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Machine Learning and Artificial Intelligence Lab


New book published:

Tiansi Dong: A Geometric Approach to the Unification of Symbolic Structures and Neural Networks.

Studies in Computational Intelligence 910, Springer


New paper out:

Florian Seiffarth, Tamás Horváth, and Stefan Wrobel: Maximum Margin Separations in Finite Closure Systems at ECMLPKDD'20


New Paper out:

Pascal Welke, Florian Seiffarth, Michael Kamp, and Stefan Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs
at KDD-2020, the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.


New book published:

Pascal Welke: Efficient Frequent Subtree Mining Beyond Forests.

Dissertations in Artificial Intelligence 348, IOS Press


Best Paper at ICANN 2019:

Christian Bauckhage, Rafet Sifa, and Tiansi Dong: Prototypes within Minimum Enclosing Balls.


Best Paper at EuroVA 2019: Siming Chen, Gennady Andrienko, Natalia Andrienko, Christos Doulkeridis and Athanasios Koumparos: Contextualized Analysis of Movement Events



New Paper Out:

Tiansi Dong, Christian Bauckhage, Hailong Jin, Juanzi Li, Olaf Cremers, Daniel Speicher, Armin Cremers, Joerg Zimmermann (2019). Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction at ICLR-19 The Seventh International Conference on Learning Representations


New Paper Out:

Tiansi Dong, Zhigang Wang, Juanzi Li, Christian Bauckhage, Armin B. Cremers (2019). Triple Classification Using Regions and Fine-Grained Entity Typing at AAAI-19 The Thirty-Third AAAI Conference on Artificial Intelligence

Welcome to the Machine Learning and Artificial Intelligence Lab. Our group is part of the Chair of Intelligent Analysis and Information Systems (Prof. Dr. Stefan Wrobel) and jointly led by Prof. Dr. Stefan Wrobel and Prof. Dr. Christian Bauckhage.

Our group focuses on the neighboring subfields of computer science known as machine learning (ML), artificial intelligence (AI), and knowledge discovery in databases (KDD, sometimes referred to simply as data mining).  For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making.  On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.

Given the enormous growth of collected and available data in companies, industry and science, techniques for analyzing such data are becoming ever more important.  Today, data to be analyzed are no longer restricted to sensor data and classical databases, but more and more include textual documents and webpages (text mining, Web mining), spatial data, multimedia data, relational data (molecules, social networks).

Research in knowledge discovery and machine learning combines classical questions of computer science (efficient algorithms, software systems, databases) with elements from artificial intelligence and statistics up to user oriented issues (visualization, interactive mining). In our work, we strive to combine theoretical and technical advances in research with real-world applications to show that things really work.

Our group is part of the Chair of Intelligent Analysis and Information Systems (Prof. Dr. Stefan Wrobel) and thus carried by two institutions, namely the computer science department of the University of Bonn, where we are part of Informatik III, and Fraunhofer IAIS, the Fraunhofer Institute for intelligent analysis and information systems, where Prof. Wrobel is also director. 

To find out about our group members, research, publications and teaching, please click on the menu items in the menu on top. If you are interested in working with us as a PhD student or postdoc, please send an email with the usual material and a brief statement of your research interest to Martina Doelp.

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