Machine Learning with Uncertainty for Monitoring Moving Objects and People

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Sequence data is very common in the natural world, emerging in myriad datasets such as stock markets, weather, biomedical, target tracking and so on. Often the sequences are composed of multiple channels of correlated information that change together in time. State of the art deep learning models of sequence data represent data using scalars, rather than random variables. These scalar-based deep learning models require large amounts of data to train, perform poorly when trained on data from one domain and then applied to a different domain, and model rare events poorly. Traditional statistical models for sequence data are not learning-based and very often rely on fixed parameters that should be adjusted for different environments or different situations. Bayesian probabilistic models allow for designing interpretable systems, dealing with small data and quantifying uncertainties in estimations and predictions. New probabilistic deep learning frameworks have primed this field for advancement. Research on taking advantage of both Bayesian probabilistic and deep learning models has just started recently. In this program, we plan to significantly contribute towards developing probabilistic deep learning models for time series and sequence data. Initial work in this field resulted in powerful, versatile but very complex models. We plan to develop simpler models and quality metrics, and to make them easier to train as well as to implement the model in real-time in practical applications. We will test the developed model building approaches on at least two practical applications: monitoring the activities and intents of people with the focus on monitoring elderly people, as well as classifying and tracking multiple UAVs and inferring their intents. For data collection, we plan to use the same sensors for both applications, and a very similar algorithmic framework. Recognizing intents is extremely important because this brings machine learning closer to the way that humans observe the behaviour of other humans and moving objects. This research will move the field of machine learning by integrating multiple concepts including Bayesian probabilistic learning, time series, deep learning, and uncertainty quantification. The benefits to Canada and the world would be tremendous if the developed model building framework outperforms the state of the art, and additional benefits to Canada are the training of ten students in a highly sought after field, and new applications advancing the care of elderly people and improving public safety by more accurately monitoring the risk posed by objects in motion.