Mitarbeitende

Carlo Albert

Dr. Carlo Albert

Abteilung Systemanalyse, Integrated Assessment und Modellierung

Über mich

Head of the group Mathematical Methods in Environmental Research, at the  Department of Systems Analysis, Integrated Assessment and Modelling

Education

Ph.D. in Theoretical Physics, Dr. Phys. ETH

M.Sc. in Mathematics, Dipl. Math. ETH

Research

Modelling of environmental systems
I apply methods from statistical physics, nonlinear systems theory and statistics to detect and model predictable features of complex environmental systems. Of particular interest is a faithful quantification of uncertainty.

Selected publications

DD Giudice, M Honti, A Scheidegger, C Albert, P Reichert, J Rieckermann, Improving uncertainty estimation in urban hydrological modeling by statistically describing bias, Hydrology and Earth System Sciences 17 (10), 4209-4225, 2013.
doi:10.5194/hess-17-4209-2013

Jager T., Albert C., Preuss T.G., Ashauer R., General Unified Threshold Model of Survival - a Toxicokinetic Toxicodynamic Framework for Ecotoxicology, Env. Science and Technology, 45, 2529-2540, 2011.
doi: 10.1021/es103092a

Calogovic J., Albert C., Arnold F., Beer J., Desorgher L., and Flueckiger E. O., Sudden cosmic ray decreases: No change of global cloud cover, Geophys. Res. Lett. 37,  2010.
doi:10.1029/2009GL041327


Development of algorithms
Quantifying the parametric uncertainty of a model that needs to be calibrated to data is a computationally hard problem, in particular, if the model is slow or stochastic. Statistical physics and non-equilibrium thermodynamics offer some great tools to make parameter inference with stochastic models more efficient. Mechanistic emulators are an efficient way of speeding up slow simulators and make them amenable to simulation-intense tasks such as parameter inference.

Selected publications

Albert C., Ulzega S., Stoop, R., Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation, Phys. Rev. E 93, 2016.
doi: 10.1103/PhysRevE.93.043313, arXiv:1509.05305 [cs.DS]

Albert C., A Simulated Annealing Approach to Bayesian Inference,  2015.
arXiv:1509.05315 [stat.CO]

Albert C., Künsch HR., Scheidegger A., A Simulated Annealing Approach to Approximate Bayes Computations, Stat. Comput., 2014.
doi: 10.1007/s11222-014-9507-8, arXiv:1208.2157 [stat.CO]

Albert C., A Mechanistic Dynamic Emulator, J. Nonlinear Analysis B 13, 2747–2754, 2012.

doi:10.1016/j.nonrwa.2012.04.003, arXiv:1112.5304v2 [stat.ME].


Publications    Google Scholar


Teaching     Summer School in Environmental Systems Analysis


Workshop  History of Solar Activity Recorded in Polar Ice

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Projekte

Komplexe Systemtheorie trifft auf Phytoplankton “Big Data”.
Kalibrierung stochastischer Regen-Abflussmodelle mit Hilfe von Skalengesetzen für verbesserte Prognosen von Extremereignissen.

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Ausgewählte Publikationen

Held, J.; Lorimer, T.; Pomati, F.; Stoop, R.; Albert, C. (2020) Second-order phase transition in phytoplankton trait dynamics, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(5), 053109 (9 pp.), doi:10.1063/1.5141755, Institutional Repository
Kavetski, D.; Fenicia, F.; Reichert, P.; Albert, C. (2018) Signature-domain calibration of hydrological models using Approximate Bayesian Computation: theory and comparison to existing applications, Water Resources Research, 54(6), 4059-4083, doi:10.1002/2017WR020528, Institutional Repository
Machac, D.; Reichert, P.; Rieckermann, J.; Del Giudice, D.; Albert, C. (2018) Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator, Environmental Modelling and Software, 109, 66-79, doi:10.1016/j.envsoft.2018.07.016, Institutional Repository
Albert, C.; Ulzega, S.; Stoop, R. (2016) Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation, Physical Review E, 93(4), 1-8, doi:10.1103/PhysRevE.93.043313, Institutional Repository
Del Giudice, D.; Albert, C.; Rieckermann, J.; Reichert, P. (2016) Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation, Water Resources Research, 52(4), 3162-3186, doi:10.1002/2015WR017871, Institutional Repository
Albert, C.; Vogel, S.; Ashauer, R. (2016) Computationally efficient implementation of a novel algorithm for the General Unified Threshold model of Survival (GUTS), PLoS Computational Biology, 12(6), 1-19, doi:10.1371/journal.pcbi.1004978, Institutional Repository
Del Giudice, D.; Reichert, P.; Bareš, V.; Albert, C.; Rieckermann, J. (2015) Model bias and complexity - understanding the effects of structural deficits and input errors on runoff predictions, Environmental Modelling and Software, 64, 205-214, doi:10.1016/j.envsoft.2014.11.006, Institutional Repository
Albert, C.; Künsch, H. R.; Scheidegger, A. (2015) A simulated annealing approach to approximate Bayes computations, Statistics and Computing, 25(6), 1217-1232, doi:10.1007/s11222-014-9507-8, Institutional Repository
Albert, C. (2012) A mechanistic dynamic emulator, Nonlinear Analysis: Real World Applications, 13(6), 2747-2754, doi:10.1016/j.nonrwa.2012.04.003, Institutional Repository

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Adresse

E-Mail: carlo.albert@eawag.ch
Telefon: +41 58 765 5244
Fax: +41 58 765 5802
Adresse: Eawag
Überlandstrasse 133
8600 Dübendorf
Büro: FC D02

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Experte für

Modellierung, Datenwissenschaft

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