Staff

Andreas Scheidegger

Extbase Variable Dump
'fileadmin/user_upload/tx_userprofiles/profileImages/scheidan.jpg' (64 chars)

Andreas Scheidegger

Statistics, Data Science & Modeling

Department Systems Analysis, Integrated Assessment and Modelling

About Me

Research Interest
As a statistician, my focus is on using statistical techniques, machine learning, and applied mathematics to develop accurate models for examining scientific hypotheses and aiding decision making.

Mathematical modeling is a fundamental tool of science. Models are 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, so that all available information is utilized. This includes data but also system understanding and expert opinions.

In addition to the technical aspects of modeling, I also place a strong emphasis on effective communication, as it is crucial for successful collaboration. For successful application users must understand the underlying assumptions and limitations 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
  • Machine Learning
  • Artificial Neuronal Networks, Deep Learning
  • Uncertainty Quantification
  • Causal Inference
  • Graphical (hierarchical) Models

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

[[ element.title ]]

[[ element.title ]]

Curriculum Vitae

[[ entry.date || 'empty' ]]

[[ element.title ]]

Publications

[[item.title]]
Höge, M.; Scheidegger, A.; Baity-Jesi, M.; Albert, C.; Fenicia, F. (2022) Improving hydrologic models for predictions and process understanding using neural ODEs, Hydrology and Earth System Sciences, 26(19), 5085-5102, doi:10.5194/hess-26-5085-2022, Institutional Repository
Fernandez-Cassi, X.; Scheidegger, A.; Bänziger, C.; Cariti, F.; Tuñas Corzon, A.; Ganesanandamoorthy, P.; Lemaitre, J. C.; Ort, C.; Julian, T. R.; Kohn, T. (2021) Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high, Water Research, 200, 117252 (9 pp.), doi:10.1016/j.watres.2021.117252, Institutional Repository
Fu, Q.; Scheidegger, A.; Laczko, E.; Hollender, J. (2021) Metabolomic profiling and toxicokinetics modeling to assess the effects of the pharmaceutical diclofenac in the aquatic invertebrate Hyalella azteca, Environmental Science and Technology, 55(12), 7920-7929, doi:10.1021/acs.est.0c07887, Institutional Repository
Caradima, B.; Scheidegger, A.; Brodersen, J.; Schuwirth, N. (2021) Bridging mechanistic conceptual models and statistical species distribution models of riverine fish, Ecological Modelling, 457, 109680 (15 pp.), doi:10.1016/j.ecolmodel.2021.109680, Institutional Repository
Gold, M.; Egger, J.; Scheidegger, A.; Zurbrügg, C.; Bruno, D.; Bonelli, M.; Tettamanti, G.; Casartelli, M.; Schmitt, E.; Kerkaert, B.; De Smet, J.; Van Campenhout, L.; Mathys, A. (2020) Estimating black soldier fly larvae biowaste conversion performance by simulation of midgut digestion, Waste Management, 112, 40-51, doi:10.1016/j.wasman.2020.05.026, Institutional Repository
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

[[ element.title ]]

[[ element.title ]]

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

[[ element.title ]]

[[ element.title ]]

[[ element.title ]]

[[ element.title ]]

Focalpoints

Statistical Modeling

Machine Learning and Data Science

Uncertainty Quantification

Bayesian Inference

[[ element.title ]]