Ph.D. Programs in Statistics at Carnegie Mellon University
The Ph.D. programs of the Department of Statistics at Carnegie Mellon University 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.
Graduate Student Handbook
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Unique opportunities for our Ph.D. students include the following:
- 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.
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