (BIS), Biomedical Informatics & Data Science (BIDS), Statistics & Data Science (S&DS), and Computational Biology & Bioinformatics (CBB)—while also engaging the broader community of PhD scholars working on innovative data science projects across campus.
All participants are eligible for funding to support activities such as travel to conferences and workshops, participation in outreach events, and obtaining credits for data access, storage, and advanced research computing. In addition, a subset of students are selected for up to two years of stipend and tuition support in their home PhD program.
Fellows are chosen through a competitive review process that considers research potential, interdisciplinary engagement with data science, and alignment with the program’s mission to cultivate data-driven scholarship across Yale. In the first year, the steering committee expects to select around 20 fellows.
Requirements
The following requirements are in addition to those of the student’s home PhD program. Students are expected to complete these requirements within two years.
Coursework
Two courses are required.
Each student completes a course that complements the training of their home PhD program, such as a course in an application area outside of their primary research focus; a course covering the societal impact of data science; or, for students in non-STEM areas, a methods course in statistics, data science, or computing.
All fellows enroll in a special seminar on Data Science at Yale, organized as guest lectures by researchers across the University.
Event Participation
To foster community and collaboration, we require that fellows participate in at least two approved events. Examples include, but are not limited to:
Presenting research with a poster or oral presentation at the annual program-organized research showcase event.
Attending a data science research seminar, workshop, or conference, as offered through the Yale Institute for Foundations of Data Science.
Joining a program-sponsored professional development workshop on topics such as writing a CV and applying for jobs in industry and academia.
It’s a two-year, cohort-based program for early-stage Yale PhD students whose research is grounded in data science foundations or meaningfully integrates data science methods to advance their field. It’s designed to create an interdisciplinary intellectual community, structured opportunities, and mentorship across Yale.
The program is jointly supported by Yale GSAS and the Institute for Foundations of Data Science (FDS). The steering committee (faculty from across campus) oversees admissions and program direction, co-chaired by Professors Bhramar Mukherjee and John Lafferty. The administrative team helps with onboarding, logistics, and programming.
The inaugural cohort is expected to be about 20 fellows—large enough for diversity, small enough for real community.
Fellows participate for two years.
Eligibility and Fit
Eligibility is intentionally broad: early-stage Yale PhD students across all disciplines (including humanities and social sciences), as long as you can clearly explain how data science is meaningfully part of your research and training.
Yes. The program is open to early-stage Yale PhD students, including international students.
Primarily 1st- and 2nd-year PhD students, but the program is flexible (e.g., some 3rd-year students may be appropriate depending on disciplinary timelines and when research direction becomes clear).
No hard prerequisites. Reviewers look for a clear and compelling research vision and a convincing explanation of why data science methods/ideas are integral to your dissertation direction. Coursework during the program can help you build needed technical depth.
Broadly. Data science can include theory, methodology, computation, measurement/inference, responsible practice, and domain-driven questions. The program is intentionally interdisciplinary, and welcomes a wide range of approaches.
Yes. The program explicitly welcomes humanities perspectives, including critical/ethical/sociotechnical approaches and humanities work that uses methods like NLP/LLMs.
Potentially yes—especially when data science is coherent and central to the dissertation research question, not just an add-on tool.
Program Requirements
Everything is in addition to your PhD requirements, but it is designed to complement, not compete with your department. The goal is structure without making it feel like a second degree or second job.
Fellows complete: – A cohort seminar: “Data Science at Yale” – Coursework (two courses total as part of the program’s framework) – Event participation (at least two pre-approved events each year) –Community contribution (at least one pre-approved contribution activity)
Fellows take two courses as part of the program framework. One purpose is to build intentional breadth—often by taking a course that complements your home training (possibly outside your department).
Mentoring and Community
Mentoring is multi-layered: – Peer learning within the cohort – Access to program faculty leadership (including co-chairs and steering committee) for guidance on research direction, professional development, and career conversations – Ongoing connections with program alumni (as the program grows)
The two-year fellowship is the formal period, but the vision is for fellows to remain part of the community (events, listserv, mentoring future cohorts, etc.).
That’s understood and expected for early-stage PhD students. The program is centered on you and your development; the advisor listed at application time may not end up being your dissertation advisor
Funding
Funding varies. The program emphasizes community and opportunities as the core benefit. Some support may include: – Conference travel – Data/software resources – Professional development support – Small research-related expenses
Funding is not necessarily identical for everyone, and full tuition/stipend support may not be available for all fellows.
Yes. The program is not “just about funding”—it’s about cohort community, exposure, mentorship, and research/professional development opportunities.
Application Process
– CV – Personal statement (one-page maximum) describing goals and how the program aligns – Advisor/mentor recommendation form (a structured form with targeted questions)
No. The recommendation is collected via a structured form (not a free-upload letter) to ensure consistent, targeted input.
Choose someone who can write meaningfully about your potential and your fit—this could be a research mentor, a faculty member you worked closely with, or in some cases your Director of Graduate Studies (DGS).
Yes. There’s no program-imposed limit.
No—listing a co-mentor is optional. It can be helpful if you already have a clear interdisciplinary direction and a complementary faculty connection.
Applications are reviewed holistically—not by a single metric. Reviewers look at your research trajectory, how data science fits, and your potential to contribute to and benefit from an interdisciplinary cohort. The steering committee evaluates applications and may consult additional faculty if needed for expertise.
No. The program does not plan to conduct interviews.
No. Applications are reviewed after the deadline, and selections are made once the full pool is available.
Yes. Students are encouraged to apply in future rounds.
“Great public health insights often begin with curiosity about the data we collect and the stories it tells. As a Salovey and Moret Data Science Fellow at Yale, you’ll learn to go beyond numbers and equations—developing exciting novel methods, uncovering new insights about the underlying context, and contributing to better outcomes for communities everywhere—all while connecting with scholars across disciplines to enrich your perspective.”
Bhramar Mukherjee, PhD Senior Associate Dean of Public Health Data Science and Data Equity; Anna M.R. Lauder Professor of Biostatistics; Professor of Epidemiology (Chronic Diseases) and of Statistics and Data Science
“Advances in Data Science and the fields that use it are often the result of conversations between researchers in those fields and those who study the methods of data science. This Fellowship provides an opportunity for students to make such connections early in their careers so that their research can benefit from the broad spectrum of data science at Yale.”
Dan Spielman Sterling Professor of Computer Science; Professor Statistics and Data Science and of Mathematics
