This program differs from the standard Statistics Ph.D. program in its emphasis on machine learning and computer science. Students in this track will be involved in courses and research from both the Departments of Statistics and Machine Learning.
During the first year, students will normally be situated in and supported by the Department of Statistics. During later years, students will be located in the Department of their primary advisor.
Students will be granted the joint degree if they meet TWO sets of program requirements corresponding to the TWO departments, namely the ML PhD Requirements and the Statistics PhD Requirements, as we present next.
Students in this program are subject to all of the core Ph.D. requirements, except that the Data Analysis Exam is not required. (The Data Analysis Exam is required, however, in order to receive the M.S. in Statistics.)
Students in this program are required to complete the Advanced Data Analysis (ADA) project to the same standards as regular Statistics Ph.D. students. Namely, they are required to work on a substantive, real data project with a domain expert as outside advisor. A faculty member from the Department of Statistics must play an oversight role in the project, if not as the primary advisor. This project will satisfy the ML Data Analysis Project (DAP), speaking skills, and writing requirements, provided that an ML faculty member is an advisor.
The student must be advised by a faculty from the home department along with a Core Faculty member from the joint department as a co-mentor. Joint Statistics-MLD faculty cannot serve both roles. Both faculty must be identified at the time of admission to the joint program.
The thesis proposal and defense must be announced to the MLD community.
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Students interested in this joint PhD degree should apply to the PhD program that best aligns with their research interests (PhD in Statistics or PhD in Machine Learning).