Reference number : GC-2017-Q1-02121, amendment number : , agreement type : G, recipient legal name : Sedig, Kamran (The University of Western Ontario), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : According to the National Research Council of Canada, in support of Canada&rsquo;s digital economy, a high-priority area of research and development is software solutions that deal with the explosive growth of data and escalating needs for &ldquo;revolutionary ways to use computers to make decisions, synthesize information, and discover new knowledge.&rdquo; As professionals from a diverse set of fields (e.g., health, law, finance, insurance, energy, and education) increasingly use large and heterogeneous datasets to do research, innovate, solve problem, make decisions, and set policies, there is need for sophisticated software tools that support the daily complex activities in these fields. My research involves designing, implementing, and evaluating human-centred visual analytics tools (VATs) that support the execution of complex activities, such as studying causes of diseases, solving scientific problems, and analyzing health data. VATs facilitate the performance of these complex activities by combining automated machine learning models, visualizations, and user interactions. VATs take advantage of the visual, reasoning, and decision-making abilities of humans and the powerful data discovery and analysis strengths of computers. Complex activities cannot be performed entirely by VATs; human judgment and constant involvement are required to steer the activity process. This means that tasks must be carried out through a high degree of human-VAT collaboration. The human must (a) determine the input and interpret the output of all intermediate tasks, must (b) decide how to interactively steer machine learning models and visualizations, and must (c) choose which tasks to delegate to the tool and which to perform themselves. The design of VATs that couple human cognition and computation and support complex activities poses a series of challenges; such design must consider a host of interrelated issues: coordination and distribution of datasets and tasks, machine learning models, visualizations, user interactions, and human cognition. My research program aims at developing techniques, methods, principles, and frameworks for the design of human-centred visual analytics tools that support users in the execution of complex activities. I will build and study tools that enable a close &lsquo;collaboration&rsquo; between humans and their data, for analytic purposes. I will use scaffold-based techniques to support how humans work with complex data and machine learning models. As human activities become more and more complex, this type of research is indispensable. Well-designed VATs will enable all types of users to carry out knowledge work involving large data, helping to streamline their daily tasks by providing significant improvement in work efficiency. This is important for increasing Canada&rsquo;s global share of the growing information and communications technology market., program purpose french : According to the National Research Council of Canada, in support of Canada&rsquo;s digital economy, a high-priority area of research and development is software solutions that deal with the explosive growth of data and escalating needs for &ldquo;revolutionary ways to use computers to make decisions, synthesize information, and discover new knowledge.&rdquo; As professionals from a diverse set of fields (e.g., health, law, finance, insurance, energy, and education) increasingly use large and heterogeneous datasets to do research, innovate, solve problem, make decisions, and set policies, there is need for sophisticated software tools that support the daily complex activities in these fields. My research involves designing, implementing, and evaluating human-centred visual analytics tools (VATs) that support the execution of complex activities, such as studying causes of diseases, solving scientific problems, and analyzing health data. VATs facilitate the performance of these complex activities by combining automated machine learning models, visualizations, and user interactions. VATs take advantage of the visual, reasoning, and decision-making abilities of humans and the powerful data discovery and analysis strengths of computers. Complex activities cannot be performed entirely by VATs; human judgment and constant involvement are required to steer the activity process. This means that tasks must be carried out through a high degree of human-VAT collaboration. The human must (a) determine the input and interpret the output of all intermediate tasks, must (b) decide how to interactively steer machine learning models and visualizations, and must (c) choose which tasks to delegate to the tool and which to perform themselves. The design of VATs that couple human cognition and computation and support complex activities poses a series of challenges; such design must consider a host of interrelated issues: coordination and distribution of datasets and tasks, machine learning models, visualizations, user interactions, and human cognition. My research program aims at developing techniques, methods, principles, and frameworks for the design of human-centred visual analytics tools that support users in the execution of complex activities. I will build and study tools that enable a close &lsquo;collaboration&rsquo; between humans and their data, for analytic purposes. I will use scaffold-based techniques to support how humans work with complex data and machine learning models. As human activities become more and more complex, this type of research is indispensable. Well-designed VATs will enable all types of users to carry out knowledge work involving large data, helping to streamline their daily tasks by providing significant improvement in work efficiency. This is important for increasing Canada&rsquo;s global share of the growing information and communications technology market., agreement title english : Scaffold-based visual analytics tools for complex activities, agreement title french : Scaffold-based visual analytics tools for complex activities, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada