Staff

Andreas Scheidegger

Andreas Scheidegger

Department Systems Analysis, Integrated Assessment and Modelling

About Me

Research Interest
Mathematical models must be tailored to the question at hand. All available information including data as well as system understanding should be considered. As a statistician my interests lay in constructing adequate models by means of statistics, machine learning and applied mathematics to investigate scientific hypotheses or support decision makers.

Besides the mathematical/technical aspects of modeling I’m also very interested in communication. Conveying to the users the underlying model assumptions, the interpretation and limits of the results is fundamental for a successful application of any model.


Methods and Tools
Some topics and methods I work with or I am interested in:

  • Bayesian Inference
  • Gaussian Processes
  • Data assimilation(Deep) Artificial Neuronal Networks
  • Uncertainty Quantification
  • Causal Inference
  • Graphical (hierarchical) Models

For implementation I use among others Julia, R, Python, STAN, Emacs.

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Curriculum Vitae

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Selected publications

Blumensaat, F.; Leitão, J. P.; Ort, C.; Rieckermann, J.; Scheidegger, A.; Vanrolleghem, P. A.; Villez, K. (2019) How urban storm- and wastewater management prepares for emerging opportunities and threats: digital transformation, ubiquitous sensing, new data sources, and beyond – a horizon scan, Environmental Science and Technology, 53(15), 8488-8498, doi:10.1021/acs.est.8b06481, Institutional Repository
Penn, R.; Maurer, M.; Michalec, F.-G.; Scheidegger, A.; Zhou, J.; Holzner, M. (2019) Quantifying physical disintegration of faeces in sewers: stochastic model and flow reactor experiments, Water Research, 152, 159-170, doi:10.1016/j.watres.2018.12.037, Institutional Repository
Mutzner, L.; Vermeirssen, E. L. M.; Mangold, S.; Maurer, M.; Scheidegger, A.; Singer, H.; Booij, K.; Ort, C. (2019) Passive samplers to quantify micropollutants in sewer overflows: accumulation behaviour and field validation for short pollution events, Water Research, 160, 350-360, doi:10.1016/j.watres.2019.04.012, Institutional Repository
Spuhler, D.; Scheidegger, A.; Maurer, M. (2018) Generation of sanitation system options for urban planning considering novel technologies, Water Research, 145, 259-278, doi:10.1016/j.watres.2018.08.021, Institutional Repository
Wani, O.; Scheidegger, A.; Carbajal, J. P.; Rieckermann, J.; Blumensaat, F. (2017) Parameter estimation of hydrologic models using a likelihood function for censored and binary observations, Water Research, 121, 290-301, doi:10.1016/j.watres.2017.05.038, Institutional Repository
McCall, A.-K.; Scheidegger, A.; Madry, M. M.; Steuer, A. E.; Weissbrodt, D. G.; Vanrolleghem, P. A.; Kraemer, T.; Morgenroth, E.; Ort, C. (2016) Influence of different sewer biofilms on transformation rates of drugs, Environmental Science and Technology, 50(24), 13351-13360, doi:10.1021/acs.est.6b04200, Institutional Repository
Scheidegger, A.; Leitão, J. P.; Scholten, L. (2015) Statistical failure models for water distribution pipes – a review from a unified perspective, Water Research, 83, 237-247, doi:10.1016/j.watres.2015.06.027, Institutional Repository
Scheidegger, A.; Scholten, L.; Maurer, M.; Reichert, P. (2013) Extension of pipe failure models to consider the absence of data from replaced pipes, Water Research, 47(11), 3696-3705, doi:10.1016/j.watres.2013.04.017, Institutional Repository

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Address

E-Mail: andreas.scheidegger@eawag.ch
Phone: +41 58 765 5053
Fax: +41 58 765 5802
Address: Eawag
Überlandstrasse 133
8600 Dübendorf
Office: FC D10

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Research Focus

Statistical Modeling

Machine Learning and Data Science

Uncertainty Quantification

Bayesian Inference

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