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