Data Science Chair

    Deep Learning for Imbalanced Data

    The Data Science Chair works with environmental data to contribute to the ongoing climate research. Based on low-cost sensors, map and geographical data, we develop novel approaches to environmental questions. These questions include "What is the air pollution at a given spot on earth?" and "What type of wine should I grow on my land given the development in climate in the next few decades?". While answering such questions is one concern of our research, the other objective is to raise awareness for the environment in the society.


    We are currently working on the following projects:


    Combining low-cost mobile sensors and subjective feedback to build understandable maps regarding air pollution in Würzburg.


    Developing machine learning based approaches to regional environmental prediction models and answering future-related questions by small and medium-sized enterprises regarding environmental factors.


    Learning and researching with bees.


    Here is a list of selected publications.

    • Anomaly Detection in Beeh...
      Anomaly Detection in Beehives using Deep Recurrent Autoencoders Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Kobs, Konstantin; Krause, Anna; Hotho, Andreas in Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020) (2020). 142–149.
    • Collective Sensing Platfo...
      Collective Sensing Platforms Atzmueller, Martin; Becker, Martin; Mueller, Juergen in Participatory Sensing, Opinions and Collective Awareness (2017). 115–133.
    • Participatory sensing, op...
      Participatory sensing, opinions and collective awareness Loreto, Vittorio; Haklay, Mordechai; Hotho, Andreas; Servedio, Vito C. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca (2017). Springer.
    • Participatory Patterns in...
      Participatory Patterns in an International Air Quality Monitoring Initiative Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris in PLoS ONE (2015). 10(8) e0136763.
    • Awareness and learning in...
      Awareness and learning in participatory noise sensing Becker, Martin; Caminiti, Saverio; Fiorella, Donato; Francis, Louise; Gravino, Pietro; Haklay, Mordechai (Muki); Hotho, Andreas; Loreto, Vittorio; Mueller, Juergen; Ricchiuti, Ferdinando; Servedio, Vito D. P.; Sirbu, Alina; Tria, Francesca in PLOS ONE (2013). 8(12) e81638.