Carnegie Mellon University

Overview

The Joint Ph.D. Program in Machine Learning and Public Policy at Carnegie Mellon University equips students with the skills to develop advanced machine learning techniques and apply them to pressing public policy issues. This interdisciplinary program fosters expertise in both the technical aspects of machine learning and relevant policy research, preparing graduates for impactful careers in academia, government, and industry.

What you'll study

In this program, students will engage in a rigorous curriculum that combines core doctoral coursework in machine learning and policy. The curriculum typically includes:

• Core doctoral coursework: Advanced studies in machine learning and related technical fields, in addition to policy and methodological courses pertinent to the student’s dissertation.
• Research training: Collaboration with faculty and research groups through reading sessions, seminars, and milestones in dissertation research.
• Dissertation: A comprehensive Ph.D. dissertation that contributes to the understanding of the intersection between machine learning and public policy.
• Scholarly development: Opportunities for presentations, conference participation, and academic writing, aligned with doctoral standards.

Students must first gain admission to a participating Ph.D. program at Heinz College, such as the Ph.D. in Public Policy and Management or the Ph.D. in Information Systems and Management, and then pursue the joint program requirements alongside their home program.

Entry requirements

Admission to the program is competitive, and there is no universal minimum GPA or standardized test score requirement. Decisions are based on the overall strength of the applicant’s academic background, research potential, and compatibility with faculty and research interests. Additionally, applicants whose previous education was not conducted in English must demonstrate proficiency through standardized tests such as TOEFL, IELTS, or Duolingo.

Application materials

• Online Application Form
• Transcripts
• Standardized test scores (GRE or GMAT), if required
• English language proficiency test results (if applicable)
• Letters of recommendation
• Professional résumé
• Essays detailing research interests and fit with the program

Career prospects

Graduates of the Joint Ph.D. Program in Machine Learning and Public Policy can pursue diverse career paths in academia, research institutions, government agencies, and private sector organizations. They are uniquely positioned to influence policy development and implementation through the application of machine learning methodologies, contributing to data-driven decision-making in various public sectors.