School of Engineering

Health and Environmental Analytics

Statistical modeling and machine learning make complex data understandable and provide sound insights, supporting smart decisions on health and environmental issues in business, science, and policy.

Our Data Analytical Approaches

  • Statistical Methods are tools for collecting and evaluating data. They help identify trends, validate hypotheses, and support data-driven decisions even under uncertainty. 
  • Predictive Analytics applies statistical models and machine learning techniques, including ensemble methods and deep learning, to analyze data and reliably forecast future trends, events, or behaviors.
  • Special Topics
    • Causal Inference
    • Conformal Prediction
    • Data Anonymization
    • Experimental and Study Design
    • Network Analysis

Research & Projects

A Model-Based Three-Stage Classifier for Particulate Matter

Heatmap of the elemental composition of 889 particles from a sample collected in Kaiseraugst. More in project details.

Development of a universally applicable three-stage particle classifier for Particle Vision GmbH, which categorizes particles into several thousand classes based on their chemical composition.
Project details

Data Anonymization

Distribution of individual re-identification risk for pseudonymized data and anonymized data. More in project details.

Data anonymization is required for both data sharing and internal use to prevent identification of individuals while preserving analytical quality as much as possible. This anonymization process is applied to complex data from Helsana.
Project details

Deep Learning-Based Classification of Histological Subtypes of Lung T

a) 3D t-SNE of 4,096 features from lung cancer images.
b) VGG CNN extracts 4,096 image features at specified layer.
More in project details.

Deep learning methods (specifically convolutional neural networks) can distinguish different types of lung tumors from histological images almost as accurately as pathologists.
Project details

Sales Forecasting for the Restaurant and Catering Industry

Understanding guest flow and preferences supports restaurant operations through data-driven planning, enabling accurate forecasting, inventory control, and staff scheduling. More in project details.

Development of forecasting algorithms that use calendar data, weather, events and other factors for Prognolite, a provider of software for revenue and footfall forecasting in the hospitality industry.
Project details

Tariff System for Inpatient Rehabilitation

Schematic view of the grouping logic in the tariff system

As part of the performance-based and lump-sum reimbursement model for hospitals and clinics mandated by the Swiss Health Insurance Act, ZHAW has developed a tariff system for performance-based daily flat rates in rehabilitation.
Project details

News

No news available.

Publications

Some recent articles in scientific journals, peer-reviewed

Templ Matthias, Hofer Christoph, 2025. Compositional analysis of the relationships between the organic matter content and chemical and physical properties of soil. Applied Geochemistry. 193, S. 1-16. https://doi.org/10.1016/j.apgeochem.2025.106526

Achermann Basil B, Regazzi Naire, Heynen Rahel, Lüdin Dennis, Suter Julia, Drewek Anna, Lorenzetti Silvio R., 2025. From monitoring to prediction : velocity-based strength training in female floorball athletes.Sports. 13(6), S. 175. https://doi.org/10.3390/sports13060175

Spurk Christoph, Koch Carmen, Bürgin Reto, Chikopela Louis,  Konaté Famagan, Nyabuga George, Sarpong Daniel Bruce, Sousa Fernando, Fliessbach Andreas, 2023. Farmers’ innovativeness and positive affirmation as main drivers of adoption of soil fertility management practices: evidence across sites in Africa. The Journal of Agricultural Education and Extension. https://doi.org/10.1080/1389224X.2023.2281909

Bürgin Reto, Muratori Corrado, Roca-Riu Mireia, Heitz Christoph, 2023. A space-time model for demand in free-floating carsharing systems. Journal of Advanced Transportation. 2023(6610624). https://doi.org/10.1155/2023/6610624

Arpogaus Marcel, Voss Marcus, Sick Beate, Nigge-Uricher Mark, Dürr Oliver, 2023. Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid. 14(6), S. 4902-4911. https://doi.org/10.1109/TSG.2023.3254890

Mildenberger Thoralf, Braschler Martin, Ruckstuhl Andreas, Vorburger Robert, Stockinger Kurt, 2023. The role of data scientists in modern enterprises : experience from data science education. SIGMOD Record. 52(2), S. 48-52. https://doi.org/10.21256/zhaw-27357

Müller Werder Claude, Mildenberger Thoralf, Steingruber Daniel, 2023. Learning effectiveness of a flexible learning study programme in a blended learning design : why are some courses more effective than others? International Journal of Educational Technology in Higher Education. 20(10). https://doi.org/10.1186/s41239-022-00379-x

Some recent articles in scientific journals, non-peer-reviewed

Thalmann Basilius, Hofer Christoph, Wächter Daniel, Kulli Beatrice, 2022. Per- und polyfluorierte Alkylsubstanzen (PFAS) in Schweizer Böden. altlasten spektrum. 31(6), S. 176-179. https://doi.org/10.37307/j.1864-8371.2022.06.05

Some recent published project reports

Bürgin Reto, Stucki Michael, Vetsch-Tzogiou Christina, Kauer Lukas Kohler Andreas, Drewek Anna, Thommen Christoph, Dettling Marcel, Wieser Simon, 2024. Wirkungsanalyse zum Risikoausgleich mit pharmazeutischen Kostengruppen (PCG): Schlussbericht. https://doi.org/10.21256/zhaw-30489

Drewek Anna, Ordelt Christian, Riahi Nima, Sedding Helmut, 2024. 100 Jahre Sollzeiten - Ein Konzept für die Zukunft?. Logistics Innovation. 2024(1), S. 10-13.

Cieliebak Mark, Drewek, Anna, Jakob Grob Karin, Kruse Otto, Mlynchyk Katsiaryna, Rapp Christian, Waller Gregor, 2023. Generative KI beim Verfassen von Bachelorarbeiten: Ergebnisse einer Studierendenbefragung im Juli 2023. https://doi.org/10.21256/zhaw-2491

Some recent talks, peer-reviewed

Bürgin, Reto; Vetsch-Tzogiou, Christina; Stucki, Michael; Kauer, Lukas; Pirktl, Lennart; van Kleef, Richard C.; Kohler, Andreas; Drewek, Anna; Thommen, Christoph; Dettling, Marcel; Wieser, Simon, 2024. Improving risk adjustment in Switzerland with pharmaceutical cost groups [Paper]. In: 6th Swiss Health Economic Workshop, Lucerne, Switzerland, 7 June 2024.

 

A complete overview of all publications by the institute's members can be found in the institute's publication list.

Teaching

“We teach students to learn from data with statistical methods, separating structure from noise."
 


Lectures

We teach Bachelor students all about statistical data analysis starting from exploratory data analysis, to inferential statistics, statistical modelling, advanced regression modelling, survey design analysis, data mining, predictive modelling, data analytics, and statistical quality control.

Our primary teaching contributions are focused on the Bachelor programs Wirtschaftsingenieurwesen, Mobility Science and Data Science , complemented by two advanced module courses—Advanced Statistical Data Analysis and Business Analytics—within the Master of Engineering program.

Furthermore, we are running two CAS course call Data Analysis and Advanced Statistical Data Analysis organised by the school of Engineering under the program MAS Data Science. In addition, we offer a short introductory course in R, called R Boot Camp.

 

Supervising Student Projects

We are excited to collaborate with students and provide dedicated support to both Bachelor's and Master's (MSE) candidates throughout their project journeys.
You can explore current project and thesis opportunities on Complesis.
We also welcome your own ideas and would be glad to discuss them with you!
 

Team