Presenters
Prof. Anthony Bagnall
University of Southampton, United Kingdom
Tony is a Professor in Computer Science at the University of Southampton. His primary research interest is in developing algorithms for time series machine learning, including classification, regression and clustering, and finding novel areas of application. He is currently PI on a three year EPSRC grant called aeon: a toolkit for machine learning with time series that is supporting this tutorial. He has ongoing research collaborations with scientists on four continents and a track record of consultancy with industrial partners. He is an action editor for ECML 2024, is on the editorial board for the Data Mining and Knowledge Discovery journal and regularly reviews for data mining conferences such as KDD, ICDM and SDM. He helps maintain the time series classification archive that has been used in thousands of publications.
Dr. Matthew Middlehurst
University of Southampton, United Kingdom
Matthew is a senior postdoctoral researcher on the ESPRC aeon grant and is a core developer for the aeon toolkit. His research interests are time series classification and regression, with a keen interest in developing open source software for the field and promoting reproducibility. His group has developed the HIVE-COTE family algorithms for time series classification, which have a good claim to represent state-of-the-art in the field.
Ali Ismail-Fawaz
Université de Haute-Alsace, France
Ali is a PhD student supervised by Germain Forestier, Jonathan Weber, Maxime Devanne and Stefano Berretti, stationed at the IRIMAS institute of the University of Haute-Alsace in France. He is a core developer for the aeon toolkit and is leading the deep learning part of the package with a recent kick off for a deep clustering module. His main research topic is focused on time series analysis, with a speciality in deep learning. His work has addressed different tasks such as classification and clustering.
Dr. Antoine Guillaume
Novahé and Constellation, France
Antoine is a R&D manager at Novahé and Constellation. He is currently studying explainability, uncertainty quantification and carbon footprint reduction in the context of ML products. He specializes in time series machine learning and similarity search with an emphasis on rare event prediction, and works on applications in industry 4.0 and IT systems.
Arik Ermshaus
Humboldt University of Berlin, Germany
Arik is a research associate and PhD student at Humboldt University of Berlin. His field of research is unsupervised TS data mining with a focus on segmentation.
Dr. Patrick Schäfer
Humboldt University of Berlin, Germany
Patrick is a postdoctoral researcher and lecturer of computer science at Humboldt University of Berlin. His main research interests include scalable TS analytics, such as classification, motif discovery and segmentation.
Sebastian Schmidl
Hasso Plattner Institute, University of Potsdam, Germany
Sebastian is a PhD student affiliated with the research groups of Thorsten Papenbrock (Philipps-University of Marburg) and Felix Naumann (Hasso Plattner Institute). His field of research is the development of efficient algorithms for the detection of anomalies in time series data and for the discovery of data dependencies in relational data.
Contributors
Dr. David Guijo-Rubio
University of Alcalá, Spain
David is a postdoctoral researcher at the University of Alcal ́a, Spain. He is working on time series extrinsic regression and clustering, and is a core developer for the aeon toolkit. His research interests are time series machine learning, explainability, evolutionary artificial neural networks, and ordinal classification. He is also interested in the applications of such techniques to real-world problems related to biomedicine and renewable energies.
Prof. Germain Forestier
Université de Haute-Alsace, France
Germain is a university professor at the University of Haute-Alsace in Mulhouse France. He is part of the IRIMAS research institute, for which he is head of the computer science department. His research is mainly oriented around time series analysis, with a speciality in deep learning models. He is co-author of the deep learning review for time series classification for which the tutorial will be based on mostly, as well as new papers which he is co-authored as well. His contributions to the deep learning community in time series classification have been significant recently.
Prof. Thorsten Papenbrock
Philipps-University of Marburg, Germany
Thorsten is a professor for big data analytics at the Philipps-University of Marburg. In his research, he studies novel approaches for data cleaning, data profiling, time series analytics, and database systems. His research activities focus on a more environmentally friendly and resource-saving use of information technology in data-intensive applications
Phillip Wenig
Hasso Plattner Institute, University of Potsdam, Germany
Phillip is a PhD student affiliated with the research groups of Thorsten Papenbrock (Philipps-University of Marburg) and Felix Naumann (Hasso Plattner Institute). His main research interests are time series analytics (with a focus on clustering and anomaly detection) and distributed machine learning.