Artificial Intelligence Group

Welcome to the Artificial Intelligence Group

The Artificial Intelligence (AI) Group at the David R. Cheriton School of Computer Science conducts research in many areas of artificial intelligence. Faculty members of the group have active interests in: models of intelligent interaction, multi-agent systems, natural language understanding, constraint programming, computational vision, robotics, machine learning, and reasoning under uncertainty.

The AI Group also has a particular investment in Societal AI.

  1. Aug. 29, 2019Moojan Ghafurian, Kerstin Dautenhahn and Jesse Hoey awarded funding to develop emotionally intelligent robots to help people with dementia

    Moojan Ghafurian, Graham Postdoctoral Fellow in the Cheriton School of Computer Science, and Kerstin Dautenhahn, Canada 150 Chair in Intelligent Robotics in the Department of Electrical and Computer Engineering, have received a catalyst grant from the University of Waterloo’s Network for Aging Research to d

  2. July 15, 2019Emotionally intuitive artificial intelligence

    People suffering from the early symptoms of Alzheimer’s disease often have difficulty remembering things that recently happened to them. As the disease takes root, a person’s reasoning and behaviour can change. Day-to-day routines — like handwashing — may become challenging for them and they begin to need more assistance from caregivers for simple tasks.

    But now there is technology that can help.

  3. May 30, 2019Haonan Duan receives Vector Scholarship in Artificial Intelligence

    Haonan Duan has received a prestigious Vector Scholarship in Artificial Intelligence from the Vector Institute.

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  1. Dec. 6, 2019PhD Defence: Likelihood-based Density Estimation using Deep Architectures

    Priyank Jaini, PhD candidate
    David R. Cheriton School of Computer Science

    Multivariate density estimation is a central problem in unsupervised machine learning that has been studied immensely in both statistics and machine learning. Several methods have thus been proposed for density estimation including classical techniques like histograms, kernel density estimation methods, mixture models, and more recently neural density estimation that leverages the recent advances in deep learning and neural networks to tractably represent a density function. In today's age when large amounts of data are being generated in almost every field it is of paramount importance to develop density estimation methods that are cheap both computationally and in memory cost. The main contribution of this thesis is in providing a principled study of parametric density estimation methods using mixture models and triangular maps for neural density estimation. 

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