AI Colloquium
The AI Colloquium is a series of lectures dedicated to cutting-edge research in the field of machine learning and artificial intelligence, coorganized by the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr Institute), the Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas).
Programme
Distinguished researchers deliver captivating lectures followed by vibrant discussions. However, unlike traditional colloquia, the AI Colloquium prioritizes interactive dialogue, fostering international collaboration. Conducted primarily in English, these 90-minute sessions feature hour-long lectures and 30-minute Q&A sessions. Join every Thursday at 10 AM c.t. for a stimulating exploration of cutting-edge topics. Whether in-person at our Lecture Room on Fraunhofer Strasse 25 or via Zoom, our hybrid format ensures accessibility for all.
| Day (usually) | Thursday |
| Start and end time | 10 AM c.t. - 12 AM |
| Duration of Presentation | 60 Minutes |
| Location (usually) | Lecture Room 303 3. Floor Fraunhofer Strasse 25 Dortmund |
Upcomming Events
Recent advances in Concept Learning
- Lamarr
Abstract: This talk discusses the Lamarr project WHALE, which aims to advance neuro-symbolic concept learning at web scale. We begin with an overview of foundational principles in concept learning. Then, we examine limitations of existing systems—such as scalability challenges, adaptability issues, and vulnerability to noisy data. To address these gaps, we introduce innovations that improve runtime efficiency and accuracy by integrating tensor-based models and embedding techniques. These methods bridge neural flexibility and symbolic precision, enabling robust performance even with imperfect data. WHALE represents a significant step toward scalable, reliable concept learning at Web scale.
Prof. Axel-Cyrille Ngonga Ngomo
Bio: Prof. Dr. Ngonga studied Computer Science and Physics at Leipzig University. In his doctoral studies, he developed unsupervised and weakly supervised methods for the extraction of ontologies from large text corpora. His work was granted the best student paper award at CICLing 2008. His habilitation focused on machine learning and rapid execution approaches for data integration. He then led the Agile Knowledge Engineering and Semantic Web group at Leipzig University, where he performed research on various topics related to the lifecycle of knowledge graphs. Since 2017, he is a full professor (W3) of Data Science at Paderborn University, where he is also the director of the Joint Artificial Intelligence Institute and of the Computer Science Computing Facility. He has received over 30 international awards-including 6 best paper awards and a Next Einstein Fellowship-for works on knowledge extraction, fact checking, benchmarking, storage, and machine learning on knowledge graphs. Amongst others, he is one of the first two Lamarr fellows. Prof. Ngonga is currently a PI in over 15 national and international research projects and the coordinator of the doctoral training network LEMUR on learning with multiple representations.
Past Events
Recent advances in Concept Learning
- Lamarr
Abstract: This talk discusses the Lamarr project WHALE, which aims to advance neuro-symbolic concept learning at web scale. We begin with an overview of foundational principles in concept learning. Then, we examine limitations of existing systems—such as scalability challenges, adaptability issues, and vulnerability to noisy data. To address these gaps, we introduce innovations that improve runtime efficiency and accuracy by integrating tensor-based models and embedding techniques. These methods bridge neural flexibility and symbolic precision, enabling robust performance even with imperfect data. WHALE represents a significant step toward scalable, reliable concept learning at Web scale.
Prof. Axel-Cyrille Ngonga Ngomo
Bio: Prof. Dr. Ngonga studied Computer Science and Physics at Leipzig University. In his doctoral studies, he developed unsupervised and weakly supervised methods for the extraction of ontologies from large text corpora. His work was granted the best student paper award at CICLing 2008. His habilitation focused on machine learning and rapid execution approaches for data integration. He then led the Agile Knowledge Engineering and Semantic Web group at Leipzig University, where he performed research on various topics related to the lifecycle of knowledge graphs. Since 2017, he is a full professor (W3) of Data Science at Paderborn University, where he is also the director of the Joint Artificial Intelligence Institute and of the Computer Science Computing Facility. He has received over 30 international awards-including 6 best paper awards and a Next Einstein Fellowship-for works on knowledge extraction, fact checking, benchmarking, storage, and machine learning on knowledge graphs. Amongst others, he is one of the first two Lamarr fellows. Prof. Ngonga is currently a PI in over 15 national and international research projects and the coordinator of the doctoral training network LEMUR on learning with multiple representations.
Recent advances in Concept Learning
- Lamarr
Abstract: This talk discusses the Lamarr project WHALE, which aims to advance neuro-symbolic concept learning at web scale. We begin with an overview of foundational principles in concept learning. Then, we examine limitations of existing systems—such as scalability challenges, adaptability issues, and vulnerability to noisy data. To address these gaps, we introduce innovations that improve runtime efficiency and accuracy by integrating tensor-based models and embedding techniques. These methods bridge neural flexibility and symbolic precision, enabling robust performance even with imperfect data. WHALE represents a significant step toward scalable, reliable concept learning at Web scale.
Prof. Axel-Cyrille Ngonga Ngomo
Bio: Prof. Dr. Ngonga studied Computer Science and Physics at Leipzig University. In his doctoral studies, he developed unsupervised and weakly supervised methods for the extraction of ontologies from large text corpora. His work was granted the best student paper award at CICLing 2008. His habilitation focused on machine learning and rapid execution approaches for data integration. He then led the Agile Knowledge Engineering and Semantic Web group at Leipzig University, where he performed research on various topics related to the lifecycle of knowledge graphs. Since 2017, he is a full professor (W3) of Data Science at Paderborn University, where he is also the director of the Joint Artificial Intelligence Institute and of the Computer Science Computing Facility. He has received over 30 international awards-including 6 best paper awards and a Next Einstein Fellowship-for works on knowledge extraction, fact checking, benchmarking, storage, and machine learning on knowledge graphs. Amongst others, he is one of the first two Lamarr fellows. Prof. Ngonga is currently a PI in over 15 national and international research projects and the coordinator of the doctoral training network LEMUR on learning with multiple representations.