Ph.D. Programs

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These pages provide an overview of the department and its activities for prospective Ph.D. students. Official department policy is contained in the Department of Statistics Graduate Student Handbook, which is available upon request from Chad Schafer, the Director of Graduate Studies.


Dear Student:

Thank you for taking a look at the program of graduate studies in the Department of Statistics at Carnegie Mellon University. The field of Statistics has an impact in almost every academic discipline. Our graduates are prepared for and in demand for industrial, academic and government positions.

Our graduate program trains students in basic theory, methodological and computational skills for data analysis, and in interacting with quantitative researchers who have statistical problems they wish to solve. Students who obtain a Master's degree are prepared for jobs in which they might analyze data, design experiments, model random phenomena, and serve as a consultant. They also will have an understanding of the power and limitations of the tools they learn to use. Our Ph.D. curriculum offers comprehensive training in theory, applied statistics, computational methods, and cross-disciplinary research. An important component of the program is year-long data analysis project supervised in part by interested researchers outside of the Department. In addition, we provide students with opportunities for research apprenticeships and other experience with applications.

The Department's reputation is based primarily on the research in theory and methodology conducted by our faculty and students, our strong commitment to cross-disciplinary work, our emphasis on computing in both research and education, and the substantial involvement of our faculty in governmental, professional, and editorial work. One indication of its leadership is the Department's presence on editorial boards of major professional journals. Editorships of the Journal of the American Statistical Association have been held on three occasions by members of our faculty, and Statistical Science, Chance, and the Journal of Computational and Graphical Statistics were founded by our faculty members. Over the years our faculty have served on the editorial boards of many of the major journals in statistics, biometrics and psychometrics.

Our Department is organized so that students receive a lot of individual attention. All graduate students have daily contact with faculty members; graduate student offices are interspersed among the faculty offices which ensures constant interaction. We have roughly 45 masters and Ph.D. students combined and about 23 faculty members, so class sizes are quite small. As you will see as you browse this web site, the faculty members are very talented and especially enthusiastic about working with students. We try to create a harmonious, non-competitive learning environment for our students. When we accept a student, we do so with the expectation that the student will complete his or her degree program successfully, and we work very hard to help the student achieve that goal.

To support research and teaching, the Department has outstanding computing facilities, which are for the exclusive use of our faculty, students, and staff. The main component is a large network of workstations, which are powerful computers that enable users to edit text, perform numerical calculations, and display graphical output in a time-sharing environment. The department also has a network of processors dedicated to applications requiring parallel processing. All of our graduate students have easy access to all of the computing facilities, and this helps prepare them for the computing challenges they will face in the future.

As you navigate this web site, I hope you will sense the excitement we have about the field of statistics and will begin to appreciate the outstanding group of faculty and students in our Department. You will, no doubt, have questions about our program. I encourage you to call our director of admissions, myself, or any other faculty member with your questions, and if possible, please come to visit us.

Dear Fellow Student:
We welcome your interest in Carnegie Mellon's graduate statistics program.
We all have recent experience in applying to graduate programs in probability and statistics. We greatly appreciate that your choice of school is important in establishing who you will become as a professional and/or scholar.
At CMU, you will be challenged to think for yourself and to set and attain goals. You have your own background and so you get to set your own objectives in how you spend your time here. Some students decide to take two years of masters level work; some take one year. Others with more extensive backgrounds in mathematics and statistics may take one or more of the Ph.D. core courses their first year, and then supplement this coursework with the more application-oriented master's level classes in subsequent years.
Classes are taught by faculty who have established themselves in the statistics research community as dedicated scholars, but we can attest that they also make the time and effort to teach and supervise our work, and to be accessible outside of class. Faculty promotion decisions emphasize teaching ability in addition to research ability.
Another resource available is a state-of-the-art computing environment. In classes and research, you will use widely known statistical software. In addition, you will be frequently encouraged to solve problems using your own programming. Furthermore, statistical computing as an area of research is of interest to some faculty and students in the department. The statistics department has strong research ties to Carnegie Mellon's computer science department, computer and electrical engineering department, and the Pittsburgh Supercomputing Center.
Nearly half of the Department's 40 server nodes are for the exclusive use of statistics graduate students, so we rarely have any difficulty getting access to these machines. A system administrator is available for hardware and software concerns. Computing is a heavily emphasized element in many student activities, including research projects with faculty, coursework, and thesis research--of course, some would add computer games to this list!
Yes, we have our fun too. Friendships develop outside of work, and groups will often go out to one of the great nearby restaurants, bars, museums, cultural institutions, or sporting events. We also have teams in several intramural sports, including basketball, softball and water polo. There are many city and state parks in and around Pittsburgh for year-round recreation, and we sometimes organize short excursions in the great outdoors.
Indeed, Pittsburgh has shed its reputation of heavy air pollution, mainly because of its dramatic shift away from the once predominant steel manufacturing industry. Our quality of life is characterized by inexpensive housing, a low crime rate, excellent public transportation, and a climate with a pleasant change of seasons. Although often thought of as a mid-western city, Pittsburgh is also near the mid-Atlantic East and does not suffer the harsh winters experienced by many cities in the northern Midwest.
We are confident that this guide will help you better understand what studying probability and statistics in our department is like, and we encourage you to visit and see for yourself.
Click here to read additional comments from current graduate students and alumni.

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The Ph.D. in Statistics

The program of study leading to the degree of Doctor of Philosophy in Statistics seeks to strike a balance between theoretical and applied Statistics. The Ph.D. program prepares you for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods.

Cross Disciplinary Programs

The Department offers a Ph.D. in conjunction with the Department of Engineering and Public Policy (EPP). Research in EPP focuses on four main areas: energy and environmental systems, information and communication technology policy, risk analysis and communication, and technical innovation and R&D policy. These are areas which clearly require statistical methodology; students in this joint program will focus on developing novel methodology to address such challenges.
This program differs from the standard Statistics Ph.D. program in its emphasis on machine learning and computer science. Students in this track will be involved in courses and research from both the Departments of Statistics and Machine Learning. Thesis research must be either co-supervised by a faculty in ML and a faculty in Statistics, or supervised by a faculty member who holds a joint appointment in Statistics and Machine Learning. More information can be found here.
This program, which typically takes 5 years, allows students to pursue a Ph.D. that combines Ph.D.-level training in statistics with a solid understanding of the elements of neuroscience, as in the Ph.D. Program in Neural Computation. Students complete the requirements for a Ph.D. in Statistics while also fulfilling the core requirements for the Ph.D. Program in Neural Computation by taking courses in cellular, cognitive, and systems neuroscience, as well as computational neuroscience, and gaining exposure to methods of data collection in at least one experimental laboratory.
The Department offers a joint program in collaboration with the H. John Heinz III School of Public Policy and Management, leading to a Ph.D. in Statistics and Public Policy. This five-year program provides students with comprehensive preparation at the Ph.D. level in both statistics and public policy. The curriculum draws on existing courses in both Statistics and the H. John Heinz III College, recognizing that selected courses can meet, simultaneously, the usually-separate objectives of the Ph.D. programs in Statistics and Public Policy. Critical to the success of the joint program is the close collaboration among faculty members in Statistics and the H. John Heinz III College.