Computational Sustainability

Computational sustainability is an interdisciplinary field of sustainability research, including applied science about the research in sustainable solutions and their implementation. Machine Learning and Data Mining is at the center of this research area linking together diverse application areas such as environmental sciences, atmospheric science, agriculture, or social science.


Chester, Andrew; Koh, Yun Sing; Sun, Quan; Wicker, Jörg; Lee, Junjae

Balancing Utility and Fairness against Privacy in Medical Data Inproceedings Forthcoming

IEEE Symposium Series on Computational Intelligence, IEEE, Forthcoming.

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Roeslin, Samuel; Ma, Quincy; and Chigullapally, Pavan; Wicker, Jörg; Wotherspoon, Liam

Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience Inproceedings Forthcoming

17th World Conference on Earthquake Engineering, Forthcoming.

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Roeslin, Samuel; Ma, Quincy; Juárez-Garcia, Hugon; Gómez-Bernal, Alonso; Wicker, Jörg; Wotherspoon, Liam

A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake Journal Article

Earthquake Spectra, 2020.

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Roeslin, Samuel; Ma, Quincy; Wicker, Jörg; Wotherspoon, Liam

Data integration for the development of a seismic loss prediction model for residential buildings in New Zealand Inproceedings

Cellier, Peggy; Driessens, Kurt (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 88-100, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-43887-6.

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Latino, Diogo; Wicker, Jörg; Gütlein, Martin; Schmid, Emanuel; Kramer, Stefan; Fenner, Kathrin

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data Journal Article

Environmental Science: Process & Impact, 2017.

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Wicker, Jörg; Fenner, Kathrin; Kramer, Stefan

A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction Incollection

Lässig, Jörg; Kersting, Kristian; Morik, Katharina (Ed.): Computational Sustainability, pp. 75-97, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-31858-5.

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Wicker, Jörg; Lorsbach, Tim; Gütlein, Martin; Schmid, Emanuel; Latino, Diogo; Kramer, Stefan; Fenner, Kathrin

enviPath - The Environmental Contaminant Biotransformation Pathway Resource Journal Article

Nucleic Acid Research, 44 (D1), pp. D502-D508, 2016.

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Wicker, Jörg

Large Classifier Systems in Bio- and Cheminformatics PhD Thesis

Technische Universität München, 2013.

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Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan

Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach Journal Article

Bioinformatics, 26 (6), pp. 814-821, 2010.

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Hardy, Barry; Douglas, Nicki; Helma, Christoph; Rautenberg, Micha; Jeliazkova, Nina; Jeliazkov, Vedrin; Nikolova, Ivelina; Benigni, Romualdo; Tcheremenskaia, Olga; Kramer, Stefan; Girschick, Tobias; Buchwald, Fabian; Wicker, Jörg; Karwath, Andreas; Gütlein, Martin; Maunz, Andreas; Sarimveis, Haralambos; Melagraki, Georgia; Afantitis, Antreas; Sopasakis, Pantelis; Gallagher, David; Poroikov, Vladimir; Filimonov, Dmitry; Zakharov, Alexey; Lagunin, Alexey; Gloriozova, Tatyana; Novikov, Sergey; Skvortsova, Natalia; Druzhilovsky, Dmitry; Chawla, Sunil; Ghosh, Indira; Ray, Surajit; Patel, Hitesh; Escher, Sylvia

Collaborative development of predictive toxicology applications Journal Article

Journal of Cheminformatics, 2 (1), pp. 7, 2010, ISSN: 1758-2946.

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Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan

Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction Inproceedings

Bridewell, Will; Calders, Toon; de Medeiros, Ana Karla; Kramer, Stefan; Pechenizkiy, Mykola; Todorovski, Ljupco (Ed.): Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008, 2008.

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