MSE in Data Science & Artificial Intelligence
Research
Research & Applied Projects
The Data Science & Artificial Intelligence Program connects students to research happening across Penn’s campus, especially through its institutes and centers, giving students the chance to apply data science and artificial intelligence methods to real problems in fields ranging from medicine to social science to engineering. The Industry Capstone, which launched in Fall 2026, lets students work directly with an industry sponsor on a proposed project, mentored jointly by the sponsor and DSAI teaching staff.
Three paths let you engage with research and applied projects at different levels of depth and independence:
Practicum (DATS 5990 - 001)
An independent study where you lead one or more parts of a research project. Your contribution is similar to that of a coauthor on a publication, taking ownership of a distinct piece of work within a larger collaborative effort.
- Credits: 1 CU (2 CU possible with approval)
- Advisor: Any Penn faculty member
- Report: 5 to 10 pages
- Presentation: Poster session
- Industry projects: Allowed, with an academic product
Industry Capstone (DATS 5990 - 002)
A project proposed by an industry sponsor, mentored jointly by that sponsor and DATS teaching staff. Admission is by application and interview.
- Credits: 1 CU per semester (2 CU max)
- Sponsor: Industry partner and DSAI teaching staff
- Admission: Application and interview (GPA of 3.6 or higher)
- Report: Sponsor deliverables and final presentation
- New: Launching Fall 2026
Thesis (DATS 9990)
A deeper research endeavor where you lead every aspect of a project, including problem formulation, materials, methods, results, and interpretation, aimed at answering a research question with the potential for publication in a peer reviewed journal.
- Credits: 2 CU (1 CU per semester, across two semesters)
- Advisor: Any Penn faculty member
- Report: Full thesis (SEAS format)
- Presentation: Five minute talk
- Reader: An additional Penn faculty reader is required