Doctor of Philosophy in Statistics (PhD)
Established in 1983, the Department of Statistics at UBC is internationally renowned for its excellence in research and the high calibre of its faculty members. Our programs offers students different options for pursuing their interests and professional goals. Students completing our PhD program will be well-prepared for a job in industry, government or academia. During their program our students develop important professional skills that include: effective communication skills for both technical and non-technical audiences, creativity and originality, and grant writing skills, among others. They also acquire a broad knowledge of modern statistical methods, including computing and data management.
What makes the program unique?
The Department is renowned in Canada for its research excellence and its leadership in the research community. Students are engaged through both courses and research, and develop a strong set of skills, both applied and theoretical. The Department has always valued data driven research, consulting and collaboration, and has long held communication and computing skills as crucial for success. Graduate students participate actively in our research, teaching and consulting activities, and enjoy a wide variety of opportunities for interaction with other researchers and students on- and off-campus. In addition, our graduate students run their own statistical consulting service, which provides them with professional (paid) experience even before they finish their program.
We have recently introduced a highly innovative qualifying process – instead of writing an exam, first year PhD students register in a reading and research course where they work on research papers proposed by individual faculty members.
Faculty are conducting research in a variety of applied an theoretical areas, such as Bayesian Statistics, Bioinformatics, Biostatistics, Environmental and Spatial Statistics, Forest Products Stochastic Modeling, Modern multivariate and time series analysis, robust statistics, and Statistical learning. Further details can be found on our website:
We require a 2 page (maximum) statement of interest/research proposal, as well as a CV.
TOEFL (ibT) Overall Score Requirement
IELTS Overall Score Requirement
Supervisor commitment required prior to application?
Prerequisites / Course Requirements
Successful PhD applicants typically have an MSc in Statistics or an MSc or PhD in Mathematics with strong evidence of interest in statistics. A student with only a Bachelors degree cannot usually be admitted to our PhD program, but rather must first enter the MSc program, either first completing the MSc or applying for transfer to the PhD after one year. If you have only had a few courses in statistics, your application to the PhD program will not be successful.
For admission to the PhD program, the Admissions committee requires the following, in addition to the requirements for admission to the MSc program.
a course in advanced statistical inference
courses in rigorous mathematics
at least 3 of the following courses at the graduate level: stochastic processes, advanced probability, mathematical statistics, linear models
The above requirements are in addition to the minimum admission requirements of the Faculty of Graduate and Postdoctoral Studies. Please note that meeting our admission requirements does not guarantee admission.
The following background will strengthen the application.
courses in real analysis, and possibly measure theory, advanced probability (limit theorems, sigma fields);
a broad range of courses in statistical methods (e.g., some topics among statistical computing, Bayesian statistics, generalized linear models, time series, multivariate statistics);
undergraduate or graduate computer science courses;
research or work experience relevant to statistics;
solid programming experience (e.g., C, C++, Fortran, Python, R, SAS, Matlab).
Deadline to submit online application. No changes can be made to the application after submission.Transcript Deadline
Deadline to upload scans of official transcripts through the applicant portal in support of a submitted application. Information for accessing the applicant portal will be provided after submitting an online application for admission.Referee Deadline
Deadline for the referees identified in the application for admission to submit references. See Letters of Reference for more information.
September 2020 Intake
Application Open Date01 October 2019
PhD students in the Department of Statistics receive a minimum funding package of $18,000 for the first four years of the program. This funding comes in the form of teaching and/or research assistantships. Motivated students can often find additional sources of funding. Domestic students are expected to apply for NSERC PGSD scholarships.
31 students graduated between 2005 and 2013. Of these, career information was obtained for 29 alumni (based on research conducted between Feb-May 2016):
RI (Research-Intensive) Faculty: typically tenure-track faculty positions (equivalent of the North American Assistant Professor, Associate Professor, and Professor positions) in PhD-granting institutions
TI (Teaching-Intensive) Faculty: typically full-time faculty positions in colleges or in institutions not granting PhDs, and teaching faculty at PhD-granting institutions
Term Faculty: faculty in term appointments (e.g. sessional lecturers, visiting assistant professors, etc.)
Sample Employers in Higher EducationUniversity of British Columbia (3)
Simon Fraser University (3)
Northern Illinois University (2)
University of Dhaka
Grant MacEwan University
University of Toronto
University of Saskatchewan
Ecole des Hautes Etudes Commerciales de Montreal
West Virginia University
Sample Employers Outside Higher EducationGoogle (3)
Children's Hospital of Philadelphia
Eli Lilly and Company
Ghement Statistical Consulting Company Ltd.
Sample Job Titles Outside Higher EducationSenior Statistician (2)
Senior Research Scientist
Senior Statistical Scientist
Staff Data Scientist
PhD Career Outcome SurveyYou may view the full report on career outcomes of UBC PhD graduates on outcomes.grad.ubc.ca.
DisclaimerThese data represent historical employment information and do not guarantee future employment prospects for graduates of this program. They are for informational purposes only. Data were collected through either alumni surveys or internet research.
Tuition / Program Costs
|Fees||Canadian Citizen / Permanent Resident / Refugee / Diplomat||International|
|Installments per year||3||3|
|Tuition per installment||$1,665.26||$2,925.58|
|Tuition per year|
(plus annual increase, usually 2%-5%)
|Int. Tuition Award (ITA) per year (if eligible)||$3,200.00 (-)|
|Other Fees and Costs|
|Student Fees (yearly)||$930.14 (approx.)|
|Costs of living (yearly)||starting at $16,884.10 (check cost calculator)|
All fees for the year are subject to adjustment and UBC reserves the right to change any fees without notice at any time, including tuition and student fees. Tuition fees are reviewed annually by the UBC Board of Governors. In recent years, tuition increases have been 2% for continuing domestic students and between 2% and 5% for continuing international students. New students may see higher increases in tuition. Admitted students who defer their admission are subject to the potentially higher tuition fees for incoming students effective at the later program start date. In case of a discrepancy between this webpage and the UBC Calendar, the UBC Calendar entry will be held to be correct.
Completion Rates & Times
This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.
Bouchard-Cote, Alexandre (machine/statistical learning; mathematical side of the subject as well as in applications in linguistics and biology)
Chen, Jiahua (finite mixture model, empirical likelihood, asymptotic theory, sample survey)
Cohen Freue, Gabriela (statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity )
Gustafson, Paul (Meta-Analysis, Parametric and Non-Parametric Inference, Theoretical Statistics, Pharmacoepidemiology, Bayesian statistical methods, Causal inference, Evidence synthesis, Biostatistics and Epidemiology, Partial Identification)
Heckman, Nancy (Statistics and Probabilities, functional data analysis, smoothing, splines)
Joe, Harry Sue Wah (dependence modelling, copula construction, non-normal time series, extreme value inference, parsimonous high-dimensional dependence )
Mostafavi, Sara (machine/statistical learning applied to disease genetics )
Nolde, Natalia (Statistics and Probabilities, Multivariate extreme value theory, Risk assessment, Applications in finance, insurance, geosciences)
Salibian-Barrera, Matias (S-regression estimationg, robust statistics, functional principal component analysis, bootstrap estimators, rgam, clustering algorithm)
Welch, William (, Design of experiments, experiments with computer models, data mining, drug discovery, quality improvement)
Wu, Lang (Biostatistics)
Zamar, Ruben (Data mining and text mining, Modeling data quality, Development of new robust procedures, Statistical computing, Bioinformatics)
Recent Doctoral Citations
- Dr. Shing Fu
"Researchers today are able to study the behaviour of deep diving animals via sensors that generate high volumes of data. Dr. Fu developed automatic data analytic methods to group dive depth trajectories of southern elephant seals by dive shape. His methods help researchers understand seals' foraging and resting behaviour." (May 2019)
- Dr. Tingting Zhao
"Dr. Zhao worked on improving probabilistic models for Continuous Time Markov Chains and developing Bayesian models and associated Monte Carlo methods for inference. Her modelling framework has been applied to build novel protein evolution models, where the model complexity can be controlled and good estimation is achieved." (May 2019)
- Dr. Tingting Yu
"Dr. Yu developed statistical models and methods that can assess associations between longitudinal data and survival data, and handle the complications in the longitudinal data simultaneously. She applied her methods to an HIV vaccine study and discovered significant relationships between the risk of HIV infection and some immune response biomarkers." (May 2019)
- Dr. Daniel Richard Dinsdale
"Dr. Dinsdale developed new statistical methods to improve the prediction of oceanographic measurements, for example water temperature, using data collected by tags attached to marine mammals such as seals. This research helps to improve our understanding of changing ocean dynamics in sparsely sampled areas such as near Antarctica." (November 2018)
- Dr. Xiaoli Yu
"Developing a new drug can be a complicated, time consuming and expensive process. Dr. Yu developed a new optimal design method, which will accurately estimate the safe and effective dose level of the new drug for patients. Her study greatly improves the accuracy and safety of clinical trials, and speeds up the drug development process." (May 2018)