Reference number : GC-2017-Q1-03405, amendment number : , agreement type : G, recipient legal name : Bouguila, Nizar (Concordia University)|Bouguila, Nizar (Université Concordia), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The modeling, recognition and prediction of events are central problems of several disciplines. These are challenging tasks that have important economic, social, technological and security advantages. Several machine learning and data mining techniques have been proposed in the past to extract useful knowledge and hidden patterns from data to recognize events and to predict future ones (e.g. opportunities, threats, etc.). The extensive use of these techniques, to tackle different tasks, has allowed practitioners to figure out that events are generally complex in nature (i.e. combine data from different sources) and that traditional events modeling, recognition and prediction approaches make generally two assumptions that are often false in real-life applications. First, that the number of classes of events is finite and known. Second, that the feature vectors representing the events are independent which is not true in the majority of the cases. Generally, events arrive sequentially and have certain dependency in time since they depend on time-varying data. 
The goal of this research program is to explore and develop large-scale statistical learning approaches to model, recognize and predict events by analyzing time-varying data. In particular, I will focus on nonparametric Bayesian models. These models are flexible, eliminate the need for an assumption of a fixed number of classes of events, but are unfortunately exchangeable (i.e. old events have equal importance to new ones) and do not capture time-varying features. I propose then to introduce the notion of time into nonparametric Bayesian models to maintain an up-to-date representation of the events at hand. While in most existing statistical frameworks specific distributional assumptions are made for the feature vectors, I propose to consider families of distributions to add more flexibility and generalization capabilities. These extensions require the development of new learning approaches to estimate the parameters of the developed models and the design of efficient feature selection techniques. 
The proposed research program is very promising, based on strong mathematical and statistical formulations. It will advance knowledge and improve the state of the art. Many applications are possible. Examples include the modeling of the multimedia content (text, image, video) of social networks, criminal threats prediction, abnormal events recognition in videos, etc. The results of the research that I propose can be applied for different other fields and disciplines (e.g. biology, social science, etc.)., program purpose french : The modeling, recognition and prediction of events are central problems of several disciplines. These are challenging tasks that have important economic, social, technological and security advantages. Several machine learning and data mining techniques have been proposed in the past to extract useful knowledge and hidden patterns from data to recognize events and to predict future ones (e.g. opportunities, threats, etc.). The extensive use of these techniques, to tackle different tasks, has allowed practitioners to figure out that events are generally complex in nature (i.e. combine data from different sources) and that traditional events modeling, recognition and prediction approaches make generally two assumptions that are often false in real-life applications. First, that the number of classes of events is finite and known. Second, that the feature vectors representing the events are independent which is not true in the majority of the cases. Generally, events arrive sequentially and have certain dependency in time since they depend on time-varying data. 
The goal of this research program is to explore and develop large-scale statistical learning approaches to model, recognize and predict events by analyzing time-varying data. In particular, I will focus on nonparametric Bayesian models. These models are flexible, eliminate the need for an assumption of a fixed number of classes of events, but are unfortunately exchangeable (i.e. old events have equal importance to new ones) and do not capture time-varying features. I propose then to introduce the notion of time into nonparametric Bayesian models to maintain an up-to-date representation of the events at hand. While in most existing statistical frameworks specific distributional assumptions are made for the feature vectors, I propose to consider families of distributions to add more flexibility and generalization capabilities. These extensions require the development of new learning approaches to estimate the parameters of the developed models and the design of efficient feature selection techniques. 
The proposed research program is very promising, based on strong mathematical and statistical formulations. It will advance knowledge and improve the state of the art. Many applications are possible. Examples include the modeling of the multimedia content (text, image, video) of social networks, criminal threats prediction, abnormal events recognition in videos, etc. The results of the research that I propose can be applied for different other fields and disciplines (e.g. biology, social science, etc.)., agreement title english : Time-sensitive non-parametric Bayesian approaches for events modeling, recognition and prediction, agreement title french : Time-sensitive non-parametric Bayesian approaches for events modeling, recognition and prediction, agreement number : RGPIN, agreement value : 115000.0, 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