Machine Learning for Modelling and Decision Making in Complex Physical Domains

Presentation Date: 

Wednesday, September 25, 2019


University College Dublin, Dublin, Ireland
My lab at the University of Waterloo carries out work on a variety of topics within Artificial Intelligence and Machine Learning with a focus on using real world problems to discover computationally hard challenges for modelling of uncertainty, dealing with large or streaming data, learning predictive models and enabling decision making.  The methods we focus on include Deep Reinforcement Learning, Convolutional and Recurrent Neural Networks, Ensemble Tree methods and manifold based data/dimensionality reduction analysis. Motivation for our work comes from domains such as automotive, medical imaging, physical chemistry, forest fires management and safety-critical embedded systems.  In this talk I will provide a brief overview of all the topics and highlight results particularly recent one in combustion modelling using Deep Learning and use of supervised and semi-supervised methods for modelling of the dynamics of Forest Fire Spread.