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Computational sustainability: computing for a better world and a sustainable future

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Published:21 August 2019Publication History
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Abstract

Computer and information scientists join forces with other fields to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.

References

  1. Abdelrahman, H., Berkenkamp, F., Poland, J., and Krause, A. Bayesian optimization for maximum power point tracking in photovoltaic power plants. In Proceedings of the 2016 European Control Con. (Aalborg, Denmark, June 29--July 1, 2016), 2078--2083.Google ScholarGoogle ScholarCross RefCross Ref
  2. Albers, J.H., Dietterich, T., Hall, K., Katherine, L., and Taleghan, M. Simulator-defined Markov decision processes: A case study in managing bio-invasions. Artificial Intelligence and Conservation (2nd. ed.). F. Fang, M. Tambe, B. Dilkina, and A. Plumptre, (Eds.). Cambridge Univ. Press, 2018Google ScholarGoogle Scholar
  3. Azimi, J., Fern, X., and Fern, A. Budgeted optimization with constrained experiments. J. Artif. Intell. Res. 56 (2016), 119--152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bai, J. et al. Phase mapper: Accelerating materials discovery with AI. AI Mag. 39, 1 (2018), 15--26.Google ScholarGoogle Scholar
  5. Barrett, C., Garg, T., and McBride, L. Well-being dynamics and poverty traps. Annual Review of Resource Economics 8 (2016), 303--327.Google ScholarGoogle ScholarCross RefCross Ref
  6. Bernstein, G., McKenna, R., Sun, T., Sheldon, D., Hay, M., and Miklau, G. Differentially private learning of undirected graphical models using collective graphical models. In Proceedings of the 34<sup>th</sup> International Conference on Machine Learning, 2017, 478--487. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Chen, D., Xue, Y., and Gomes, C. End-to-end learning for the deep multivariate probit model. ICML (2018).Google ScholarGoogle Scholar
  8. Coble, K., Mishra, A., Ferrell, S., and Griffin, T. Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy 40, 1 (2018), 79--96.Google ScholarGoogle ScholarCross RefCross Ref
  9. Dilkina, B. et al. Trade-offs and efficiencies in optimal budget-constrained multi-species corridor networks. Conservation Biology 31, 1 (2017), 192--202.Google ScholarGoogle ScholarCross RefCross Ref
  10. Donti, P., Kolter, J.Z., and Amos, B. Task-based end-to-end model learning in stochastic optimization. In Proceedings of Advances in Neural Information Processing Systemsg Systems (Long Beach, CA, USA, Dec. 4--9, 2017), 5490--5500. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Ermon, S. et al. Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa. In AAAI, 2015, 644--650. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Faghmous, J. and Kumar, V. A big data guide to understanding climate change: The case for theory-guided data science. Big Data 2, 3 (2014), 155--163.Google ScholarGoogle ScholarCross RefCross Ref
  13. Fang, F. et al. PAWS---A deployed game-theoretic application to combat poaching. AI Magazine 38, 1 (2017), 23--36.Google ScholarGoogle ScholarCross RefCross Ref
  14. Fang, F., Tambe, M., Dilkina, B., and Plumptre, A (eds.). Artificial Intelligence and Conservation. Cambridge University Press, 2018.Google ScholarGoogle Scholar
  15. Fink, D. et al. Spatiotemporal exploratory models for broad-scale survey data. Ecological Applications 20, 8 (2010), 2131--2147.Google ScholarGoogle ScholarCross RefCross Ref
  16. Fisher, D.H. Recent advances in AI for computational sustainability. IEEE Intelligent Systems 31, 4 (2016), 56--61 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Freund, D., Henderson, S.G., and Shmoys, D.B. Sharing Economy: Making Supply Meet Demand. Springer, 2018.Google ScholarGoogle Scholar
  18. Gomes, C.P. Computational sustainability: Computational methods for a sustainable environment, economy, and society. The Bridge 39, 4 (2009), 5--13.Google ScholarGoogle Scholar
  19. Grover, A. et al. Best arm identification in multi-armed bandits with delayed feedback. In Proceedings of the Inter Conf. Artificial Intelligence and Statistics (Playa Blanca, Lanzarote, Canary Islands, Spain, April 9--11, 2018), 833--842.Google ScholarGoogle Scholar
  20. Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., and Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790--794.Google ScholarGoogle ScholarCross RefCross Ref
  21. Kelling, S. et al. Can observation skills of citizen scientists be estimated using species accumulation curves? PloS one 10, 10 (2015).Google ScholarGoogle Scholar
  22. Khazaei, J. and Powell, W.B. SMART-Invest: A stochastic, dynamic planning for optimizing investments in wind, solar, and storage in the presence of fossil fuels. The case of the PJM electricity market. Energy Systems 9, 2 (2018), 277--303.Google ScholarGoogle ScholarCross RefCross Ref
  23. Kraus, S. Automated negotiation and decision-making in multiagent environments. ECCAI Advanced Course on Artificial Intelligence. Springer, 2001, 150--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Lässig, J., Kersting, K., and Morik, K (Eds.). Computational Sustainability. Studies in Computational Intelligence 645 (2016). Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Phillips, S.J., Anderson, R.P., and Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecological Modeling 190, 3--4 (2006), 231--259.Google ScholarGoogle ScholarCross RefCross Ref
  26. Powell, W. A unified framework for stochastic optimization. European J. Operational Research 275, 3 (2019), 795--821.Google ScholarGoogle ScholarCross RefCross Ref
  27. Reynolds, M.D. et al. Dynamic conservation for migratory species. Science Advances 3, 8 (2017), e1700707.Google ScholarGoogle ScholarCross RefCross Ref
  28. Rockström, J. et al. Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society 14, 2 (2009).Google ScholarGoogle ScholarCross RefCross Ref
  29. Rudin, C. and Wagstaff, K. Machine learning for science and society. Machine Learning 95, 1 (2014), 1--9 Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Ruiz-Muñoz, J.F., You, Z., Raich, R., and Fern, X.Z. Dictionary learning for bioacoustics monitoring with applications to species classification. Signal Processing Systems 90, 2 (2018), 233--247. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Russell, S.J. et al. Letter to the editor: Research priorities for robust and beneficial artificial intelligence: An open letter. AI Magazine 36, 4 (2015).Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Sheldon, D.R. and Dietterich, T.G. Collective graphical models. Advances in Neural Information Processing Systems, 2011, 1161--1169. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Sheldon, D.R. et al. Approximate Bayesian inference for reconstructing velocities of migrating birds from weather radar. In Proceedings of the 27<sup>th</sup> AAAI Conf. Artificial Intelligence. (Bellevue, WA, USA, July 14--18, 2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Sullivan, B.L. et al. The eBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation 169 (2014), 31--40.Google ScholarGoogle ScholarCross RefCross Ref
  35. Tambe, M. and Rice, E (eds.). Artificial Intelligence and Social Work. Cambridge University Press, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Giesen, N., Hut, R., and Selker, J. The Trans-African Hydro-Meteorological Observatory (TAHMO). Wiley Interdisciplinary Reviews: Water 1, 4 (2014), 341--348.Google ScholarGoogle Scholar
  37. Wahabzada, M., Mahlein, A.K., Bauckhage, C., Steiner, U., Oerke, E.C., and Kersting, K. Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants. Scientific Reports 6 (2016).Google ScholarGoogle Scholar
  38. Wu, X. et al. Efficiently approximating the Pareto frontier: Hydropower dam placement in the Amazon basin. In AAAI (2018).Google ScholarGoogle Scholar
  39. Xue, Y., Davies, I., Fink, D., Wood, C., and Gomes, C.P. Avicaching: A two stage game for bias reduction in citizen science. In Proceedings of the 2016 Intl. Conf. on Autonomous Agents & Multiagent Systems. 776--785. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Yadav, A. et al. Influence maximization in the field: The arduous journey from emerging to deployed application. In Proceedings of the 16<sup>th</sup> Conference on Autonomous Agents and MultiAgent Systems, 2017, 150--158. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            • Published in

              cover image Communications of the ACM
              Communications of the ACM  Volume 62, Issue 9
              September 2019
              95 pages
              ISSN:0001-0782
              EISSN:1557-7317
              DOI:10.1145/3358415
              Issue’s Table of Contents

              Copyright © 2019 Owner/Author

              This work is licensed under a Creative Commons Attribution International 4.0 License.

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 21 August 2019

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