Using Deep Learning and Reinforcement Learning to Tame Spatially Spreading Processes Wednesday, October 25, 2017

Recent advances in Artificial Intelligence and Machine Learning (AI/ML) allow us to learn predictive models and control policies for larger, more complex systems than ever before. However, some important real world domains such as forest wildfire spread, flooding​ and medical imaging present a particular challenge. They contain spatially spreading processes(SSP) where some local features change over time based on proximity in space.

BIRC Workshop On Deep Learning In Medicine, at University Hospital, London, Ontario, Canada, Monday, August 28, 2017:

This all-day workshop brough together researchers, students and medical professionals from medical imaging, image processing and machine learning to discuss what the new class of machine learning algorithms known collectively as Deep Learning are, how they are and could be used for medicine and what the impacts for medicine as a whole are of this technology. The workshop was hosted by the Biomedical Imaging Research Centre (BIRC) at the University of Western Ontario.

AI Education Through Real World Problems, at San Francisco, USA, Sunday, February 5, 2017


Automation of safety-critical systems is one of the most important and challenging areas of AI/ML research, I presented my vision for integrating engineering principles into AI education in a unified way.

Presented at the Seventh Symposium on Educational Advances in Artificial Intelligence.