Carnegie Mellon University School of Computer Science

Source: https://ml.cmu.edu/academics/joint-ml-phd Parent: https://www.cs.cmu.edu/academics/overview-programs

Academics Joint Machine Learning Ph.D. Programs

Students interested in a machine learning joint Ph.D. should first apply to the Ph.D. program that best aligns with their research interests (e.g., machine learning, statistics, neuroscience, public policy, or social and decision sciences).\

The MLD requirements for graduation with a joint machine learning Ph.D. are the same as those for the regular MLD Ph.D. (including the requirement for the Ph.D. thesis committee composition), with the following differences:

A joint ML Ph.D. thesis will be a contribution to the combination of machine learning and the other field.
One elective course, the speaking and writing skills requirements may be satisfied within the student’s home department.
A joint ML Ph.D. student is still required to TA twice, but only one TA-ship has to be within MLD.

#### Special Notes for Joint Ph.D. Programs:

Statistics and Machine Learning Joint Ph.D. Program

Students must choose from the menu core courses with a prefix in a department that is not their home department.
Students may satisfy the Practice course requirement through the Advanced Data Analysis project in Statistics.

Neural Computation and Machine Learning Joint Ph.D. Program

Students may satisfy the Practice course requirement by completing a data-intensive project for their second year milestone.

Heinz and Machine Learning Joint Ph.D. Program

Students complete the Practice course requirement in their program.

A student pursuing a joint ML Ph.D. may earn an M.S. degree along the way, either from their home department or from MLD, but not from both. To earn an M.S. in research from MLD, they must satisfy all the relevant requirements.

Ph.D. in Statistics and Machine Learning

This Ph.D. program differs from the Machine Learning Ph.D. program in that it places significantly more emphasis on preparation in statistical theory and methodology. Similarly, this program differs from the Statistics Ph.D. program in its emphasis on machine learning and computer science. The Joint Ph.D. Program in Machine Learning and Statistics is aimed at preparing students for academic careers in both computer science and statistics departments at top universities or industry.

Students in the program must be advised by a faculty member 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 members must be identified at the time of admission to the joint program.

Note: MLD students can apply for this program after they have completed the courses and have a sponsoring faculty in statistics to make the case for admission.

Fill out the Machine Learning Ph.D. Online Application if you are already a Statistics Ph.D. student.

Statistics Joint Program Requirements

Statistics Ph.D. Online Application Machine Learning Ph.D. Online Application

For Statistics Department questions, email admissions@stat.cmu.edu\ For Machine Learning Department questions, email ml-phd-admissions@cmu.edu

Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is operated jointly by faculty in machine learning and CMU's Heinz College (which has 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.

Fill out the Machine Learning Ph.D. Online Application if you are already a Heinz Ph.D. student.

Public Policy Joint Program Requirements

Public Policy Ph.D. Online Application Machine Learning Ph.D. Online Application

For Public Policy questions, email [hnzadmit@andrew.cmu.edu](mailto:hnzadmit@andrew.cmu.edu)For Machine Learning Department questions, email ml-phd-admissions@cmu.edu

Ph.D. in Neural Computation and Machine Learning

This joint Ph.D. program trains students in the application of machine learning to neuroscience and neural inspired machine learning algorithms by combining core elements of the ML Ph.D. program and the Program in Neural Computation (PNC) offered by the Neuroscience Institute (NI).

Fill out the Machine Learning Ph.D. Online Application if you are already a Neural Computation Ph.D. student.

PNC Joint Program Requirements

Neural Computation Ph.D. Online Application Machine Learning Ph.D. Online Application

For Neuroscience Department questions, email [pnc-admissions@cnbc.cmu.edu](mailto:pnc-admissions@cnbc.cmu.edu)For Machine Learning Department questions, email ml-phd-admissions@cmu.edu

Ph.D. in Autonomous and Human Decision Making

This joint Ph.D. program trains students in both the technology of AI and human decision science, focusing on how and when AI can complement human decision-making. Students will be trained in fundamentals of AI, autonomous decision-making, fundamentals of human decision and behavioral science, cognitive models of decision-making, and the societal impact of AI technologies. This program is offered jointly by faculty in machine learning and social and decision sciences.

Fill out the Machine Learning Ph.D. Online Application if you are already a SDS Ph.D. student.

Autonomous & Human Decision Making Joint Program Requirements

SDS Ph.D. Online Application Machine Learning Ph.D. Online Application

For Social and Decision Sciences Department questions, email [John Miller](mailto:20jm7t@andrew.cmu.edu)For Machine Learning Department questions, email ml-phd-admissions@cmu.edu

To Be Considered for a Joint ML Ph.D.

To apply to a joint ML Ph.D. program, a student must already be enrolled in one of the participating Ph.D. programs in machine learning, statistics, PNC, Heinz or SDS.

Before applying, a student must meet the following MLD requirements (in addition to any requirements from the other relevant department):

Take 10-715, 36-705 and *10-716 and earn at least an A- in your first attempt to take each course. Letter grades are required. (Students who took courses before June 2023 will be grandfathered in under the previous requirement of B+ for the courses already taken.)
Identify an MLD core faculty member who agrees to serve as their MLD mentor.

*Note to Fall 2026 Joint Machine Learning Ph.D. Program Applicants: Since 10-716 will not be offered in spring 2026, we have adjusted the prerequisite requirements for the current admissions cycle. Applicants will need to complete 36-705, 10-715, and satisfy the third course requirement by taking one of the following: 10-716, 10-708 or 10-725.

Applications must be submitted by May 31.

How To Apply

Once you've taken the required courses, follow the instructions below to apply. Submit your online application by May 31.

Include the following information:

Statement of Purpose — Why do you want to pursue the joint Ph.D.?
Your updated CV.
Your unofficial Carnegie Mellon transcript, including your letter grades for 10-715, 36-705 and 10-716. Note: For consideration for Fall 2026, applicants may replace 10-716 by taking either 10-708 or 10-725.
Your GRE and TOEFL scores (if applicable) from your original application to your current Ph.D. program.
Recommenders: (1) Ask your adviser to send a letter of recommendation with their agreement that the joint program would be a good thing for you to pursue and how it would benefit your research. (2) Ask your ML core faculty mentor to send a letter of recommendation including why you would be a good fit for the joint program.

The online application opens January 15 and deadline is May 31.

The Role of the MLD Mentor

Provides ML expertise, advice and oversight to support the student’s research.
Influences the student’s research direction to ensure that their Ph.D. thesis makes a sufficient contribution to machine learning to warranty a joint Ph.D. in machine learning. (A joint ML Ph.D. thesis will make a contribution to the combination of machine learning and the other field). For this influence to be successful, a mentor must engage with the student early in their research exploration.
Meets with the student at least once per semester, preferably including the student’s home adviser, to discuss progress and plans. The student is responsible for scheduling the meeting.
Maintains contact with the student’s home adviser.
Represents the student in MLD’s end-of-semester Ph.D. student review.
The MLD mentor does not have a financial responsibility to the student, unless otherwise agreed upon in advance.