Carnegie Mellon University

Joint PhD Program in Machine Learning & Public Policy

The Joint PhD Program in Machine Learning and Public Policy is a program for students to gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy issues. Students are expected both to make fundamental contributions to the science of machine learning as well as addressing core problems in one or more policy domains. 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 Public Policy and Management PhD Requirements, as we present next.

ML Joint Program Requirements

  • Successfully complete the 5 ML Core courses, with an average GPA of 3.5
  • Complete a Data Analysis Project (DAP), satisfied within the Home Department. (One Machine Learning Core or Affiliated faculty member must be on the committee). The project presentation must be announced to the MLD community.
  • Serve as a Teaching Assistant once for the Machine Learning Department

The Joint PhD thesis committee must include one MLD Core or Affiliated Faculty and the thesis proposal/defense must be announced to the MLD community.

For questions send email to: diane@cs.cmu.edu

Public Policy Joint Program Requirements

http://www.heinz.cmu.edu/school-of-public-policy-management/doctoral-program/phd-ppm/curriculum/index.aspx

For questions send email to:

MS degree along the way to the joint PhD

Students in the joint PhD program may earn a MS degree along the way to the joint PhD, but if they do they will have to decide if the degree will be from the Home Department or the Machine Learning Department. There is no joint MS degree and a student is not able to receive an MS from both departments. If choosing the ML MS degree, the student must comply with the requirements of the ML MS degree.