Computational Sustainability

Connecting Machine Learning with Global Sustainability challenges.

In the field of Computational Sustainability, I have worked on learning predictive models of and optimizing policies for domains in invasive species control, forest harvest management and forest fire management. These types of domains offer unique challenges for traditional artificial intelligence and machine learning algorithms for decision making, prediction and anomaly detection.

See my blog for more writing on this topic.

Our Papers on Computational Sustainability

  1. A Complementary Approach to Improve WildFire Prediction Systems.
    Sriram Ganapathi Subramanian, and Mark Crowley
    In Neural Information Processing Systems (AI for social good workshop). NeurIPS. 2018.
  2. Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
    Juan Carrillo, Daniel Garijo, Mark Crowley, Yolanda Gil, and Katherine Borda.
    In Third International Workshop on Capturing Scientific Knowledge (Sciknow 2019), Collocated with the tenth International Conference on Knowledge Capture (K-CAP). Los Angeles, California, USA. 2019.
  3. MCTS+A3C
    Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings
    Sriram Ganapathi Subramanian, and Mark Crowley
    In Canadian Conference on Artificial Intelligence. Toronto, Ontario, Canada. 2018.
  4. Using Spatial Reinforcement Learning to Build Forest Wildfire Dynamics Models From Satellite Images
    Sriram Ganapathi Subramanian, and Mark Crowley
    Frontiers in ICT. 5, (6). Frontiers, Apr, 2018.
  5. PAC Optimal MDP Planning with Application to Invasive Species Management
    Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, and H. Jo Albers.
    Journal of Machine Learning Research. 16, 2015.
  6. Using equilibrium policy gradients for spatiotemporal planning in forest ecosystem management
    IEEE Transactions on Computers. 63, (1). IEEE computer Society Digital Library. IEEE Computer Society., 2014.
  7. PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs
    Thomas G Dietterich, Majid Alkaee Taleghan, and Mark Crowley
    In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2013). Bellevue, WA, USA. 2013.
  8. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains
    In 13th INFORMS Computing Society Conference. Santa Fe, NM, United States. 2013.
  9. Managing Invasive Species in a River Network
    Kim Hall, Majid Alkaee Taleghan, Heidi J. Albers, Thomas Dietterich, and Mark Crowley
    In Third International Conference on Computational Sustainability. Copenhagen, Denmark. 2012.
  10. phd-thesis
    Equilibrium Policy Gradients for Spatiotemporal Planning
    UBC Library, Vancouver, BC, Canada.. 2011.
  11. Seeing the Forest Despite the Trees : Large Scale Spatial-Temporal Decision Making
    Mark Crowley, John Nelson, and David Poole.
    In Conference on Uncertainty in Artificial Intelligence (UAI09). Montreal, Canada. 2009.