statistics phd placement

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression (new 2021-22)
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every year (due May 1), students are expected to fill out the Annual Progress Review . 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
• Intel Corporation
• Berry Consultants

Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

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College of Liberal Arts and Sciences

Department of Statistics

Ph.d. placements.

After they graduate, alumni of the statistics Ph.D. program go on to work at colleges and universities in research, teaching, and tenure-track positions. They also take on a wide variety of roles in public and private organizations.

Recent Graduates

Learn more about the first jobs our Ph.D. alumni land after earning their degrees.

Class of 2023

Prince Allotey , Asst. Professor of Practice, University of Washington Anhar Aloufi Eric Baron , Senior Biostatistician, Servier Pharmaceuticals Xiaolin Chang , Biostatistician, Moderna Jianmin Chen Simiao Gao , Postdoctoral Research Fellow, Yale University Zijian Huang , Quantitative Analytics Specialist, Wells Fargo Yakov Khariton Jackson Lautier , Asst. Professor,  Bentley University Jung Wun Lee , Postdoctoral Research Fellow, Harvard T.H. Chan School of Public Health Jing Li Yifan Li , Senior Consultant, Ernst & Young Daeyoung Lim , Mathematical Statistician, CDER, FDA Zhongmao Liu , Mathematical Statistician, CDER, FDA Austin Menger , Founder, Menger Analytics Namitha Pais , Postdoctoral Research, U.S. Dept. of Agriculture Jiwon Park , Postdoctoral Fellow, Johns Hopkins University, Dept. of Epidemiology Jingyu Sun , Statistical Data Scientist , Revolution Medicines Boyang Tang , Mathematical Statistician, FDA Patrick Toman , Data Scientist, Hartford Steam Boiler Ziyang Wang , Wells Fargo Ganchao Wei Hao Wu , Mathematical Statistician, CBER, FDA Meiruo Xiang , Mathematical Statistician, CDER, FDA Zehan Yang Yelie Yuan , CCB Risk Program Associate, JP Morgan Chase Katherine Zavez , Postdoctoral Research, UConn Haiwei Zhou , Senior Statistician, AbbVie

More Ph.D. Placements

Yuen Tsz (Abby) Lau , Postdoctoral Associate, University of Pennsylvania, Biostatistics

Sai Ma , Senior Biostatistician, Vertex Pharmaceuticals

Lubing Wang , Senior Associate, Travelers

Zhiyong Hu , Data Scientist, Microsoft

Srawan Bishnoi , Teaching Asst. Professor, University of Pittsburgh

Cheng Huang , Statistician, Vir Biotechnology

Wenlin Yuan , Statistician, Moderna

Hongfei Li , Principle Biostatistician, Incyte Corporation

Yiming Zhang , Mathematical Statistician, FDA

Md. Tuhin Sheikh , Postdoctoral Associate, Yale University

Chiranjit Dutta, Applied Researcher, Ebay

Wei Shi , Senior Statistician, Modeling & Simulation, Amgen

Yaqiong Yao , Postdoctoral Research Scientist, Columbia University, Biostatistics

Wanwan Xu , Postdoctoral Associate, Yale University

Xiaomeng Li, Senior Consultant, Ernst & Young

Biju Wang , Senior Biostatistician, Johnson & Johnson

Yan Li , Postdoctoral Fellow, University of Michigan, Biostatistics

Soumik Banerjee , Visiting Asst. Professor, Binghamton University

Jiyeon Song , Postdoctoral Fellow, University of Michigan, Biostatistics

Zhe Sun , Post Doc, Yale University

JooChul Lee , University of Pennsylvania, Dept. of Biostatistics and Epidemiology

Ziqi Yang , Ant Group

Qingyang Liu , Incyte Corporation

Jinjian Mu , Ohio State University, College of Nursing

Shuang Yin , Transamerica Insurance

Ellis Shaffer , S & P Global

Cheng Zhang , Novartis

Qi Qi , Genetech

Zhe Wang , Asst. Prof., Denison University

Aritra Halder , Asst. Prof., University of Virginia, Bio-Complexity Institute

Lijiang Geng , Boehringer Ingelheim, Shanghai

Yulia Sidi , Takeda

Xiaokang Liu , University of Pennsylvania, Postdoc

Jieying Jiao , Travelers Insurance

Chaoran Hu , Eli Lilly and Company

Renjie Chen , Zillow

Kangyang Liu , CTO Office, Motorola Solutions Company

Yishu Xue , Travelers Insurance

Yang Liu , Upstart

Tairan Ye , Liberty Mutual Insurance

Matthew Henry Linder , Oden Technologies

Qian Meng , Trinity

Disheng Mao , Microsoft

Wenjie Wang , Eli Lilly and Company

Paul McLaughlin , Penn State University

Di Zheng , Liberty Mutual Insurance

Yeongjin Gwon , University of Nebraska Medical Center

Jun Hu , University of Vermont

Hao Li , Boehringer Ingelheim

Shariq Mohammed , University of Michigan

Ruochen Zha , Mapfre Insurance

Fan Zhang , Pfizer

Chen Zhang , Travelers Insurance

Yan Zhuang , Connecticut College

Yaohua Zhang , Vertex Pharmaceuticals

Ved Deshpande , Ebay

Aditya Mishra , Flatiron Institute

Abhishek Bishoyi , Selective Insurance

Sudeep Bapat , University of California, Santa Barbara

Jing Wu , University of Rhode Island

Daoyuan Shi , Vertex Pharmaceuticals

Chongliang Luo , UConn Health Center

Yujing Jiang , Colorado State University

Gregory Vaughan , Bentley University

Dooti Roy , Boehringer Ingelheim

Wei Fu , Mapfre Insurance

Brian Bader , KPMG

Abhisek Saha , MD Anderson Cancer Institute

Qianzhu Wu , Liberty Mutual Insurance Co.

Yu-Bo Wang , National Institute of Child Health & Human Development (NICHD/NIH)

Chun Wang , Liberty Mutual Insurance Co.

Hee-Koung Joeng , Merck

Patrick Harrington , Genomic Health, Inc.

Bo Zhao , Travelers Insurance

Dooti Roy , Biostatistician, Boehringer Ingelheim

Zhuo Wang , Shenzen University

Volodymyr Serhiyenko , Metabiota

Guang Ouyang , Data Scientist, Id Analytics

Chantal Larose , Assistant Professor, SUNY New Paltz

Gyuhyeong Goh , Assistant Professor, Kansas State University

Danjie Zhang , Biostatistician, Gilead Sciences, Inc.

Swarnali Banerjee , Assistant Professor, Old Dominion College

Sankha Perrera , Linear Squared

Hongwei Shang , Hewlett Packard

Ashok Chaurasia , Postdoctoral Fellow, National Institute of Child Health & Human Dev.

Gong-Yi Liao , Northern Trust

Hongwei Shang , Researcher, Hewlett Packard Labs

Jennifer Boyko , Biostatistician, Boehringer Ingelheim

Sairam Rayaprolu , Decision Science Consultant, Disney

Steven Chiou , Assistant Professor, University of Minnesota, Duluth

Valerie Pare , Wesleyan University

Xiao (Leo) Wang , Assistant Vice President, Barclay’s

Xun Jiang , Statistician, Amgen

Bhargab Chattopadhyay , Assistant Professor, University of Texas at Dallas

Hui Yao , Ernst & Young

Karthik Bharath , Visiting Assistant Professor at Department of Statistics, Ohio State University

Ran Liu , Biometrician, Merck

Rui Wu , Statistician, Novartis

Wenqing Li , Novartis

Yuanye Zhang , Senior Biostatistician, Novartis

Ziwen Wei , Statistician, Merck

Arijit Sinha , Novartis

Debanjan Bhattacharjee , Assistant Professor, Department of Mathematics, Utah Valley University

Gregory Matthews , Postdoctoral Research Associate, School of Public Health, University of Massachusetts

Jeffrey Stratton , Postdoctoral Research Associate, Department of Mathematics, University of Massachusetts, Amherst, MA.

Marcos Prates , Federal University of Minas Gerais

Miaomiao Ge , Statistician, Boehringer Ingelheim

Xiaojing Wang , Google, New York City

Sandra Hurtado-Rua , Postdoctoral Associate Division of Biostatistics and Epidemiology Department of Public Health Weill Medical College of Cornell University

Yuchen Fama , Statistician, Travelers, Hartford, CT.

Balaji Raman , Visiting Assistant Professor, Department of Statistics, Yale University, CT

Elijah Gaioni , IBM, TJ Watson, NY

Jian Zou , Postdoctoral Fellow, NISS, North Carolina

Patrick Joyce , Census Bureau

Wangang Xie , Statistician, Abbott Laboratories, Illinois

Xia Wang , Postdoctoral Fellow, NISS, North Carolina

Yifang Zhao

Sourish Das , Postdoctoral Fellow/Visiting Faculty, SAMSI/Duke University, Durham, NC

Yingmei Xi , Averion International Corp., Southborough, MA

Fang Yu , Biostatistics, University of Nebraska, Omaha

Feng Guo , Department of Statistics, Virginia Tech University, Blacksburg, VA

Jaydip Mukhopadhyay , Bristol Myers Squibb, Wallingford, CT

William Pepe , AT&T, NJ

Changhong Song , Biostatistician at EMMES Corporation in Rockville, Maryland.

Samiran Ghosh , Mathematical Sciences Dept, Indiana University Purdue University at Indianapolis

Sonali Das , Council for Scientific and Industrial Research, Pretoria, South Africa

Ulysses Diva , Bristol Meyers & Squibb, Wallingford, CT

Hai Xu , Statistician, St. Paul Travelers, Hartford, CT.

Seongho Song , Department of Mathematics, University of Cincinnati, Ohio.

Zhaohui Liu , Senior Biostatistician, Novartis, Inc., NJ.

Zhenkui Zhang , Statistical Analyst, Liberty Mutual Bank, Boston, MA.

Anandamayee Majumdar , Department of Mathematics, Arizona State University, Arizona

Lan Huang , National Cancer Institute, National Institute of Health, Rockville, Maryland.

Madhuja Mallick , Biostatistician, Merck Research Laboratories.

Prashni Paliwal , Biostatistician, Bristol-Myers Squibb, Wallingford, CT

Shanshan Wu , Statistician, ING Clarion, New York, NY

Amitabha Bhaumik , Bristol-Myers-Squibb

Junfeng Liu , Department of Statistics, Case Western Reserve University, Cleveland

Rongwei Fu , School of Public Health, Oregon Science and Health University, Portland, Oregon

Greg Cicconetti

Jun Ying , Professor, Department of Environmental Health, Division of Biostatistics and Bioinformatics, University of Cincinnati

Athanasios Micheas , Department of Statistics, University of Missouri, Columbia, MO

Deepak K. Agarwal , Yahoo Corporation

Zhen Chen , Biostatistics, University of Pennsylvania

Athanasios Kottas , University of California, Santa Cruz, CA

Fei Wang , Assistant Professor,Public Health School, Boston University

Kaushik Patra , Center for Biostatistics and AIDS Research, Harvard University, Cambridge, MA

Keith Holler , St. Pauls Travelers Insurance, Hartford, CT

Sudipto Banerjee , Biostatistics Dept, University of Minnesota

Kenneth Klesczewski , Research Statistician, Bristol Myers Squibb , Wallingford, CT

William Duggan , Research Statistician, Pfizer, Groton, CT

Hui-May Chu , Statistician/Computing Scientist, CPBD, Inc. Cambridge, MA

Jie Chen , Statistician, Computing Service Dept. UMASS, Boston, MA

Murali Niverthi , Lincoln National Corp, Fort Wayne, IN

Malini Iyengar , Glaxo Smith Klein, Philadelphia, PA Mark Ecker , University of Northern Iowa, Cedar Falls, IA Zuqiang Qiou , Vital Computer Services International Inc., Florham Park, NJ

Dan Larose , Central Connecticut State University, New Britain, CT

Kuo-ren Lou , Tamkang University, Taiwan, ROC

Marco Bonetti , Institute of Quantitative Methods, Universita Bocconi, Milano, Italy

Pantelis Vlachos , Carnegie Mellon University, Pittsburgh, PA

Sujit Ghosh , North Carolina State University, Raleigh, NC

Cristina Sison , North Shore University Hospital, NY

Hong Chang , Cooper and Lybrand, Boston, MA

Jeffrey Pai , University of Manitoba, Canada

Sujay Datta , Texas A&M University, College Station, TX

Tae Yang , Myongji University, Korea

Bani Mallick , Texas A&M University, College Station, TX

Fengchun Peng , Department of Biostatistics, University of Rochester

Lea R. Birmiwal , Birminal Investment Trust

Sujit Sahu , School of Mathematics, University of Southampton, U.K.

Saibal Chattopadhyay , Indian Institute of Management, Calcutta, India

Tai-Ming Lee , Department of Statistics, Fu Jen Catholic University, Taiwan

Constantine Yiannoutsos , Department of Biostatistics, Indiana University-Purdue University

Tumulesh Solanky , Professor,Department of Mathematics, University of New Orleans

Younshik Chung , Professor, Pusan National University, Korea

Brad Carlin , School of Public Health, Division of Biostatistics, University of Minnesota

Daniel Miller , Central Connecticut State University

Mabel H. Moreno

Pei-San Liao , Fu Jen Catholic University, Taiwan

Phyllis Schumacher , Mathematics Department, Bryant College

John Judge , Westfield State College

James Kenyon , Senior Research Biostatistician, Bristol-Myers Squibb

Patrick Cantwell , Business Div, Bureau of the Census

Silvi Liberman , Statistics Department, Temple University (Deceased)

Steve Leeds , President, The Marketing Investigators

Suryoguritno Darmanto , Godjah Mada University, Indonesia

Marsha Davis , Math and Computer Sciences Dept, Eastern Connecticut State University

Robert Leighty , Department of Statistics, Ohio State University

John F. Carter , Donnelley Marketing Information Services

William R. Stephenson , Department of Statistics, Iowa State University

Eleanor J. Dietz , Mathematics Dept., Meredith College, Raleigh, WC

Brian W. Woodruff

Lawrence C. Berger

Michael A. Fligner , Statistics Dept., Ohio State University

Michael J. Barthel , DC Dept. of Human Services

Labib J. Abdunnur

Richard C. Murphy

Teng-Shan Weng

William A. Powers III

David F. Bauer , Math Science, Virginia Commonwealth University

Oswald G. DeLisser

Richard H. Lavoie , Dept. of Mathematics, Providence College

Bodh R. Gulati , Southern Connecticut State University

David L. Fulton

Matthew Goldstein , Chancellor, CUNY

Mrs. Gene S. Sogliero

Shadeo K. Badhe

Chris P. Tsokos , Dept. of Mathematics, University of South Florida

Donald Fridshal

Jimmie Dale Woods

Leigh Harrington

David Salsburg , Statistical Consultant (our very first Ph.D.)

Vidya S. Taneja , Dept. of Mathematics, Western Illinois University

Class of 2022

Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

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Department of Statistics

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• Open Positions
• PhD Program

The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the  basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

PhD Program Requirements

PhD Program Admissions Process

• PhD Admissions FAQ
• AM in Statistics
• Stat 300: Research in Statistics
• Stat 303: The Art and Practice of Teaching Statistics

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PhD Program

The PhD Statistics program provides excellent training in the modern theory, methods, and applications of statistics to prepare for research and teaching careers in academia or industry, including interdisciplinary research in a wide array of disciplines. The median time to degree is five years.

Students will take courses in modern theory, methods, and applications of statistics, demonstrate mastery of this material via a qualifying examination, and then conduct statistical research under the supervision of one of the many regular or affiliate faculty members in the department, resulting in a dissertation.

The PhD qualifying examination is primarily based on the first-year curriculum. Most students pass at the end of the summer after the first year of the program. Students select between two versions of the examination, one with questions from mathematical statistics and probability or the second with questions from mathematical statistics and statistical methods.

Graduates are prepared for positions in academia, business, or government. They have taken positions at leading universities such as UC-Berkeley, Penn, and Yale and at top companies such as Google, Facebook, and Eli Lilly. The department strives to support students in the PhD program as teaching, research, or project assistants.

Questions about our Statistics PhD Programs can be directed to our graduate program coordinator at  [email protected] .

Resources, Regulations, and Policies

• Statistics PhD Handbook 2023-2024 More
• Criteria for Satisfactory Progress More
• Current PhD Regulations More
• 2014 PhD Regulations More

PhD Statistics Program Options

There are two program options students can select from – PhD Statistics, Statistics Option or PhD Statistics, Biostatistics Option . 

We have a single admissions process for both options and we encourage applicants to select only one of the options and not list both when applying. Selection of the program to which you apply has very little influence on the admissions decision. If you are unsure of which program option to choose, students who enter our PhD program may readily switch between the programs. 

Please note that the Department of Biostatistics and Medical Informatics has a separate PhD program in Biomedical Data Science that is distinct from the programs in the Department of Statistics.

Statistics Option

Career Outcomes : Students will be prepared for research and teaching careers in academia, industry, and other disciplines.

Coursework : Students will take courses in several broad areas of statistical methods and theory. This includes two-semester sequences in mathematical statistics and in statistical methods, either a course in probability theory or a course in statistical computing, a statistical consulting course, and a wide variety of elective options.

Biostatistics Option

Career Outcomes : Students will be prepared for careers in clinical research, genetics, drug testing, and experimental design in academia, government, and private sector.

Coursework : Students in the Biostatistics named option complete the same required courses as are in the Statistics named option, but have additional required coursework in biostatistics and biology and fewer elective course requirements.

Applying to the PhD Statistics Program

The application deadline is December 1 for a fall term start (no spring admissions).   A reminder to only list either the Statistics Option or Biostatistics Option in your application, not both. Again, students who enter the PhD program in Statistics can readily switch between the programs.

We welcome applications from around the world and strive to admit well-qualified applicants who are interested in the diverse array of research interests of our faculty. We do not make preliminary evaluations of any applicant inquiry based on email communication. No decision will be made until after the deadline has passed and a completed file (including the application fee) has been received.

Before applying to the Statistics Department, please read the Graduate School Frequently Asked Questions. Note that there is a non-refundable application fee. Applicants whose native language is not English, or whose undergraduate instruction was not in English, must provide an English proficiency test score.

To be considered for financial assistantship, all required application materials listed below should be submitted via the electronic application at https://apply.grad.wisc.edu/ by the December 1 deadline.

• Letters of Recommendation
• Transcripts
• Statement of Purpose
• CV or Resumé
• Supplemental Application (Including a List of Courses)
• English Proficiency
• A minimum of three (3) letters of recommendation to be submitted electronically by the recommenders.
• The online application for admission asks for the name and email contact information of the references from whom you request recommendations. A recommendation request will be sent, by email, to each of your references. The email will include your name with a link to each department’s electronic recommendation form. The request can be sent at any time providing you meet department deadlines. You can change references or send a reminder through your application.
• It is common practice to contact your references ahead of time so that they expect your request.
• After you have submitted your application, you can view receipt of your recommendations through the online status system.
• As part of the online application, please upload a clear and easy-to-read PDF copy of your transcript from each institution of higher learning (post High School) that you have attended. Unofficial transcripts are acceptable. If we offer you admission, you will be asked to provide an official copy of your transcript to the Graduate School at that time. Admission will be contingent upon receiving the official transcript.
• If courses at the institution were not taught in English, we will need an electronic copy of both the transcript in the original language, and the transcript in English.
• Your statement of purpose should include why you feel that the UW-Madison program is a good fit for you, and conversely, why you are a good fit for our program. What are you hoping to work on in the field with your degree? Are there any professors here that you would particularly like to work with? Any research areas in statistics that particularly excite you?
• The overall length of the statement is usually about 2 pages, single or double spaced. You can use whatever font and formatting you are comfortable with.

Please upload a PDF copy of your CV or Resumé to the online application.

A supplemental application is required as part of the online application. You will be asked to answer the following questions and provide the following information:

• Are you applying to the Biostatistics option? Yes/No (There is no advantage to applying to both programs.)
• List any major competitive honors, awards, and/or fellowships you have received.
• List any undergraduate or graduate research experiences.
• Provide a table with all courses you have taken, are currently taking, or plan to take prior to coming to UW-Madison that contain substantial mathematical, statistical, quantitative, or computational content. Include courses from other disciplines such as economics, physics, or engineering, if applicable. Use one row per course with columns for the course number, course title, textbook used (if possible), and grade received (if already completed). Upload this document as a pdf.

The GRE is not required.

• For all international degree-seeking students, see the  Graduate School requirements page  for additional information.

Consult the Graduate School for general information about graduate admissions to the University of Wisconsin-Madison.

If you have any further questions, please email [email protected] . Please include your full name and what semester you are interested in applying for.

• Graduate Studies

Ph.D. Program

The PhD program prepares students for research careers in theory and application of probability and statistics in academic and non-academic (e.g., industry, government) settings.  Students might elect to pursue either the general Statistics track of the program (the default), or one of the four specialized tracks that take advantage of UW’s interdisciplinary environment: Statistical Genetics (StatGen), Statistics in the Social Sciences (CSSS), Machine Learning and Big Data (MLBD), and Advanced Data Science (ADS). 

Admission Requirements

For application requirements and procedures, please see the graduate programs applications page .

Recommended Preparation

The Department of Statistics at the University of Washington is committed to providing a world-class education in statistics. As such, having some mathematical background is necessary to complete our core courses. This background includes linear algebra at the level of UW’s MATH 318 or 340, advanced calculus at the level of MATH 327 and 328, and introductory probability at the level of MATH 394 and 395. Real analysis at the level of UW’s MATH 424, 425, and 426 is also helpful, though not required. Descriptions of these courses can be found in the UW Course Catalog . We also recognize that some exceptional candidates will lack the needed mathematical background but succeed in our program. Admission for such applicants will involve a collaborative curriculum design process with the Graduate Program Coordinator to allow them to make up the necessary courses. 

While not a requirement, prior background in computing and data analysis is advantageous for admission to our program. In particular, programming experience at the level of UW’s CSE 142 is expected.  Additionally, our coursework assumes familiarity with a high-level programming language such as R or Python. 

Graduation Requirements 

This is a summary of the department-specific graduation requirements. For additional details on the department-specific requirements, please consult the  Ph.D. Student Handbook .  For previous versions of the Handbook, please contact the Graduate Student Advisor .  In addition, please see also the University-wide requirements at  Instructions, Policies & Procedures for Graduate Students  and  UW Doctoral Degrees .  

General Statistics Track

• Core courses: Advanced statistical theory (STAT 581, STAT 582 and STAT 583), statistical methodology (STAT 570 and STAT 571), statistical computing (STAT 534), and measure theory (either STAT 559 or MATH 574-575-576).  
• Elective courses: A minimum of four approved 500-level classes that form a coherent set, as approved in writing by the Graduate Program Coordinator.  A list of elective courses that have already been pre-approved or pre-denied can be found here .
• M.S. Theory Exam: The syllabus of the exam is available here .
• Research Prelim Exam. Requires enrollment in STAT 572. 
• Consulting.  Requires enrollment in STAT 599. 
• Applied Data Analysis Project.  Requires enrollment in 3 credits of STAT 597. 
• Statistics seminar participation: Students must attend the Statistics Department seminar and enroll in STAT 590 for at least 8 quarters. 
• Teaching requirement: All Ph.D. students must satisfactorily serve as a Teaching Assistant for at least one quarter. 
• General Exam. 
• Dissertation Credits.  A minimum of 27 credits of STAT 800, spread over at least three quarters. 
• Passage of the Dissertation Defense. 

Statistical Genetics (StatGen) Track

Students pursuing the Statistical Genetics (StatGen) Ph.D. track are required to take BIOST/STAT 550 and BIOST/STAT 551, GENOME 562 and GENOME 540 or GENOME 541. These courses may be counted as the four required Ph.D.-level electives. Additionally, students are expected to participate in the Statistical Genetics Seminar (BIOST581) in addition to participating in the statistics seminar (STAT 590). Finally, students in the Statistics Statistical Genetics Ph.D. pathway may take STAT 516-517 instead of STAT 570-571 for their Statistical Methodology core requirement. This is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript.

Statistics in the Social Sciences (CSSS) Track

Students in the Statistics in the Social Sciences (CSSS) Ph.D. track  are required to take four numerically graded 500-level courses, including at least two CSSS courses or STAT courses cross-listed with CSSS, and at most two discipline-specific social science courses that together form a coherent program of study. Additionally, students must complete at least three quarters of participation (one credit per quarter) in the CS&SS seminar (CSSS 590). This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript.

Machine Learning and Big Data Track

Students in the Machine Learning and Big Data (MLBD) Ph.D. track are required to take the following courses: one foundational machine learning course (STAT 535), one advanced machine learning course (either STAT 538 or STAT 548 / CSE 547), one breadth course (either on databases, CSE 544, or data visualization, CSE 512), and one additional elective course (STAT 538, STAT 548, CSE 515, CSE 512, CSE 544 or EE 578). At most two of these four courses may be counted as part of the four required PhD-level electives. Students pursuing this track are not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript. 

Advanced Data Science (ADS) Track

Students in the Advanced Data Science (ADS) Ph.D. track are required to take the same coursework as students in the Machine Learning and Big Data track. They are also not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. The only difference in terms of requirements between the MLBD and the ADS tracks is that students in the ADS track must also register for at least 4 quarters of the weekly eScience Community Seminar (CHEM E 599). Also, unlike the MLBD track, the ADS is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript. 

Department of Statistics

Last update: 11/10/23

PhD Degree in Statistics

The Department of Statistics offers an exciting and recently revamped PhD program that involves students in cutting-edge interdisciplinary research in a wide variety of fields. Statistics has become a core component of research in the biological, physical, and social sciences, as well as in traditional computer science domains such as artificial intelligence and machine learning. The massive increase in the data acquired, through scientific measurement on one hand and through web-based collection on the other, makes the development of statistical analysis and prediction methodologies more relevant than ever.

Our graduate program prepares students to address these issues through rigorous training in scientific computation, and in the theory, methodology, and applications of statistics. The course work includes four core sequences:

• Probability (STAT 30400, 38100, 38300)
• Mathematical statistics (STAT 30400, 30100, 30210)
• Applied statistics (STAT 34300, 34700, 34800)
• Computational mathematics and machine learning (STAT 30900, 31015/31020, 37710).

All students must take the Applied Statistics and Theoretical Statistics sequence. In addition it is highly recommended that students take a third core sequence based on their interests and in consultation with the Department Graduate Advisor (DGA). At the start of their second year, the students take two preliminary examinations. All students must take the Applied Statistics Prelim. For the second the students can choose to take either the Theoretical Statistics or the Probability prelim. Students planning to take the Probability prelim should take the Probability sequence as their third sequence.

Incoming first-year students have the option of taking any or all of these exams; if an incoming student passes one or more of these, then he/she will be excused from the requirement of taking the first-year courses in that subject. During the second and subsequent years, students can take more advanced courses, and perform research, with world-class faculty in a wide variety of research areas .

In recent years, a large majority of our students complete the PhD within four or five years of entering the program. Students who have significant graduate training before entering the program can (and do) obtain their doctor's degree in three years.

Most students receiving a doctorate proceed to faculty or postdoctoral appointments in research universities. A substantial number take positions in government or industry, such as in research groups in the government labs, in communications, in commercial pharmaceutical companies, and in banking/financial institutions. The department has an excellent track record in placing new PhDs.

Prerequisites for the Program

A student applying to the PhD program normally should have taken courses in advanced calculus, linear algebra, probability, and statistics. Additional courses in mathematics, especially a course in real analysis, will be helpful. Some facility with computer programming is expected. Students without background in all of these areas, however, should not be discouraged from applying, especially if they have a substantial background, through study or experience, in some area of science or other discipline involving quantitative reasoning and empirical investigation. Statistics is an empirical and interdisciplinary field, and a strong background in some area of potential application of statistics is a considerable asset. Indeed, a student's background in mathematics and in science or another quantitative discipline is more important than his or her background in statistics.

To obtain more information about applying, see the Guide For Applicants .

Students with questions may contact Yali Amit for PhD Studies, Mei Wang for Masters Studies, and Keisha Prowoznik for all other questions, Bahareh Lampert (Dean of Students in the Physical Sciences Division), or Amanda Young (Associate Director, Graduate Student Affairs) in UChicagoGRAD.

Handbook for PhD Students

Information for first and second year phd students in statistics.

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The Ph.D. in Statistics is flexible and allows students to pursue a variety of directions, ranging from statistical methodology and interdisciplinary research to theoretical statistics and probability theory. Students typically start the Ph.D. program by taking courses and gradually transition to research that will ultimately lead to their dissertation, the most important component of the Ph.D. program.

These requirements apply to students admitted for Fall 2020 and after. Students admitted in Fall 2019 and earlier should consult the Previous Program Requirements page .

PhD Coursework:

The core PhD curriculum is divided into five areas: 

Methods — STATS 600 and 601

Practice — STATS 604

Statistical Theory — STATS 511, 610, 611

Probability — STATS 510, 620, 621

Computing — STATS 507, 606, 608 

All doctoral students must complete the following in their first three semesters in the program and before advancing to candidacy: 

Take all methods and practice courses (600, 601, 604)

Take at least two courses in the combined areas of statistical theory and probability,  including at least one course in statistical theory and at least one 600-level course 

Take at least one computing course

Achieve a 3.5 average grade  (on the 4.0 scale used by Rackham) in 600, 601, 604, and one 600-level statistical theory or probability course

Not completing requirements 1-4 by the end of the third semester will trigger probation which, if not resolved by the end of the fourth semester, may lead to dismissal from the program.  For more details, see the link below. 

By the end of the PhD program, all students must take at least 30 credits of graduate statistics courses.    All courses from the core areas count towards this total, as well as all 600-level, 700-level, and selected additional  500-level courses with approval of the PhD Program Director. Seminars and independent study courses do not count. At least 21 credits must be at the 600 level or higher.    The Rackham Graduate School requires PhD students to maintain an overall GPA of at least 3.0 to remain in good standing.   

In addition, all doctoral  students must take 3 credits of cognate courses as required by the Rackham graduate school, and two professional development seminar courses.    Cognate courses are 400- and higher-level courses from outside Statistics and not cross-listed with Statistics. All cognate course selections must be approved by the PhD Program Director.   The professional development courses are 

STATS 810, research ethics and introduction to research tools, in the first semester in the program.

STATS 811, technical writing in statistics. Students are strongly advised to complete this course in their second or third year.

Typical Course Schedules:

Our Ph.D. program admits students with diverse academic backgrounds. All PhD students take STATS 600/601  in their first year. Students are strongly encouraged to take STATS 604 in their second year (Stats 600 is a prerequisite).  

Students with less mathematical preparation typically take STATS 510/511 (the Master’s level probability and statistical theory) in their first year and 600-level probability and/or statistical theory courses in their second year.    

Advanced students, for example those with a Master’s degree, typically do not need to take 510/511, and in some cases may skip 610 and 621.   Students who wish to take 600-level probability and statistical theory courses in their first year must take a placement test just before the fall semester of their first year to get approved.  The PhD Program Director will help each student choose their individual path towards completing the requirements.  

Some typical sample schedules are listed below.   In most cases, we do not recommend taking more than three full-load courses per semester (not counting seminars).

Sample schedule 1:

Sample schedule 2:

Advancing to Candidacy:

Students are expected to find a faculty advisor and start research leading to their dissertation proposal no later than the summer after their first year. The PhD Program Director and the faculty mentor assigned to each first year student can assist with finding a faculty advisor. Students are expected to submit a dissertation proposal and advance to candidacy some time during their second or third year in the program.   

Requirements for advancing to candidacy are:

Satisfying Requirements 1-4

Completing at least 3 credit hours of cognate courses

Writing a dissertation proposal and passing the oral preliminary exam, which consists of presenting the proposal to the student's preliminary thesis committee

A dissertation proposal should identify an interesting research problem, provide motivation for studying it, review the relevant literature, propose an approach for solving the problem​, and present at least some preliminary results​. The written proposal must be submitted to the preliminary thesis committee and the graduate coordinator a​head of time (one week minimum, two weeks recommended)​ and then presented in the oral preliminary exam. The preliminary thesis committee is chaired by the faculty advisor and must include at least two more faculty members, at least one of them from Statistics. ​​The faculty on the preliminary thesis committee typically continue t​o serve ​on ​the doctoral thesis committee​​, but changes are allowed.  Please see Rackham rules on thesis committees for more information.  

At the oral preliminary exam, the committee will ask questions about the proposal and the relevant background and either elect to accept the proposal as both substantial and feasible, ask for specific revisions, or decline the proposal. The unanimous approval of the proposal by the committee is necessary for the student to advance to candidacy.

Additional Information:

Students are encouraged to complete the bulk of their coursework beyond Requirements 1-4 in the first two years of study.  Candidates are allowed to take only one course per semester without an increase in tuition.

All PhD students are expected to register for Stats 808/809  (Department Seminar) every semester unless restricted by candidacy, and attend the seminar regularly regardless of whether they are registered.  

Exceptions to the PhD program requirements may be granted by the PhD Program Director.

Annual Report:

Each candidate is required to meet with the members of their thesis committee annually. This could be in the form of either giving a short presentation on their research progress to the thesis committee as a group, or meeting with committee members individually.

Each committee member should complete a Thesis Committee Member Report and return it to the student. The student should share the completed Thesis Committee Member Reports with both the PhD Program Coordinator and their advisor.

All meetings with the committee members should take place by April 15.

Following the meetings, the student and the advisor should complete the Annual PhD Candidate Self-Evaluation and Feedback Form . The advisor should review the committee members’ Thesis Committee Member Reports and take them into account when completing the advisor’s portion. The completed Annual PhD Candidate Self-Evaluation and Advisor Feedback Form must be submitted to the PhD Program Coordinator by May 31. The completed form will be saved with the department, and a copy will be shared with the student.

Dissertation and Defense:

Each doctoral student is expected to write a dissertation that makes a substantial and original contribution to statistics or a closely related field. This is the most important element of the doctoral program. After advancing to candidacy, students are expected to focus on their thesis research under the supervision of the thesis advisor and the doctoral committee. The composition of the doctoral committee must follow the Rackham's  guidelines for dissertation committee service . The written dissertation is submitted to the committee for evaluation and presented in an oral defense open to the public.

Rackham Requirements:

The Rackham Graduate School imposes some additional requirements concerning residency, fees, and time limits. Students are expected to know and comply with these requirements.

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Ph.D. in Statistics Degree

Doctor of philosophy program.

Applying: Please visit  Statistics PhD Admissions .

The aim of the Ph.D. program is to provide comprehensive and balanced training in statistical methods, computational statistics, and the theory of statistics. Particular emphasis is placed on training students to independently recognize the relevance of statistical methods to the solution of specific problems and to enable them to develop new methods when they are needed. The training aims to convey a sound knowledge of existing statistical theory, including the mathematical facility to develop new results in statistical methodology. At the same time, the program is kept sufficiently flexible to permit students to develop their specific interests.

The students seeking a Ph.D. degree must fulfill a number of departmental requirements in addition to   university requirements   specified by the Graduate and Professional School ( GPS ).

The Ph.D. at Texas A&M requires a minimum of 64 credit hours  beyond a Master’s degree or 96 credit hours beyond a Bachelor’s degree.  Statistics Ph.D. students are required to take 36 credit hours of Ph.D. level courses, 4 credit hours of seminar, 2 credit hours of statistical consulting and a sufficient number of research hours to meet the minimum 64/96 credit hour requirement.

Ph.D. Level Courses:

• Core Courses 21 credit hours – STAT 600 , 612 , 613 , 614 , 632 , 648 , 649
• At least two courses 6 credit hours from STAT 605 , 615 , 620 , 624
• At least 9 credit hours from STAT 616 , 618 , 621 , 627 , 631 , 633 , 642 , 647 , 673 , 674 , 677 , 689 , 695 or outside of the department courses upon approval.

Other requirements:

• Four credit hours of STAT  681  — Seminar over 4 semesters.
• Two credit hours of STAT  684  — Statistical Consulting over 2 semesters before the end of the fourth year.
• A sufficient number of research hours STAT 691  to meet the minimum requirement of 64 hours beyond a Master’s degree or 96 credit hours beyond a Bachelor’s degree.

Ph.D. Qualifying Examination

The Ph.D. Qualifying Exam will cover the material from 5 of the 7 required courses: STAT 600 ,  612 , 613 , 614  and  632 . All Ph.D. students must pass the qualifying examination at the Ph.D. level given before the end of the first year.  There are three ratings of the exam: (1) Pass at the Ph.D. level; (2) Pass at the M.S. level; (3) Fail. Students who fail to pass at the Ph.D. level will be given the opportunity to obtain an M.S. degree.

Degree Plan

Students are expected to have selected a thesis advisor and the remainder of the advisory committee before Spring semester of the second year.  The student’s advisory committee, in consultation with the students, will develop a proposed degree plan. Degree plans must be approved by the student’s advisory committee and the Ph.D. program director.

Degree plans must be filed through GPS’ online system before the end of Spring semester of second year and at least 90 days prior to the preliminary examination date.  Any petitions to the degree plan must be submitted at least 3 weeks before the proposed date of preliminary exam.

Preliminary Examination

Completing preliminary exam is a university requirement. The preliminary exam for a doctoral student in the Department of Statistics is an oral exam. The format of the exam shall be determined by the student’s advisory committee and communicated to the student in advance of the examination. The student and committee chair should review and complete  Preliminary Examination Checklist  to verify the student’s eligibility to schedule the preliminary exam. Details are available in   Steps to Fulfill Preliminary Exam  from the GPS .

The Ph.D. Dissertation

After successfully completing the course work and the preliminary examination, a period of time is to be devoted to a research topic in either statistical methodology or statistical theory under the guidance of the student’s advisor. The results of this research must be collected in a written dissertation satisfying the  guidelines  established by the University. The research must constitute an original contribution to the science of statistics and may derive new results in statistical theory or methodology or may be concerned with developing statistical methodology in new areas of application.

Admittance to Candidacy

Prior to students being allowed to take their dissertation defense examination, they must be admitted to candidacy and their current official cumulative and degree plan GPAs must be 3.00 or better. To be admitted to candidacy for a doctoral degree, a student must have:

• Completed all coursework on the degree plan except any remaining 684 and 691,
• A 3.0 Graduate GPA and a Degree Plan GPA of at least 3.0 with no grade lower than C in any course on the degree plan,
• Passed the preliminary examination,
• Submitted an approved dissertation proposal,
• Met the  residence requirements .

The research proposal and its  approval form must be submitted to the GPS at least 20 working days prior to the submission of the Request for the Final Examination .

Final Examination/Dissertation Defense

The dissertation is the major requirement of the Ph.D. degree. Students must complete and pass a final examination by deadline dates announced in the GPS Calendar  each semester. The doctoral student is allowed only one opportunity to take the final examination.

The dissertation defense at Texas A&M is public. The  request to hold and announce the final examination must be submitted to the GPS a minimum of 10 working days in advance of the scheduled date. The student’s advisory committee will conduct this examination. Details of the final exam procedures are described in  Steps to Fulfill Doctoral Degree Requirements from the GPS website.

A growing number of our students decide to pursue doctoral studies after graduation. Graduate placements of MA in Statistics Alumni span several countries, institutions, and diverse fields and disciplines.

To support our qualified MA Students, we offer research and faculty-led hands-on project opportunities.  In particular, the MA Mentored Research Course and select Ph.D. level courses are available. 

MA Mentored Research is a mentored on-campus learning and research program providing our PhD-oriented MA students with research experience. It allows our students to gain experience and skills to grow as researchers.  Topics span various areas, ranging from machine learning and data science to specific problems in financial and medical research.  Meanwhile, faculty mentors receive research support from our students who have rigorous preparation in statistics, data science, and machine learning. 

In addition, our students can apply for the department’s yearly Joint Statistical Meetings (JSM) Conference Awards to present their work, network with statisticians in academia, industry, and government and participate in professional development opportunities. The award covers the full cost of conference registration and a portion of travel and lodging costs.

Our Fall Ph.D. Information Session is open to all MA students. It is moderated by professors and advanced Ph.D. students. The discussion revolves around topics relating to academic preparation and career opportunities in research and the corporate world. 

Students interested in pursuing a Ph.D. are encouraged to register for the Professional Development Course STAT GR 5391 in their first semester.  The first half of the semester of this course supports career building for all MA Students.  The second half offers specific support for the PhD-oriented students. 

• Alumni Profiles

Class of 2017

"I am a first-year mathematical informatics PhD student at the University of Tokyo, Graduate School of Information Science and Technology at the Yamanishi Lab researching model-change detection in online advertising platforms. Studying applied data science taught me how important it was to improve my programming skills. My statistical inference courses gave me a solid theoretical foundation, especially when inventing new algorithms and frameworks, and my machine learning courses gave me an understanding of machine-learning algorithms and their relationships. These are three very essential areas to master in order to become a successful data and/or algorithm scientist."

Class of 2014

"I am currently a Research Engineer at Verizon Media where I am responsible for identifying business trends and discovering information hidden in vast amounts of data, and working closely with the business team to develop and improve new product features. My daily work typically includes creating various machine learning-based tools, conducting end-to-end testing to deliver better products, performing statistical analysis, and doing ad-hoc analysis and presenting results in a clear manner. After completing my MA at Columbia, I joined North Carolina State’s statistics department where I obtained my PhD. My experience in the MA program provided me with a solid foundation in research and methodology. The faculty supported my interests and guided me through the program and after."

Yiran Jiang

Class of 2018

“I am a second year PhD student in Statistics at Purdue University. I received my Bachelors degree in Economics at Fudan University and Masters degree in Statistics at Columbia. I took 15 courses during my MA study, most of them are from the Department of Statistics. These courses helped me build concepts in Statistics and allowed me to continue my study in PhD level courses.”

For more Alumni Profiles, click here.

PhD Placement

Graduates of the MA in Statistics program have been accepted into the following Ph.D. programs:

2021 – 2018 (partial survey results)

2017 and earlier  (partial survey results).

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PhD in Statistics

Phd in statistics program.

The PhD in Statistics prepares students professional leadership in statistical research, teaching and collaboration as faculty at colleges and universities and as researchers at government institutions or in the private sector.

Coursework Requirements

MS in Statistics

Qualifying, Prelim, and Final Exams

Review and Advisory

Travel Funding Policy

PhD Student Handbook

Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

PhD in Statistics

The Doctor of Philosophy (PhD) program in Statistics is designed to prepare you to work on the frontiers ofthe discipline of Statistics, whether your career choice leads you into research and teaching or into leadership roles in business, industry and government.

The program is very flexible particularly in the choice of electives and of research topic. You may even choose to do research on the interface of Statistics and some other discipline, such as Computer Science, Genetics, Forestry, Bioinformatics, Economics, etc. The course requirements are designed to ensure that you have sufficient training in Probability, Statistical Inference, Computing, and Applications to prepare you for research on the cutting edge of Statistics.

Many items in this section, with some modifications for the Department’s purposes, are taken from the Graduate Bulletin .

Prerequisite and Application Information

Guidelines for PhD Program 

Detailed Program Information

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Jaxk Reeves Graduate Coordinator I [email protected]

Liang Liu Graduate Coordinator II [email protected]

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Dept. of Department of Statistics

Doctorate in statistics, advanced exploration and investigation of the theory and application of statistics and data analysis..

Ph.D. candidates in the Department of Statistics can tailor the program to suit their career goals. Depending on the choice of courses, the program can emphasize theoretical, applied, or computational aspects of statistics and data analysis. Those that choose to emphasize statistical theory would be well prepared to pursue an academic career in research and teaching. An emphasis on applied or computational statistics would be appropriate for a career in government such as at the census bureau, developing methodologies for government surveys, or in industry, assisting with high-level data analytics, research, and in the design and analysis of surveys and experiments.

Ph.D./M.S. Graduate Handbook

The Ph.D./M.S. Graduate Handbook contains policies, suggestions, and links to forms needed for all M.S. and Ph.D. students in Statistics. Topics include requirements for the degree, description of milestones like the comprehensive exam and thesis defense, and required forms for all milestones to a Ph.D. or M.S. in Statistics.

Financial Assistance

All Statistics Ph.D. students in good standing receive year-round financial support in the form of assistantships and fellowships. This support includes a stipend to cover living costs as well as full tuition and academic fees. On admission, Ph.D. students are guaranteed five years of support. Outstanding applicants to the Statistics Ph.D. Program may be nominated by the department for particular fellowships or awards offered by the University or by the Eberly College of Science (e.g., University Graduate Fellowships or Science Achievement Graduate Fellowships). Strong candidates are encouraged to submit a formal application by January 7th to be considered in the first rounds of competition for these awards. 

Applicants and current students also may wish to seek alternative sources of funding, such as a National Science Foundation Graduate Research Fellowship. The Department of Statistics is happy to consider applications from students who are receiving outside funding.

What are the requirements?

For detailed and up-to-date course requirements, please see the degree requirements under the Graduate Bulletin .

Candidacy Examination

At the end of the first year, students will take a qualifying examination to be admitted to Ph.D. candidacy, which is offered at both the beginning and end of the summer. The exam consists of three parts based on theoretical statistics, applied statistics, and probability and Monte Carlo methods. Incoming students with exceptionally strong backgrounds may petition to take any section prior to starting the program, which will excuse them from the respective course work. Students are given two chances at passing each section  

Graduate School Oral Comprehensive Examination

During the second and third year, students are expected to form a Ph.D. committee and schedule the Graduate School oral comprehensive examination. This exam consists of a written component, whose content will be determined and administered by the student's Ph.D. graduate committee, and an oral component, which includes the presentation of a thesis research proposal.

Dissertation

The student must submit and defend a doctoral thesis during the semester they intend to graduate. The oral defense will include a public presentation of the thesis, followed by a private questioning by the committee. Information on the timeline for the graduation semester can be found under the  Grad School's thesis calendar .

Foreign Language

There is no foreign language requirement for a Ph.D. in Statistics.

Dual Degrees

Three dual degrees are currently offered, one with the  Department of Meteorology and Atmospheric Science , one with  Operations Research (OR) , and one with  Social Data Analytics (SoDA) . Interested students may apply to these dual degrees after being accepted to the Ph.D. program in Statistics, though they may make their interest in these programs known while applying to the Statistics program.  For up-to-date degree requirements and details, please see the Graduate Bulletin .

How do I Apply?

Apply to a Graduate Program at Penn State. Additional information can be found here on the Statistics Graduate Program admission requirements, application deadlines, and the Penn State Graduate school requirements and application.

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Career Placement

Wharton prepares you to become an academic leader. our phd graduates have gone on to excel at leading academic institutions, research centers, and enterprises around the globe..

Working with Wharton’s faculty, you are trained in the practices of rigorous research. You learn how to frame questions from a multi-disciplinary perspective through exposure to the broadest range of business knowledge. This breadth and depth of thinking yields new ideas, and scholars who break new ground in their selected area of research.

You leave Wharton ready to contribute meaningfully to your field, typically having published with your faculty mentors prior to graduation. Most Wharton doctoral graduates take positions at leading academic institutions, and many alumni continue working with their Wharton mentors and colleagues, returning to Wharton for conferences and colloquia throughout their careers.

Student Placements

Here’s a list of placements, by program and year of graduation, over the past 10 years:

Visit here for Accounting PhD placements.

Applied Economics

World bank Post doc at NYU Furman center. Treasury’s Office of Financial Research. UT Dallas National University of Singapore

Brattle Group University of Wisconsin Business,  Post Doc at Treasury Brattle Group Cornerstone Research Rutgers Business School Cornerstone Research

OECD Federal Reserve Board NYU, Postdoc and Hong Kong University Princeton, Postdoc

University of North Carolina, Chapel Hill University of Texas, Austin Bank of Canada NBER, Postdoc

Stanford University Cornerstone The Vanguard Group

Michigan State Federal Reserve Bank of New York (Post-doc at Chicago Booth) London School of Economics

Cornell University Microsoft Research NERA Economic Consulting University of Colorado, Boulder

Econ One Harvard Business School National Taiwan University Nuna Health Toulouse School of Economics University of California, Merced

Baruch College Clemson University Northwestern University Putnam Investments Research Affiliates

Consumer Financial Protection Bureau Duke University Indiana University University of Delaware University of Wisconsin, Madison

Berkeley Research Group University of Michigan U.S. Treasury Department Sungkyunkwan University, SKK GSB

Note: The Applied Economics program was created in Fall 2008 and, therefore, has placed students beginning 2013.

Ethics & Legal Studies

Visit here for Ethics & Legal Studies PhD placements. 

BlackRock Cornerstone Research Drexel University, LeBow College of Business Harvard Business School Rothschild & Co

BlackRock Boston Consulting Group Columbia Business School Kenan-Flagler Business School of the University of North Carolina McGill University MIT Sloan Universidad Carlos III de Madrid University of Hong Kong

Capital One Stockholm School of Economics

Arizona State University Hong Kong University of Science and Technology University of Florida University of Warwick, Warwick Business School

BI Norwegian Business School INSEAD London Business School (2 placements) Peking University University of Hong Kong (2 placements)

Carnegie Mellon University Cornell University Cornerstone Research Peking University Southern Methodist University University of Wisconsin, Madison

AQR Capital Management Carnegie Mellon University, Tepper School of Business Citadel Tulane University, Freeman School of Business University of Chicago, Booth School of Business University of Southern California, Marshall School of Business

Federal Reserve Bank of New York University of Michigan University of North Carolina, Chapel Hill

Boston College Cornell University Federal Reserve Board Michigan State University Ohio State University University of Houston

Google University of California, Los Angeles University of Delaware University of Minnesota University of Oxford

Google University of Delaware

Mingshi Investment Management Peking University Southern Methodist University

Carnegie Mellon University University of British Columbia University of North Carolina, Chapel Hill University of Rochester University of Wisconsin, Madison

Cornell University Getulio Vargas Foundation Southern Methodist University U.S. Securities and Exchange Commission University of Southern California

New York University University of Florida

Cornerstone Research KAIST (Korea Advanced Institute of Science and Technology) University of Iowa University of Minnesota, Twin Cities

Aalto University School of Economics University of California, San Diego

Federal Reserve Board

Health Care Management & Economics

Charles River Associates Baylor University Cornell University Medical Campus (Weill) University of Miami USAID Emory University Securities and Exchange Commission (SEC)

Harvard Business School New York University University of Pennsylvania, Perelman School of Medicine NYU Langone Medical Center Analysis Group

University of Virginia Northwestern University, Kellogg School of Management RAND Corporation University of Pittsburgh Analysis Group

Indiana University Bloomington John Hopkins Bloomberg School of Public Health University of Pennsylvania, Perelman School of Medicine US Department of Health and Human Services

Asian Development Bank RAND Corporation

RAND Corporation

Cornell Medical School RAND Corporation Thomas Jefferson University University of Pennsylvania, Perelman School of Medicine Urban Institute

RAND-UCLA (Joint Post-Doc) San Diego State University Temple University, Fox Business School

Columbia University Peking University Urban Institute US Army Medical Department Telehealth Office

Bill and Melinda Gates Foundation Brigham and Women’s Hospital University of Pennsylvania, Perelman School of Medicine

Massachusetts General Hospital

University of Missouri University of Porto, Faculty of Economics

Bocconi University Ohio State University University of Southern California, Marshall School of Business Rice University New York University, Stern School of Business Massachusetts Institute of Technology

George Washington University University of Texas, Austin

University of Illinois at Urbana-Champaign University of North Carolina, Chapel Hill Sungkyunkwan University

Cornell University Stanford University

University of Texas, Austin University of Wisconsin, Madison

IESE Business School, Barcelona Campus University of LUISS (Rome)

INSEAD Purdue University University of North Carolina, Chapel Hill University of Pittsburgh University of South Carolina University of Texas, Austin

George Washington University University of North Carolina, Chapel Hill University of Washington, Bothell

Georgia State University Moscow School of Management, SKLOKOVO Institute for Emerging Market Studies Nanyang Business School Rutgers University

George Washington University Harvard University INSEAD University of Maryland, Smith School of Business University of Minnesota, Carlson School Washington University, St. Louis

New York University University of Hartford

Instituto De Empresa University of Michigan

Brigham Young University Florida International University National University of Singapore New York Department of Economics University of Illinois-Urbana Champaign

Visit here for Marketing PhD placements. 

Operations, Information and Decisions

Visit here for Operations, Information and Decisions PhD placements. 

University of Florida Carnegie Mellon University Columbia University Rutgers University

Massachusetts Institute of Technology The Voleon Group

University of Chicago, Booth School of Business University of California, Berkeley Emory University Rutgers University Citadel Air Liquide

INSEAD Massachusetts Institute of Technology, Sloan School of Management University of Vienna DV01

CLVmetrics IBM Stanford University

Carnegie Mellon University Columbia University Queens College Rice University Walmart Labs

Columbia University New Jersey Institute of Technology Yahoo Labs

The Climate Corporation Credit Suisse Rutgers University University of North Carolina, Chapel Hill

Krossover Intelligence Stanford University

Northwestern University US Census Bureau

Harvard University Princeton University University of Pennsylvania

London School of Economics Massachusetts Institute of Technology

Colombia University, Earth Institute (Post-Doc)

Yale University

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• Graduate Placements

Current Career Positions of Duke Statistical Science Ph.D. Graduates

Graph shows positions of those awarded a Ph.D. between Fall 2005 and Summer 2022, and their current status was reported or made known to the department or the Graduate School.

*Other includes Public and K-12 Education.

Updated December 2022

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PhD in Statistics

Study with leading statisticians at a world-class university

  Applications for entry 2024/25 are open  

Funding deadlines: 15 January 2024 (Applications received by this date will be considered for available studentships; we may also be able to consider applications received by the end of March for funding, but this is not guaranteed.) Final application deadline: 23 May 2024  

How to Apply

A PhD offers the chance to undertake a substantial piece of supervised work that is worthy of publication and which makes an original contribution to knowledge in a particular field. Our PhD programme is designed to produce professional social scientists, well versed in a range of advanced statistical techniques and methods, in addition to having an in-depth knowledge of your topic of interest. 

The Department of Statistics is one of the world's leading centres of quantitative methods in the social sciences and has long been home to some of the world's most famous and innovative statisticians. Today, the department has an international reputation for the development of statistical methodology that has grown from our long history of active contributions to research and teaching in statistics. 

Our core research areas are:

• Data science
• Probability in finance and insurance
• Social statistics
• Time series and statistical learning

If you have any questions about our MPhil/PhD Statistics programme, please  email the Research Manager .  

Research environment

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK. It has a rich research portfolio covering core areas of statistical inference and real applications, particularly in the economic, financial and actuarial, social and industrial arenas. The close collaboration with other LSE departments, our London location and strong international partnerships are reflected in the research life of the Department of Statistics through the members of staff, PhD students, postdoctoral research fellows and the thriving visitor and seminar programmes.

Research in the department is concentrated in the following areas and PhD proposals should normally be linked to one of these areas:

Data Science

Research in the data science area is concerned with the development of new machine learning and statistical methods, and their applications. The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.

Possible areas of research include:

• Bayesian inference and predictions
• Functional data analysis
• High-dimensional statistics
• Machine and statistical learning for relational data
• Network data models, inference and predictions
• Optimisation and machine learning
• Reinforcement learning
• Statistical learning methods in precision medicine
• Statistical models and inference for ranking data
• Stochastic models of epidemic processes
• Stochastic optimisation methods
• Stochastic processes in econometrics and finance

For more information about potential supervisors and their areas of interest, visit the Data Science research group .

Probability in Finance and Insurance

PhD research in probability in finance and insurance encompasses many aspects of the discipline. Methodological and theoretical research is mainly guided by applications with the aid of both academic and industrial experts, covering topics of modern stochastic finance with an emphasis on insurance and financial mathematics.  Applications include pricing and hedging exotic products, counterparty risk, portfolio optimisation, risk management and insurance, risk transfer and securitisation, etc. 

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

Suggested research areas of PhD research projects include:

• Energy markets
• Excursions of Lévy processes and applications in finance and insurance
• Financial market with frictions
• Information asymmetry
• Interface between insurance and finance
• Lévy processes
• Optimal stopping
• Point processes in insurance and credit risk
• Quantile options and options based on occupation times
• Stochastic analysis and its applications in financial mathematics
• Stochastic control and analysis of partial differential equations in mathematical finance

This list is indicative only and by no means exhaustive. For more details about supervisors and their areas of research interests, please see the  Probability in Finance and Insurance research group . You will find links to the web pages of individual members of staff here. If you are interested in applying to undertake PhD research in probability in finance and insurance, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal. 

Social Statistics

PhD programmes of study in social statistics typically include both methodological development and the application of statistical methods to a social science field or to address new developments in social data, such as in sample surveys or social networks. Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

• Analysis of complex survey data
• Disclosure risk assessment and statistical disclosure control
• Estimation from survey data (and related data), taking account of nonresponse and using auxiliary information
• Latent transition and latent class models for modelling diagnostic tests
• Latent variable models and structural equation models for categorical data
• Longitudinal data analysis, especially event history (survival) analysis and dynamic panel models
• Modelling response strategies and detection of outliers in educational and behavioural sciences
• Multilevel simultaneous equations modelling of correlated social processes

For more details about potential supervisors and their areas of interest, visit the  Social Statistics research group . If you are interested in applying to undertake PhD research in social statistics, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.

Time Series and Statistical Learning

PhD research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.

Suggested PhD research areas include:

• Automating statistical advice
• Change detection for complex data
• Dimension reduction and factor modelling
• Estimation of stochastic volatility models
• Financial econometrics
• Functional data analysis including functional time series
• High-dimensional time series analysis
• High-dimensional variable selection
• Infectious disease modelling
• Inference for sequential data including change detection in multiple data streams
• Network time series analysis
• Nonparametric and semiparametric regression
• Non-stationary time series analysis
• Reinforcement learning for time-dependent data
• Robust statistical analysis for high-dimensional data
• Shape-constrained methods
• Spatial econometrics modelling
• Spatio-temporal modelling
• Statistical analysis of high-dimensional multi-type recurrent events

For more information, please see the  Time Series and Statistical Learning research group . 

PhD Programme Structure

PhD Your application

PhD Funding Opportunities

FAQs Frequently Asked Questions

PhD Handbook Our guide for current students

Past PhD students A listing of our PhD graduates

Visiting Research Students How to apply

PhD Academy A dedicated space for PhD students

Graduate Career Destinations What our PhD graduates have done next