Author [ Title(Desc)] Type Year
Crowley, M. et al., 2007. Adding Local Constraints to Bayesian Networks. In Advances in Artificial Intelligence. Canadian AI Conference, Montreal, Quebec, Canada, 2007.: Springer Berlin Heidelberg, pp. 344–355. Available at:
Ghojogh, B., Crowley, M. & Karray, F., 2019. Addressing the Mystery of Population Decline of the Rose-Crested Blue Pipit in a Nature Preserve using Data Visualization. ArXiv Preprint. ArXiv: 1903.06671.
Houtman, R.M. et al., 2013. Allowing a wildfire to burn: Estimating the effect on future fire suppression costs. International Journal of Wildland Fire, 22(7), pp.871–882.
Salem, M., Crowley, M. & Fischmeister, S., 2016. Anomaly Detection Using Inter-Arrival Curves for Real-time Systems. In 2016 28th Euromicro Conference on Real-Time Systems. jul. Toulouse, France, pp. 97–106.
Crowley, M., 2015. Answering Simple Questions About Spatially Spreading Systems. In 2015 Summer Solstice: 7th International Conference on Discrete Models of Complex Systems.
Maryam, S. et al., 2017. Application of Probabilistically-Weighted Graphs to Image-Based Diagnosis of Alzheimer’s Disease using Diffusion MRI. In SPIE Medical Imaging Conference on Computer-Aided Diagnosis. March 3. Orlando, FL, United States: International Society for Optics and Photonics. Available at:
NekoeiQachkanloo, H. et al., 2019. Artificial Counselor System For Stock Investment. In Innovative Applications of Artificial Intelligence (IAAI-19). 27 January . IAAI-19 Conference, Honolulu, Hawaii, USA, 2019.: AAAI Press., p. 8. Available at:
Patitsas, E. et al., 2010. Circuits and logic in the lab : Toward a coherent picture of computation. In 15th Western Canadian Conference on Computing Education. Kelowna, BC, Canada. Available at: crowley/papers/wccce2010.pdf.
Subramanian, S.G. & Crowley, M., 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:
Bhalla, S. et al., 2019. Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks. In European Conference on Machine Learning. Wurzburg, Germany, p. 8.
screen_shot_2019-07-21_at_3.26.52_pm.png ecml_combustion_ml.pdf
Carrillo, J. et al., 2019. 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.
Poole, D. & Crowley, M., 2013. Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering. In IJCAI International Joint Conference on Artificial Intelligence. Beijing, China, pp. 1060–1068. Available at:
Subramanian, S.G. et al., 2018. Decision Assist For Self-Driving Cars. In 31st Canadian Conference on Artificial Intelligence, Candian AI 2018. Toronto, Ontario, Canada: Springer, pp. 381-387. Available at:
Ghojogh, B., Karray, F. & Crowley, M., 2019. Eigenvalue and Generalized Eigenvalue Problems: Tutorial. ArXiv Preprint arXiv:1903.11240.
Crowley, M., 2011. Equilibrium Policy Gradients for Spatiotemporal Planning. University of British Columbia. Available at:
Crowley, M., 2004. Evaluating Influence Diagrams. Unpublished Working Paper.
Ghojogh, B. et al., 2019. Fitting A Mixture Distribution to Data: Tutorial. ArXiv preprint. arXiv:1901.06708.
Ghojogh, B. & Crowley, M., 2019. Instance Ranking and Numerosity Reduction Using Matrix Decompositionand Subspace Learning. In Canadian Conference on Artificial Intelligence. Kingston, ON, Canada: Springer’s Lecture Notes in Artificial Intelligence., p. 12.
Carrillo, J. & Crowley, M., 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: