Staff

Andreas Scheidegger

Andreas Scheidegger

Department Systems Analysis, Integrated Assessment and Modelling

About Me

Research Interest
Mathematical modeling is a fundamental tool of science. Models may be applied to extract knowledge from data, to combine available information, or to make predictions about the future states of a system. In any case models need to be tailored to the scientific questions at hand. Ideally a model utilize all available information. This includes (often expensive field or lab) data but also system understanding and expert opinions.

As a statistician my interests lay in constructing adequate models by means of statistics, machine learning and applied mathematics to investigate scientific hypotheses and support decision makers.

Besides the mathematical and technical aspects of modeling I’m also very interested in communication, which is an important element of successful collaboration.  To be able to apply a model successful, users must understand the underlying model assumptions, how to interpret the results, and know the limits of a model.

Obtaining scientific data from field observations or experiments is a laborious and expensive task – my aim is to facilitate the best possible use of this hard-earned data.

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

  • Bayesian Inference
  • Gaussian Processes
  • Data assimilation(Deep)
  • Machine Learning
  • 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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