Summer School on Modern Methods in Survey Sampling

Summer School on Modern Methods in Survey Sampling

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University of Ottawa

75 Laurier Avenue East

Ottawa, ON K1N 6N5


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A four-day summer school for graduate students, postdoctoral fellows, statisticians working in government or industry, and young researchers

About this event

Complex surveys play an important role in providing critical information for policy makers as well as the general public. Surveys and survey data are also widely used in many scientific areas, such as public health and social science research. In recent years, national statistical offices such as Statistics Canada have been facing increasing pressure to utilize convenient but often uncontrolled ``big data sources such as web survey panels and satellite image and digital data. While such sources provide timely data for a large number of variables and population elements, they often fail to represent the target population of interest because of inherent selection biases and frame under-coverage issues. New challenges and nonstandard data sources have generated problems that traditional sampling techniques cannot easily address, which has led to substantial research interest and activity in recent years on machine learning methods and data integration techniques in finite population sampling.

The summer school is part of the scientific activities of the Collaborative Research Team (CRT) on Modern Techniques for Survey Sampling and Complex Data funded by the Canadian Statistical Sciences Institute (CANSSI). The summer school will provide training opportunities for graduate students, postdoctoral fellows, statisticians working in government or industry such as Statistics Canada, and young researchers interested in the following topics:

  • Data integration techniques
  • Machine learning methods
  • High dimensional inference problems
  • Causal inference and missing data
  • Analysis of survey data

For more information, please contact David Haziza at


This four-day event will take place at the University of Ottawa in Ottawa, Ontario, Canada. Registration for the event is C$200 and includes the cost of all lunches and coffee breaks.

A limited number of no-cost guest passes are available for graduate students with limited funding. Please contact David Haziza at for details.

Use the "Tickets" button on this page to register.


The conference will take place in the Faculty of Social Sciences, 120 University Private, Room FSS 1007, Ottawa, Ontario K1N 9A7.

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Residence rooms for graduate students: The organizers have reserved a block of two-bedroom suites with double beds at the residences of the University of Ottawa. The cost of these rooms for graduate students will be covered by CANSSI grants. If you would like to reserve a residence room, please write to David Haziza at

Hotel accommodations: The organizers have also reserved a block of rooms at the Novotel Ottawa City Centre (33 Nicholas Street, Ottawa). To reserve a room, please contact the hotel at 1-855-677-3033 and ask for the Summer School on Modern Techniques block or quote booking code 1088952. The cost of a room is $169/night.


Tuesday, July 5, 2022

Wednesday, July 6, 2022

Thursday, July 7, 2022

Friday, July 8, 2022


Summer School on Modern Methods in Survey Sampling image

Camelia Goga is Professor of Statistics at the Université de Franche-Comté. Her research interests include the theory of survey sampling theory, nonparametric estimation, and machine learning methods. She is an Associate Editor of the Journal of Nonparametric Statistics.

David Haziza is a Professor in the Department of Mathematics and Statistics at the University of Ottawa. His research interests include inference in the presence of missing data and influential units as well as machine learning methods in finite population sampling. He is a fellow of ASA and the recipient of the 2018 CRM-SSC Prize in Statistics and the 2018 Gertrude M. Cox Award.

Jae-kwang Kim is LAS Dean’s Professor in the Department of Statistics at Iowa State University. He is a fellow of ASA and IMS and the recipient of the 2015 Gertrude M. Cox Award. He is a coauthor of the book Statistical Methods for Handling Incomplete Data. He has published more than 90 papers in the area of survey sampling and missing data analysis

Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honours for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modelling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004–2012) and the Dean of the Graduate School of Arts and Sciences (2012–2017).

Jason Poulos is a Postdoctoral Fellow in Data Science in the Department of Health Care Policy at Harvard Medical School. He received his PhD in Political Science with a Designated Emphasis in Computational Science and Engineering from UC Berkeley in 2019. He subsequently held a joint postdoctoral appointment in the Department of Statistical Science at Duke University and the Statistical and Applied Mathematical Sciences Institute (SAMSI), where he participated in the Causal Inference and Deep Learning programs. His research focuses on leveraging machine (deep) learning for improving causal inference or missing data imputation in observational studies in the social sciences.

Changbao Wu is Professor of Statistics in the Department of Statistics and Actuarial Science at University of Waterloo. His main research interests include design and analysis of complex surveys, resampling techniques, missing data analysis and causal inference, and integration of data from multiple sources. He is Fellow of ASA, Fellow of IMS, Elected Member of ISI, and was the winner of the CRM-SSC Prize in Statistics in 2012. He has served on several editorial boards including Survey Methodology, The Canadian Journal of Statistics, JASA T&M and Biometrika. He is the lead author of the book Sampling Theory and Practice (with Mary Thompson), published by Springer in 2020. He has also served on Statistics Canada’s Advisory Committee on Statistical Methods since 2015.

Shu Yang is Associate Professor of Statistics and University Faculty Scholar at North Carolina State University. She received her PhD in Applied Mathematics and Statistics from Iowa State University and postdoctoral training at Harvard T.H. Chan School of Public Health. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for survey data, missing data, and spatial statistics. She has been Principal Investigator for several U.S. National Science Foundation and National Institute of Health research projects.

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