What Research Opportunities Are Available Through U-M LSA Statistics?

Updated

U-M LSA Statistics supports undergraduate and graduate research through faculty-mentored projects, an applied statistics and data science curriculum, and connections to interdisciplinary work across the university. The department describes "diverse research opportunities" alongside "expert faculty," so the practical path is to identify a faculty member whose area matches your interests, complete the relevant coursework, and ask about joining a project. The exact openings, eligibility, and application steps are not listed on the source page, so confirm details directly with the department or individual faculty.

Types of research opportunities

The department's own description points to research as part of the student experience rather than a single fixed program. In practice, opportunities in a statistics department like this typically fall into these categories:

  • Faculty-mentored independent research — you work with a faculty advisor on a defined statistical or data science problem, often leading to a paper, thesis, or conference presentation.
  • Applied/collaborative projects — statistics and data science methods applied to real datasets from other fields (health, social science, engineering, and more), which fits the department's emphasis on applied statistics.
  • Graduate-level research — thesis and dissertation work for master's and PhD students, tied to faculty research programs.
  • Course-based research experiences — projects embedded in advanced courses that can grow into longer-term work.

Because the source page does not enumerate specific labs, programs, or deadlines, treat this as the general structure and verify which of these are currently active.

Faculty and research areas

The site highlights "expert faculty" as a core strength. To find a match:

  1. Go to the department's faculty directory and read individual research statements.
  2. Note the methods and application areas each person works in (for example, statistical methodology, data science, or applied collaborations).
  3. Shortlist two or three faculty whose work overlaps with your coursework and interests.

This step matters because research access in a statistics department usually depends on a specific faculty member having room on a project — not on a general application pool.

How research connects to the statistics and data science programs

The department frames its programs around "top-ranked applied statistics and data science" study. That framing suggests research is meant to reinforce the curriculum:

  • Methods you learn in applied statistics and data science courses become the tools you use in a project.
  • A research experience gives you a concrete example to discuss in graduate applications or job interviews.
  • Working with real data is where classroom technique meets the messiness of actual analysis.

If your goal is to evaluate the department's academic resources, the presence of faculty-led research plus an applied curriculum is the relevant signal — but you should confirm the scale and availability of opportunities rather than assume open access.

How to learn more or get involved

Since the source page does not provide contact details or a research application process, use these steps:

  1. Check the department website for a research, undergraduate research, or graduate research section.
  2. Email a faculty member whose work interests you. A short message: your program and year, the courses you've taken, one sentence on why their research interests you, and a question about whether they have openings or can point you to someone who does.
  3. Ask your academic advisor about department-specific research programs, funding, and timing.
  4. Attend department talks or seminars if listed — these are low-pressure ways to meet faculty and learn what projects are active.

Common sticking points: research slots are often limited and filled by students who already know a faculty member; some opportunities require prerequisite coursework; and timing (early in a term) matters more than a formal application window. None of these specifics are confirmed on the source page, so treat them as questions to ask, not fixed rules.