Bayesian lot inspection plans

Data-driven models for the study of extremes

Doctoral project Matthys Lucas Steyn

Efficient data acquisition for the statistical analysis of spatio-temporal data.

Integrating opportunistic citizen science data and species distribution models for biodiversity conservation policy and management

Intelligent Data Acquisition for Spatio-Temporal Learning of Climate Data and its Extremes.

Open-Set Recognition using Extreme Value Theory.

Optimum survey design for the acquisition of spatial-(temporal) data

Spatio-temporal data analysis and intelligent data-acquisition

Using near-infrared spectra of coffee beans and engaging citizens for the assessment of coffee quality

A combined framework of machine learning and extreme value theory for anomaly detection and ranking

Doctoral project Jelle Hustinx

Intelligent data acquisition for incorporation of dynamic headspace composition into growth models for psychrotrophic food pathogens in the context of sustainable food packaging

The molecular causes and clinical consequences of dysregulation at the <i>HM13</i> locus in cancer

BOF-TT-professorship in biostatistics

Citizen Science Data for Biodiversity Conservation Policy in Agricultural Landscapes

Optimum survey design for the study of spatio-temporal data

Spatio-temporal statistics to study dynamic biological systems

A combined framework of machine learning and extreme value theory for anomaly detection and ranking

Towards 3D TURbidity by correlating multiBEAM sonar and in-situ Sensor data - TURBEAMS

BOF-TT-professorship in biostatistics