Artificial Intelligence for Computational Sustainability: A Lab Companion/List of Computational Sustainability Courses

Sustainability and Assistive Computing (Bryn Mawr, Fall 2010)

 * 1) C. Gomes (2009). “Computational Sustainability: Computational Methods for a Sustainable Environment, Economy, and Society” (Bryn Mawr, UBC, Georgia Tech, Stanford, Cornell)
 * 2) L. Buckley, et al.  (2010).  “Can mechanism inform species’ distribution models?”
 * 3) S. Phillips, R. Anderson, and R. Schapire. (2006) “Maximum entropy modeling of species geographic distributions.”
 * 4) J. Puckett, et al. (2005). “The Digital Dump”
 * 5) Green Electronics Council. (2009). “Environmental Benefits of 2008 EPEAT Purchasing”
 * 6) A. Krause and C. Guestrin (2009). “Optimizing Sensing: From Water to the Web.”
 * 7) A. Krause, et al. (2008). “Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks”
 * 8) A. Farrel, et al. (2006). “Ethanol Can Contribute to Energy and Environmental Goals”
 * 9) T. Searchinger, et al. (2008). “Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change”
 * 10) Robertson and Swinton. 2005. “Reconciling agricultural productivity and environmental integrity: a grand challenge for agriculture”

Computing and the Environment (Vanderbilt, Spring 2011)

 * 1) D. Fisher (2011). "Sustainability" In Leadership in Science and Technology: A Reference Handbook. William Sims Bainbridge, Ed. SAGE Publications.
 * 2) W. Tomlinson (2010). "Greening Through IT" MIT Press.
 * 3) J. Peterson, et al. (2000). "Dormitory residents reduce electricity consumption when exposed to real- time visual feedback and incentives"
 * 4) J. Ross, et al. (2010). "Collaborative Filtering and Carbon Footprint Calculation"
 * 5) J. Padgett, et al. (2008). "A comparison of carbon calculators"
 * 6) A. Seetharam, et al. (2010) “Shipping to Streaming: Is this shift green?”
 * 7) A. Martin (2001). "Towards an Energy Complexity of Computation."
 * 8) S. Albers (2010). “Energy-Efficient Algorithms”
 * 9) R. Jain, et al. (2005). "Towards a Model of Energy Complexity for Algorithms."
 * 10) P. Edwards (2010). “History of Climate Modeling”
 * 11) P. Edwards (2001).“Representing the Global Atmosphere: Computer Models, Data, and Knowledge about Climate Change”
 * 12) J. Conrad, et al. (2010). “Incorporating Economic and Ecological Information into the Optimal Design of Wildlife Corridors”
 * 13) E. Brunskill and N. Lesh (2010). “Routing for Rural Health: Optimizing Community Health Worker Visit Schedules”
 * 14) B. Dilkina and C. Gomes, Solving Connected Subgraph Problems in Wildlife Conservation CPAIOR-10: 7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization, 2010. (Vanderbilt, Georgia Tech, UMass Amherst)
 * 15) J. Williams and S. Snyder (2005). “Restoring habitat corridors in fragmented landscapes using optimization and percolation models.”
 * 16) B. Dilkina and C. Gomes (2009). “Wildlife Corridor Design: connections to Computer Science”
 * 17) C. Monteleoni, et al. (2011). “Tracking Climate Models”
 * 18) C. McGann, et al. (2008). “Adaptive Control for Autonomous Underwater Vehicles”
 * 19) E. Fiorelli, et al. (2006). “Multi-AUV Control and Adaptive Sampling in Monterey Bay”
 * 20) J. Zhou and C. Clark (2006). “Autonomous fish tracking by ROV using Monocular Camera”
 * 21) A. Kohler and L. Erdmann (2004). “Expected Environmental Impacts of Pervasive Computing”

Computational Methods in Sustainable Energy (Carnegie Mellon, Fall 2012)
No readings. All lecture slides and videos can be found online here.

Computational Sustainability (U British Columbia, Winter 2013-2014)

 * 1) S. Murugesan (2008). “Harnessing Green IT: Principles and Practices”
 * 2) G. Cook and J. Van Horn (2011). “How dirty is your data? A Look at the Energy Choices That Power Cloud Computing”
 * 3) University of Washington (2011). "Code Green: Energy-Efficient Programming to Curb Computers' Power Use"
 * 4) A. Sampson, et al. (2013). "EnerJ, the Language of Good-Enough Computing"
 * 5) L. Newman (2013). "New Algorithms Reduce the Carbon Cost of Cloud Computing"
 * 6) J. Rockstrom, et al. (2009). "Planetary Boundaries: Exploring the Safe Operating Space for Humanity" (UBC, Georgia Tech)
 * 7) S. Easterbrook (2010)."Climate Change: A Grand Software Challenge"
 * 8) RealClimate.org (2013). "Sea level in the 5th IPCC report"
 * 9) R. Bras, et al. (2013). "Robust Network Design for Multispecies Conservation"
 * 10) T. Dietterich, et al. (2012). "Machine Learning for Computational Sustainability" (UBC, Georgia Tech)
 * 11) E. Poloczanska, et al. (2013). “Global imprint of climate change on marine life”
 * 12) UBC News (2013). “Google Earth reveals untold fish catches”
 * 13) V. Klemas (2013). “Fisheries applications of remote sensing: An overview”
 * 14) B. Chamberlain, et al. (2014). "A Decision Support System for the Design and Evaluation of Sustainable Wastewater Solutions"
 * 15) P. Condon (2010). Seven Rules for Sustainable Communities: Design Strategies for the Post Carbon World (book)
 * 16) A. Fialho, et al. (2012). "A Multi-objective Approach to Balance Buildings Construction Cost and Energy Efficiency"
 * 17) M. Osborne, et al. (2011). "A Machine Learning Approach to Pattern Detection and Prediction for Environmental Monitoring and Water Sustainability"
 * 18) J. Levinson et al., "Towards fully autonomous driving: Systems and algorithms," Intelligent Vehicles Symposium (IV), 2011 IEEE, Baden-Baden, 2011, pp. 163-168.
 * 19) UN Habitat (2013). “Planning and Design for Sustainable Urban Mobility: Global Report on Human Settlements 2013”
 * 20) W. Knight (2013). “Driverless Cars Are Further Away Than You Think”
 * 21) T. Gustafson (2013). “Robots Could Help Farmers Rein In Fertilizer Pollution”
 * 22) S. Makonin, et al. (2013). "Inspiring Energy Conservation Through Open Source Metering Hardware and Embedded Real-Time Load Disaggregation"
 * 23) S. Makonin, et al. (2013). "The Cognitive Power Meter: Looking Beyond the Smart Meter"
 * 24) O. Parson, et al. (2012). “Non-intrusive load monitoring using prior models of general appliance types”

Computational Sustainability (Georgia Tech, Spring 2014)

 * 1) J. M. Conrad, C. P. Gomes, W.-J. van Hoeve, A. Sabharwal, and J. F. Suter, Wildlife corridors as a connected subgraph problem, Journal of Environmental Economics and Management, vol. 63, no. 1, pp. 1–18, Jan. 2012 (Georgia Tech, UMass Amherst)
 * 2) D. Sheldon, B. Dilkina, A. Elmachtoub, R. Finseth, A. Sabharwal, J. Conrad, C. Gomes, D. Shmoys, W. Allen, O. Amundsen, and B. Vauguan, Maximizing the Spread of Cascades Using Network Design, in UAI-2010: 26th Conference on Uncertainty in Artificial Intelligence, pp. 517–526. (Georgia Tech, UMass Amherst, Cornell)
 * 3) X. Wu, D. Sheldon, and S. Zilberstein (2013). Stochastic Network Design for River Networks
 * 4) Z. Lu, D. Noonan, J. Crittenden, H. Jeong, and D. Wang, Use of Impact Fees To Incentivize Low-Impact Development and Promote Compact Growth Environmental Science & Technology 2013 47 (19), 10744-10752
 * 5) R. Evans, et al. (2013). “A review of computational optimisation methods applied to sustainable building design”
 * 6) A. Fialho, et al. (2012). "A Multi-objective Approach to Balance Buildings Construction Cost and Energy Efficiency"
 * 7) S. Grady, M. Hussaini, and M. Abdullah (2004). “Placement of wind turbines using genetic algorithms”
 * 8) I. Fountalis, A. Bracco, and C. Dovrolis (2014). “Spatio-temporal network analysis for studying climate patterns”
 * 9) A. Tsonis, K. Swanson, and P. Roebber (2006). “What Do Networks Have to Do with Climate?”
 * 10) K. Steinhaeuser, N. Chawla, and A. Ganguly (2010). “An exploration of climate data using complex networks”
 * 11) F. Hoffman, J. Kumar, R. Mills, and W. Hargrove (2013). “Representativeness-based sampling network design for the State of Alaska”
 * 12) V. Mithal, A. Khandelwal, S. Boriah, K. Steinhaeuser, and V. Kumar (2013). “Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping”
 * 13) P. Waddell (2002). “UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning”
 * 14) D. Fagnant and K. Kockelman (2014). “THE TRAVEL AND ENVIRONMENTAL IMPLICATIONS OF SHARED AUTONOMOUS VEHICLES, USING AGENT-BASED MODEL SCENARIOS”
 * 15) J. Hood, E. Sall, and B. Charlton (2011). “A GPS-based bicycle route choice model for San Francisco, California”
 * 16) D. Fink, et al. (2010). “Spatiotemporal exploratory models for broad-scale survey data”
 * 17) R. Kumar and M. Best (2006). “Impact and Sustainability of E-Government Services in Developing Countries: Lessons Learned from Tamil Nadu, India”
 * 18) T. Smyth and M. Best (2013). “Tweet to Trust: Social Media and Elections in West Africa”
 * 19) X. Lu, et al. (2013). “Approaching the Limit of Predictability in Human Mobility”
 * 20) J. Choo, et al. (2013). “A Better World for All: Understanding and Promoting Micro-finance Activities in Kiva.org”
 * 21) J. Leskovec, et al. (2007). “Cost-effective Outbreak Detection in Networks”
 * 22) D. Golovin, A. Krause, B. Gardner, S. J. Converse, and S. Morey, Dynamic Resource Allocation in Conservation Planning, 2011. (Georgia Tech, UMass Amherst)
 * 23) Y. Xue, B. Dilkina, T. Damoulas, and D. Fink, Improving Your Chances: Boosting Citizen Science Discovery, AAAI Conference on Human Computation and Crowdsourcing, pp. 198-206, 2013. (Georgia Tech, UMass Amherst)
 * 24) G. Zhang (2013). “Multiobjective Optimization of Low Impact Development Scenarios in an Urbanizing Watershed”
 * 25) G. Baranyi, S. Saura, J. Podani, and F. Jordán (2011). “Contribution of habitat patches to network connectivity: Redundancy and uniqueness of topological indices”
 * 26) J. Kolter and J. Ferreira Jr. (2011). “A Large-scale Study on Predicting and Contextualizing Building Energy Usage”

Seminar on Computational Sustainability: Algorithms for Ecology and Conservation(UMass Amherst, Spring 2014)

 * 1) D. Sheldon, A. Farnsworth, and J. Irvine, “Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar”, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI), 2013.
 * 2) C. Guestrin, A. Krause, and A. Singh, Near-optimal sensor placements in gaussian processes, Proceedings of the 22nd International conference on Machine Learning (ICML), vol. 1, 2005.
 * 3) S. Phillips, M. Dudík, and R. Schapire, A maximum entropy approach to species distribution modeling, Proceedings of the twenty-first International Conference on Machine Learning (ICML), pp. 655–662, 2004.
 * 4) I. W. Renner and D. I. Warton, Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology., Biometrics, vol. 69, no. 1, pp. 274–81, Mar. 2013. Occupancy Models in CS
 * 5) D. MacKenzie, J. Nichols, G. Lachman, S. Droege, J. A. Royle, and C. Langtimm, Estimating site occupancy rates when detection probabilities are less than one, Ecology, vol. 83, no. 8, pp. 2248-2255, 2002.
 * 6) R. Hutchinson, L. Liu, and T. Dietterich, Incorporating Boosted Regression Trees into Ecological Latent Variable Models., AAAI Conference on Artificial Intelligence, 2011.
 * 7) J. Yu, W.-K. Wong, and R. A. Hutchinson, Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling, 2010 IEEE International Conference on Data Mining, pp. 1157-1162, Dec. 2010.
 * 8) J. Lebreton and K. Burnham, Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies, Ecological Monographs, vol. 62, no. 1, pp. 67–118, 1992.
 * 9) M. Kéry and M. Schaub, Bayesian population analysis using WinBUGS A hierarchical perspective. Academic Press, 2011. (book)
 * 10) C. Zonneveld, Estimating Death Rates from Transect Counts, Ecological Entomology, vol. 16, pp. 115–121, 1991.
 * 11) E. Matechou, E. B. Dennis, S. N. Freeman, and T. Brereton, Monitoring abundance and phenology in (multivoltine) butterfly species ; a novel mixture model, 2013.
 * 12) S. Ermon, J. Conrad, C. Gomes, and B. Selman, Playing games against nature: optimal policies for renewable resource allocation, in Proc. of The 26th Conference on Uncertainty in Artificial Intelligence, 2010.
 * 13) I. Chades, J. Carwardine, T. Martin, S. Nicol, R. Sabbadin, and O. Buffet, MOMDPs: A Solution for Modelling Adaptive Management Problems., Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 267–273, 2012.
 * 14) B. McRae, B. Dickson, T. Keitt, and V. Shah, Using circuit theory to model connectivity in ecology, evolution, and conservation Ecology, vol. 89, no. 10, pp. 2712–2724, 2008.
 * 15) K. Lai, C. Gomes, and M. Schwartz, The Steiner Multigraph Problem: Wildlife Corridor Design for Multiple Species, AAAI Conference on Artificial Intelligence, 2011.

Topics in Computational Sustainability (Stanford, Spring 2016)
All lectures found here.

Topics in Computational Sustainability (Cornell University, Spring 2011)

 * 1) E. Cha, D. Bar, et. al. The cost and management of different types of clinical mastitis in dairy cows calculated by dynamic programming.
 * 2) Kiyan Ahmadizadeh, Bistra Dilkina, et. al. An Empirical Study of Optimization for Maximizing Diffusion in Networks. CP 2010.
 * 3) Berlow, E.L., J.A. Dunne, N.D. Martinez, P.B. Stark, R.J. Williams, and U. Brose. 2009. Simple prediction of interaction strengths in complex food webs. Proceedings of the National Academy of Sciences, USA 106:187-191.
 * 4) Dunne, J.A. 2009. Food webs. Pages 3661-3682 in Encyclopedia of Complexity and Systems Science, ed. R.A. Meyers. Springer, New York.
 * 5) Dunne, J.A., R.J. Williams, N.D. Martinez, R.A. Wood, and D.E. Erwin. 2008. Compilation and network analyses of Cambrian food webs. PLoS Biology 6:693-708.
 * 6) Diane E. Pataki, Margaret M Carreiro, et al. Coupling biogeochemical cycles in urban environments: ecosystem services, green solutions, and misconceptions. Frontiers in Ecology and the Environment, February 2011.
 * 7) Thomas Whitlow. The ecology of highways in Baltimore and Los Angeles: Pollution hotspots or opportunities for redevelopment?
 * 8) L. J. Moniz et al. Inferences about Coupling from Ecological Surveillance Monitoring: Approaches Based on Nonlinear Dynamics and Information Theory.
 * 9) J. M. Nichols et al. Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics.
 * 10) L. J. Moniz et al. Application of information theory methods to food web reconstruction.
 * 11) Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics.
 * 12) Gardner, T.A. et al. Long-term region-wide declines in Caribbean corals. Science 301, 958(2003).
 * 13) Charrel, R.N., Lamballerie, X. de Raoult, D. Chikungunya outbreaks--the globalization of vectorborne diseases. The New England journal of medicine 356, 769-71(2007).
 * 14) Briggs, C.J., Knapp, R.A. & Vredenburg, V.T. Enzootic and epizootic dynamics of the chytrid fungal pathogen of amphibians. Proceedings of the National Academy of Sciences of the United States of America 107, 9695-9700(2010).
 * 15) Jon M. Conrad and Kamola Kobildjanova The Economics of an Environmental Disaster: The Aral Sea IWREC.