Welcome to the homepage of the BIOSTAT research group of the Faculty of Bioscience Engineering at Ghent University. The group is part of the Department of Data analysis and Mathematical Modelling and has a broad expertise in the theory and methods of statistical data analysis and its life sciences applications. In this era of ever growing quantities of data, the use of statistical methodology extends across a plethora of application domains, be this environmental monitoring, food processing or climate studies. The group studies applications within the current trends of industry 4.0 (where multiple sensors are being used to monitor the quality of industrial products), artificial intelligence (where software is being developed to make machines smart) and citizen science (where volunteers can contribute to scientific research).

Recent projects
VACANCY PhD STUDENT: Optimum survey design for the acquisition of spatial(-temporal) data.

BIOSTAT has a vacancy for a PhD student with a background in statistics and/or computer science. The student will work on the following (provisional) PhD topic. Interested? Apply before 22/02 through this link.

In various life science applications, the acquisition of spatial data can be labor intensive or costly such that in practice one is interested in an efficient design for data collection, e.g. for the acquisition of species observations to model species distribution or for the acquisition of weather observations for the study of climate extremes. Although a rich literature is available on spatial survey designs, less is known on how to develop optimum designs for integrating field surveys with modern acquisition techniques such as citizen science where data is collected by volunteers with different levels of expertise and in an unstandardized manner or remote sensing techniques where a parameter of interest is inferred from high-dimensional data. In this PhD project, we will test and adapt principles from the theory of optimum design to integrate optimum survey design of in-situ data with other types of sensed data. Computational methods (e.g. resampling methods, Monte Carlo theory among others) will play an important role in this research next to Bayesian statistical modelling with e.g. point processes, kriging and hierarchical modelling. Background or experience in these specific techniques is an asset but not necessarily.

Citizen Science Data for Biodiversity Conservation Policy in Agricultural Landscapes

The aim of the project is to investigate how opportunistic citizen science data can support biodiversity conservation policy in agriculture in an era of growing environmental concerns and increasing land use intensity. Species distribution models will be used to reduce the simultaneous effects of several biases that occur in opportunistic citizen science data. A combined approach will be proposed to minimize bias and we will use landscape metrics obtained through remote sensing to improve model predictions of species occurrence. Our findings will provide insight in the drivers of biodiversity in an agricultural landscape and will allow to simulate the future state of biodiversity by implementing different land use scenarios. To illustrate the application potential of our results, we address the most prominent question in today’s ‘green’ agricultural policy: do the current policy efforts, meant to safeguard and improve biodiversity, pay off and how can their effectiveness be increased? With this project, we aim to take a step forward in the use of citizen science data for policy purposes. We wish to support the uptake of environmentally beneficial practices in farms, while aspiring a more sustainable land use future.

Doctorandus: Camille Van Eupen

Open set recognition using extreme value statistics applied on (big) biological data

Classification and anomaly detection tasks are frequently encountered in today's data driven era. A large part of classification methods are based on an optimization process (a so-called training phase) that learns the boundaries between classes from available examples. These algorithms assume that all classes are known and available during training. Open set recognition is the process of detecting new classes that were not seen by the model during training. In this project solutions for the open set recognition problem will be developped that are based on the use of extreme value statistics, a field in statistics that relies on extrapolation from the data of observed classes. This work builds further on our previous obtained results in novelty detection [10,12] . A potential application of these methods is the detection of invasive species using image data. Automatic recognition of species based on image data could ease and support the labour intensive job of experts to go through all images.

Doctorandus: Matthys Lucas Steyn