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.
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:
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.
“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
