Joint PhD Program in Machine Learning and 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.
- The speaking and writing skills requirements, and the Data Analysis requirement (10-718) may be satisfied within the student's home department.
- A Joint-ML PhD student is required to TA twice, but only one TA-ship has to be within MLD.
- A Joint-ML PhD thesis will be a contribution to the combination of Machine Learning and the other field.
Rules for MLD PhD Thesis Committee (applicable to all ML PhDs):
The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s). It must include:
- At least one MLD Core Faculty member
- At least one additional MLD Core or Affiliated Faculty member
- At least one External Member, usually meaning external to CMU
- A total of at least four members, including the advisor who is the committee chair
For questions send email to: firstname.lastname@example.org
Public Policy Joint Program Requirements
For questions send email to: email@example.com
MS degree along the way to the joint PhD:
Students in a Joint-ML PhD program may earn a MS degree along the way, either from their home department or from MLD, but not from both. To earn the MS from MLD the must satisfy all relevant requirements.
Students interested in this joint PhD degree should apply to the PhD program that best aligns with their research interests (PhD in Machine Learning or PhD in Public Policy).
Machine Learning PhD online Application
A student must already be enrolled in one of the participating PhD programs of Statistics, CBNC or Heinz/Public Policy to apply for the Joint-ML PhD.
How to Apply to the Joint-ML PhD
To apply to the Joint-ML program, a student must already be enrolled in one of the participating PhD programs. Admission by students already enrolled in the PhD program of Statistics, CBNC or Heinz/Public Policy* will be by a lightweight application process to MLD, as follows.
Before applying, a student must:
- Take and pass 10715, 10705 and 10716 (10702 will count in lieu of 10716 if taken before Spring 2019). Applicants are expected to have a GPA of 3.5 or higher in these courses.
- Identify an MLD Core Faculty member who agrees to serve as their MLD mentor. The mentor will help guide the ML portion of the student’s research, represent the student at the MLD student evaluation meetings (‘Black Fridays’), become a member of the student’s thesis committee, and generally advocate for the student within MLD.
Applications must be submitted by May 31st to be considered for admission by the following Fall semester. Applications should be emailed to the MLD PhD Program Administrator, and must include:
- Student's CV
- Statement of Research Interests (one page will do)
- CMU Transcripts (unofficial will do)
- A short paragraph of recommendation from the home PhD Advisor (or PhD program Director if advisor has not yet been assigned)
- Email from the MLD Mentor confirming their willingness to serve in that role.
The MLD admissions committee may request additional information as needed.
Interested students are encouraged to apply as early as possible in their graduate studies, so that their research direction can be informed by their interactions with their MLD mentor.
Once admitted to the Joint-ML PhD program, in addition to being reviewed at their home department, the student’s progress will also be reviewed by the MLD faculty at their regular student evaluation meetings, where the student will be represented by their MLD mentor. The student’ advisor may also be present for this review.