Workshops will be held in person only on October 29, from 9:30am to 4:30pm.
Workshop 1
Smoothing based models using reproducible workflows in R – English session
Dr. Dave Campbell
Professor, Carleton University
https://people.math.carleton.ca/~davecampbell
Abstract:
In this workshop, we will introduce the use of Generalized Additive Models in R with emphasis on modern reproducible workflows that facilitate sharing and recycling efforts for use in new or updated datasets. Generalized Additive Models (GAMs) are a flexible regression tool that acts as an intermediary between linear regression and completely unconstrained function estimation from tools such as neural networks. GAMs are part of the inferential data science toolkit that allows a balance between ‘letting the data decide’ and exploiting expert insight into model curation.
Participants to this workshop will be introduced to reproducible workflows in R providing them with the ability to share results and automatically generate reports. In particular, a mathematical introduction to GAMs building on familiar tools from linear regression will be given. An overview of where these tools fit into the analytic toolbox and how they are combined into powerful predictive machines will also be discussed.
This workshop assumes only minimal experience with using R or a related data science coding language.
The workshop will be offered in English. The material will be available to participants in both official languages.
Biography:
Dr. Dave Campbell is a full Professor in the School of Mathematics and Statistics and the School of Computer Science at Carleton University in Ottawa. Academically, he runs a collaborative team researching inferential algorithms at the intersections of statistics with machine learning, computing, and applied mathematics to solve problems inspired by industry and government collaborations. He has co-authored discussion papers in Bayesian Analysis and the Journal of the Royal Statistical Society (series B) and been awarded over $3.5 million in research grants.
Dave’s career path maintains a theme of Industrial collaborations. He spent 2021-2023 period leading the inferential Data Science team at the Bank of Canada overseeing projects relating to cybersecurity, forecasting banknote demand, understanding drivers of inflation, and ensuring data privacy. Before moving to Ottawa in 2019, Dave was a Professor at Simon Fraser University, where he led the creation of their BSc in Data Science. He was the inaugural President of the Data Science and Analytics Section of the Statistical Society of Canada and was a co-organizer of the popular Vancouver Learn Data Science Meetup linking industry and academia.
It is an honor for us that Dr. Dave Campbell accepted our invitation to share his knowledge at a Symposium workshop! You can actually find him on LinkedIn: https://www.linkedin.com/in/drdavecampbell/
Workshop 2
Protecting the confidentiality of statistical data – French session
Dr. Anne-Sophie Charest
Professor, Laval University
https://www.fsg.ulaval.ca/corps-professoral/anne-sophie-charest
Abstract:
In this workshop, we will explore how to collect, analyze and share confidential data without disclosing personal information. We will look at the various risks associated with the use of personal data, as well as different ways of measuring these risks. In particular, we will consider differential privacy, an approach that has been the subject of much research and is now used in practice by some statistical agencies and private companies. We will explain the origin of this formal measure of confidentiality, look in detail at its mathematical definition and interpretation, and discuss the advantages and limitations of the approach. We will also discuss the use of synthetic datasets for privacy protection purposes: how to generate such datasets and assess their quality in terms of risk and utility. The content will be illustrated with R code, and part of the time will be set aside for participants to test the methods presented.
The workshop will be offered in French. The material will be available to participants in both official languages.
Biography:
Anne-Sophie Charest is an Associate Professor at Université Laval. She holds a PhD in Statistics from Carnegie Mellon University. Her research interests focus on the protection of the confidentiality of statistical data, including in the context of surveys or population census. She is particularly interested in the generation and analysis of synthetic datasets as well as the measurement of disclosure risk, particularly through the differential privacy framework.
It is an honor for us that Anne-Sophie Charest accepted our invitation to share her knowledge at a Symposium workshop! You can actually find her on LinkedIn: https://www.linkedin.com/in/anne-sophie-charest-900a585b/