In recent years, unprecedented advances in digital technology and artificial intelligence have brought the term data science to the foreground. Data are now collected faster than they can be processed, analyzed and interpreted, and the scope of projects led by companies, institutes and governments is becoming increasingly complex as the potential of these data is revealed. Businesses today are increasingly set on creating organized business analytics structures and departments, and students are flocking to research centres primarily focused on data analysis. A growing number of partnerships are being forged between academia and industry. These range from small projects to large-scale initiatives such as the Canada First Research Excellence Fund granted to IVADO and SCALE AI. These are the conditions under which the vision and research program must be pursued.

The vision of the FRQ-IVADO Chair in Data Science is to incorporate math, statistics and IT sciences, along with the knowledge and skills of specialists and the data they generate, into a single research program. The Chair’s objectives are built on this multi-disciplinary approach, which lends itself to the production of quality methodological tools that are adapted to specific issues, and allows for proper interpretation of the results. The Chair’s work will cover a range of sectors, including management, with a particular expertise in the fields of transportation and health.

The general objectives of the Chair are as follows :

  • Develop methods to analyze and process data combining machine learning and statistical inference, specifically tackling the challenges posed by large data volumes and complex correlations among data.
  • Participate and collaborate on large-scale applied research projects in various fields, such as transportation, health and management.
  • Put measures into place to ensure that the Chair’s various projects adhere to the principles of equity, diversity and inclusion of visible and ethnic minorities and persons with disabilities.


The proposed research program focuses on four themes: i) developing methodological tools that target data from intelligent transportation (theme 1) or health care (theme 2), or more general methodological challenges that can be applied to several fields (theme 3); and ii) participating in large-scale applied projects that may require using sophisticated analytical methods (theme 4).

With populations booming in our cities, urban mobility has become one of the greatest challenges facing local governments today. Data from intelligent systems can help us understand temporal and spatial models of urban mobility, and provide solutions to congestion, overcrowding, safety and environmental issues. This theme targets the analytical challenges created through these new technologies, which can record vehicle data (e.g., volume, speed and vehicle class) in addition to user information via sensors installed on the network (e.g., radar, infrared, Wi-Fi, Bluetooth and video cameras) and GPS data collected from smart phones

Big data has affected every field, and healthcare is no exception. Population data are already making it possible to set up systems to monitor infectious diseases through public health institutes. However, biomedical data management has a long way to go before it reaches its full potential. Public health institutes will soon be incorporating biomedical data into their monitoring policies. In Canada, certain gene sequencing data are now collected on a large scale, for example, the systematic sequencing of HIV for every person diagnosed with the disease. This enables public health institutes to detect and locate transmission chains, then implement highly targeted intervention plans (see our work on Villandré et al., 2018). This is the context in which this research theme was developed, and its relevance for health authorities and the genomics industry will only grow in the coming years.

This research theme is aimed at developing analytical tools that are not specific to a particular field, but that address general problems in analytical techniques. Specifically, we are working to reduce data volume (number of variables) using tools that combine inference and machine learning.

Here is the list of projects involving the work of the Chair:

  • Genome Canada Grant, « Precision Medicine in Cellular Epigenomics », PI : C. Greenwood, $660,512 (2019-2021)
  • CIHR, Operating Grant « A prospective study of DNA methylation in women and girls with Anorexia Nervosa: relationship to stage of illness and nutritional status », PI: H. Steiger, $535,500 (2018-2020).
  • IVADO grant « Modeling and predicting the effect of genetic variants on brain structure and Function », PI: S. Jacquemont, $150,000 (2018-2020)
  • CIHR, Operating Grant « A longitudinal study of DNA methylation in women with Anorexia Nervosa: Roles of disorder chronicity, current nutritional status and nutritional rehabilitation » , PI: H. Steiger, $100,000 (2015-2016)
  • CIHR, Operating Grant « Electroretinography (ERG) as an aid to predictive diagnosis in youths and young adults with a clinical high risk pro_le for psychosis », PI: C. Merette, $827,745 (2015- 2020).
  • CIHR, Operating Grant « Assessing the development of elementary and social perception in autism using behavioral and electrophysiological approaches », PI: A. Bertone, $721,340 (2015-2020).