In the project P2Map I am trying to combine objective sensor data with subjective context information and to use this combined information in machine learning methods to predict a geospatial and temporal distribution of air quality.
Therefore my interests spread across data mining and machine learning methods in general and more specificly methods for spatiotemporal data, for example land use regression. I am trying to develop a more general approach, because most land use regression models are designed for certain regions and rely on land usage data that may be only available in this region. For this task, neural networks, especially convolutional neural networks, are tested at the moment.
In another facet of the P2Map-project, an array of low cost sensors has to be calibrated in order to reliably measure the surrounding air quality. Finally for the incorporation of the subjective data, like context information or perceptions, I will be working with techniques from the field of natural language processing.
Introductions to algorithms and data structures:
Seminar "Web 2.0":
Seminar "Machine Learning":
SimLoss: Class Similarities in Cross Entropy. (2020).
Anomaly Detection in Beehives using Deep Recurrent Autoencoders. (2020). 142-149.
EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data. D. Burghardt, Chen, S., Andrienko, G., Andrienko, N., Purves, R., Diehl, A. (reds.) (2018).
Air Trails--Urban Air Quality Campaign Exploration Patterns. (2018).