The Leibniz Information Centre for Science and Technology (TIB) is a public-law foundation of the Federal State of Lower Saxony. TIB is a member of the Leibniz Association and it assumed responsibility for operating the German National Library for technology, architecture, chemistry, information technology, mathematics, and physics. It is a national infrastructure facility for the provision of scientific information whose national tasks lend it importance for the country as a whole. The TIB’s research and development department conducts independent research to develop novel and innovative library services.


Prof. Dr. Sören Auer is the Director of TIB and full professor at the Leibniz Universität Hannover since July of 2017, until then he has been leading the “Enterprise Information Systems (EIS)” Department at Fraunhofer Institute for Intelligent Analysis and information Systems and was professor at the University of Bonn. His research interests include social and semantic web technologies, knowledge representation engineering and management, usability, agile methodologies as well as databases and information systems.
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Prof. Dr. Ralph Ewerth is head of the Visual Analytics Research Group at TIB and professor at the Leibniz Universität Hannover since 2015. The Research Group addresses topics such as multimedia retrieval, automatic annotation of large image and video archives, semantic cross-modal relations, and particularly investigates adaptive deep learning systems for these tasks.  Ralph Ewerth is researching methods for image and video analysis, recent works include articles on the investigation of cross-modal image-text relations, deep learning for image and adaptive video classification, similarity search and semantic hashing, or multimodal video concept classification.
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Dr. Anett Hoppe is a postdoctoral researcher in the Visual Analytics Research Group at TIB. She holds a PhD of Computer Science obtained at Université Bourgogne Franche-Comté (Dijon, France) in 2016. Her research interests revolve around human learning in digital environments and digital libraries with focus on search as learning, inclusive learning environments, ethical and transparent data usage.
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Dr. Gábor Kismihók

is the head of the Learning and Skills Analytics Research Group at TIB. He concentrates his research efforts on matching processes between education, labour market, and individuals. Previously he co-founded the Center of Job Knowledge Research at the University of Amsterdam, and supervised there 5 PhD students in the fields of HRM – data science and learning analytics. He is also a regular supervisor of MSc and BSc student theses. He published his research in various peer-reviewed international journals and book chapters in the fields of Learning Analytics, Technology Enhanced Learning and Knowledge Management. In the past years he has been busy with various EU funded research projects (FP7, FP7 Marie Curie ITN, Lifelong Learning Programme) focused on employability, person – organization fit, mobile Learning Management System development, context aware educational systems and semantic technology in education. Besides scientific research, he has been managing large scale innovation networks (e.g. www.eduworks-network.eu) with a budget of 3,7M EUR (working staff of 25-30 people) and smaller scale innovation projects (e.g. www.ontohr.eu) with a budget of 500K EUR (working staff of 10-15 people). He has also been busy writing successful research project proposals (FP7, FP7 MC ITN, EU LLP, TAMOP). He is a member of expert panels reviewing proposals for EU funding.

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Christian Otto (M.Sc.) is a PhD student in Ralph Ewerth’s research group. His research topic is the examination of cross-modal interrelations between visual and textual information. This includes the consideration of insights from communication sciences, the research design of deep learning applications to analyze multimodal information and the incorporation of this knowledge into search engines, such as for scientific publications or scientific videos. He further investigates different methods to improve the usability of the TIB AV-Portal (https://av.tib.eu/) with, for instance, video recommender systems or content visualization techniques.
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