Machine Learning Lab
Comparison of Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data. In The Transportation Association of Canada and Intelligent Transportation Systems Canada Joint Conference (TAC-ITS). Halifax, Canada, p. 16.
, 2019. Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks. In European Conference on Machine Learning. Wurzburg, Germany, p. 8.
, 2019. Paper accepted to ECML 2019
Good News in the UWECEML Lab
Training Cooperative Agents for Multi-Agent Reinforcement Learning. In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). Montreal, Canada.
, 2019. Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions. In Canadian Association of Road Safety Professionals CARSP Conference. CARSP Conference, Calgary, Alberta. , p. 4 (Won best paper award!). Available at: http://www.carsp.ca/research/research-papers/research-papers-search/download-info/integration-of-roadside-camera-images-and-weather-data-for-monitoring-winter-road-surface-conditions/. Publisher's Version
, 2019.
Adaptation Through Learning : Using Machine Learning to Improve Forest Wildfire Management,
at
Department of Electrical Engineering & Computer Science and Engineering, York University,
Wednesday, February 13, 2019
Adaptation Through Learning : Using Machine Learning to Improve Forest Wildfire Management,
at
San Francisco, California,
Thursday, January 24, 2019
Accepted talk at the IAAI-19 Conference : Artificial Counselor System for Stock Investment,
at
Honolulu, Hawaii,
Thursday, January 31, 2019
Waterloo Innovation Summit Speaker Series : Technology and Climate Change
UWaterloo Innovation Summit :Beyond Climate Impact
Fighting Fire with AI: Using Artificial Intelligence to Improve Modelling and Decision Making in Wildfire Management,
at
Banff International Research Station, Banff, Alberta, Canada,
Friday, November 17, 2017:
Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models from Satellite Images. Frontiers in ICT: Environmental Informatics. Available at: https://www.frontiersin.org/articles/10.3389/fict.2018.00006/abstract. Publisher's Version
, 2018. Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings. In Canadian Conference on Artificial Intelligence. Toronto, Ontario, Canada: Springer, pp. 285-291. Available at: https://link.springer.com/chapter/10.1007/978-3-319-89656-4_28. Publisher's Version
, 2018.