Joint Machine Learning PhD Programs
Students interested in a ML- Joint PhD degree should first apply to the PhD program that best aligns with their research interests.
PhD in Statistics & Machine Learning
This PhD program differs from the Machine Learning PhD program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics PhD program in its emphasis on machine learning and computer science. The Joint PhD Program in Machine Learning and Statistics is aimed at preparing students for academic careers in both CS and Statistics departments at top universities or industry.
PhD in Machine Learning & Public Policy
The Joint Ph.D. Program in Machine Learning and Public Policy is a program operated jointly by faculty in Machine Learning and the Heinz College (Schools of Public Policy, Information Systems, and Management). Students will gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy domains.
PhD in Neural Computation & Machine Learning
This Joint PhD program trains students in the application of machine learning to neuroscience and neural inspired machine learning algorithms by combining core elements of the ML PhD program and the Program in Neural Computation (PNC) offered by the Center for the Neural Basis of Cognition (CNBC).
The MLD requirements for graduation with a Joint-ML PhD degree are the same as those for the regular MLD PhD (including the requirement for the PhD thesis committee composition), with only the following differences:
- A Joint-ML PhD thesis will be a contribution to the combination of Machine Learning and the other field.
- The single elective course, the speaking and writing skills requirements, and the Data Analysis requirement (10718) may be satisfied within the student’s home department.
- A Joint-ML PhD student is still required to TA twice, but only one TA-ship has to be within MLD
A student 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 an MS from MLD they must satisfy all the relevant requirements.
||For questions send email to: firstname.lastname@example.org||Statistics PhD Online Application|
||For questions send email to: email@example.com||Public Policy PhD Online Application|
|For questions send email to: firstname.lastname@example.org||Neural Computation PhD Online Application|
|For questions send email to: email@example.com||Machine Learning PhD Online Application|
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 a 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.