Padraig Davidson, M.Sc.
Chair of Data Science (Informatik X)
University of Würzburg
Campus Hubland Nord
Emil-Fischer-Straße 50
97074 Würzburg
Germany
Email: davidson <at> informatik.uni-wuerzburg.de
Phone: (+49 931) 31-89114
Office: Room 50.03.10 (Computer Science Building 50)
Projects and Research Interests
I received my 1. State Examination for the teaching profession at grammar schools (Computer Science and Physics) in 2016. Afterwards I graduated in 2018 with my Master's Degree in Computer Sciences. In May 2018 I started my PhD in machine learning at the Data Mining and Information Retrieval Group.
I'm currently working on the applications of machine learning in the we4bee project.
Teaching
Summer Term 2018:
- Interactive Artificial Intelligence
Winter Term 2018/19:
Summer Term 2019:
Summer Term 2020:
Winter Term 2020/21:
Summer Term 2021:
Winter Term 2021/22:
Winter Term 2022/23:
Publications
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“Semi-unsupervised Learning for Time Series Classification”, Milets@KDD, available: https://doi.org/10.48550/ARXIV.2207.03119.(2022)
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A Sensor-Driven Approach for Analyzing a beehive’s State [online], available: https://sfe2gfomeeting.sciencesconf.org.(2022)
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“Anomaly Detection in Beehives: An Algorithm Comparison”, in Ahrens, A., Prasad, R., Benavente-Peces, C. and Ansari, N., eds., International Conference on Sensor Networks, Communications in Computer and Information Science, Springer, 1–20, available: https://doi.org/https://doi.org/10.1007/978-3-031-17718-7_1.(2021)
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“Semi-unsupervised Learning: An In-depth Parameter Analysis”, in Edelkamp, S., M{\"o}ller, R. and Rueckert, E., eds., KI 2021: Advances in Artificial Intelligence, Cham: Springer International Publishing, 51–66, available: https://link.springer.com/chapter/10.1007/978-3-030-87626-5_5.(2021)
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“Density-based weighting for imbalanced regression”, Machine Learning, available: https://doi.org/10.1007/s10994-021-06023-5.(2021)
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“OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning”, Atmospheric Environment, 233, 117535, available: https://doi.org/https://doi.org/10.1016/j.atmosenv.2020.117535.(2020)
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“Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study”, Sensors, 20(9), available: https://doi.org/10.3390/s20092637.(2020)
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“Anomaly Detection in Beehives using Deep Recurrent Autoencoders”, in Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), SCITEPRESS – Science and Technology Publications, Lda., 142–149.(2020)
Other scientific activities
- PC Member ECML/PKDD 2021 and 2022
- Subreviewer for several conferences and journals