Carnegie Mellon University

Ph.D. Programs

Our Ph.D. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods.

Unique opportunities for our Ph.D. students include:

  • We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning, public policy, neuroscience, and the link between engineering and policy.
  • Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences.
    This allows students to have easy access to both the crucial questions
    in these fields, and to the data that can provide the answers.
  • Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
  • Carnegie Mellon is home to the first Machine Learning Department. Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of Doctor of Philosophy in Statistics seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page "Core Ph.D. Requirements" lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a Master of Science in Statistics as an intermediate step towards their ultimate goal.