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Christian BeecksView profile

Professor

• Machine Learning
• Data Mining
• Knowledge Discovery
• +7 more

Univ.-Prof. Dr. Christian Beecks is a full Professor at the Faculty of Mathematics and Computer Science, FernUniversity in Hagen, and heads the Intelligent Data Analysis research group at the Fraunhofer Institute for Applied Information Technology FIT. His work bridges theoretical advancements in machine learning with practical applications across industry and biomedicine. Education: PhD in Computer Science (RWTH Aachen University, 2007-2013) Diploma in Computer Science (RWTH Aachen University, 2001-2007) As a leading figure in data science, Beecks specializes in machine learning and big data analytics , focusing on scalable algorithms for complex data spaces. His research has produced over 100 peer-reviewed publications and notable contributions in time series analysis , clustering methods , and IoT data processing . Recent work explores Gaussian process modeling for anomaly detection and spatiotemporal signal analysis. His publications demonstrate expertise in automated pattern discovery and interpretable AI systems . Key themes include: clustering validation , time series representation , industrial applications , and knowledge ontologies . Technically, his team leverages Ptolemaic geometry , skyline queries , and component mining for real-world data challenges. 2021 SIAM Best Research Paper Award 2018 Warwick Workshop Best Poster Award 2015 & 2011 Best Paper Awards Through leadership roles at FernUniversity and Fraunhofer FIT, Beecks drives initiatives in competency-based education and AI workforce empowerment . His research groups develop frameworks for edge-to-cloud AI orchestration and automated model inference , with applications in manufacturing, biomedicine, and digital humanities. Current projects focus on data pooling, zero-touch orchestration, and educational AI tools like virtual tutors.

• Legal Psychology
• Eyewitness Testimony
• Statement Analysis
• +3 more

Dr. Ulrike Stühler is a Research Associate at Philipps University of Marburg in the Department of Personality and Diagnostics, working within the Psychological Assessment with Focus on Legal Psychology group (AG Suchotzki). She holds an M.Sc. in Legal Psychology from Psychologische Hochschule Berlin and a Ph.D. in General Psychology and Cognitive Neuroscience from Friedrich-Schiller-University Jena. 2024–present: Postdoctoral Researcher at Philipps University of Marburg 2020–2024: Doctoral Researcher at Friedrich-Schiller-University Jena 2016–2019: M.Sc. Psychology at Friedrich-Schiller-University Jena 2012–2015: B.Sc. Psychology at Technische Universität Braunschweig Her research focuses on legal psychology , particularly eyewitness testimony and statement analysis , with innovative work on body-worn cameras and virtual reality (VR) applications in eyewitness research. She developed the Jena Eyewitness Research Stimuli (JERS) database featuring mock theft scenarios in multiple formats. Recent publications analyze extralegal influences on legal judgments , de-escalation effects of police body cameras , and multimedia lineup identification methods. These works span forensic psychology, cognitive neuroscience, and criminal justice technology. Scientific Awards : Landesgraduiertenstipendium der Friedrich-Schiller-Universität Jena (2020–2024, ca. 67.000€)

Mareike SchumacherView profile

Assistant Professor

• Digital Humanities
• Computational Literary Studies
• Narrative Theory
• +5 more

Mareike Schumacher is a Junior Professor for Digital Humanities at the University of Regensburg since April 2023, affiliated with the Faculty of Languages, Literature and Cultural Studies and the Institute for Information and Media, Language and Culture. Previously, she served as a research assistant at the Technical University of Darmstadt, where she coordinated the forTEXT project – Digital Literature Research. Education: Doctorate in Modern German Literature (specializing in Digital Humanities) from University of Hamburg (2021), awarded summa cum laude MA in German-language literature from University of Hamburg (2010-2013) BA in Cultural Studies and Business Psychology from Leuphana University Lüneburg (2007-2010) Her research spans literary and cultural studies, narrative theory, and computational literary studies, with particular emphasis on gender representation in literature and digital methodology development. She actively works to strengthen the image of female scientists and digital humanities in public discourse through science communication. Her current projects include analyzing gender-specific representations of literary figures, developing a diversity corpus (Disco), and exploring sustainability discourses. Analysis of her recent publications reveals a clear trajectory toward increasingly sophisticated computational approaches to traditional literary questions, particularly in gender analysis and spatial representation in texts. Her work consistently bridges theoretical literary scholarship with practical digital tool development, demonstrating a commitment to both theoretical advancement and community-oriented application of digital methods. As a co-founder and co-host of the RaDiHum20 podcast, she actively contributes to building the digital humanities community. She has coordinated projects focused on digital literature research and received funding from the German National Library for her work on the Disco diversity corpus. Through her involvement with forTEXT, she has developed numerous educational materials and workshops introducing digital methods to humanities scholars. Mareike Schumacher maintains active research teams focused on gender analysis in literature (m*w project), digital tool development, and science communication. Her work with the RaDiHum20 podcast creates a platform for dialogue across the digital humanities community, from students to established professors, fostering connections and knowledge exchange within the field.

• Bibliographic Data Modelling
• Metadata
• Multimedia Franchises
• +3 more

Dr. Senan Kiryakos is a Research Fellow at the University of Tsukuba, specializing in bibliographic data modeling, metadata frameworks, and multimedia franchises. His work focuses on developing ontologies for Japanese visual media, integrating fan-created data, and enhancing access to cultural content through innovative metadata systems. Key research includes the Japanese Visual Media Graph project, which aggregates data on anime, visual novels, and manga. He collaborates extensively with institutions like Tsukuba to explore metadata-centric digital archives and RDF applications. Publications emphasize ontology design, heterogeneous data integration, and frameworks for multimedia franchises. His research bridges library science, computer science, and digital humanities to address challenges in cultural content preservation and access. Notable contributions include conceptualizing superworks as unified entities and leveraging fan-generated data for academic research. His work highlights the role of metadata in organizing digital cultural heritage and improving access to complex media franchises.

Felix Alexander GersView profile

Professor

• Artificial Intelligence
• Deep Learning
• Neural Networks
• +6 more

Prof. Felix Alexander Gers is a Professor of Multimedia Development at the Berlin University of Technology’s Department of Computer Science and Media since 2005. He holds a PhD from EPFL (Switzerland) in AI systems, focusing on Recurrent Neural Networks with LSTM. His expertise spans Artificial Intelligence, Deep Learning, NLP, and Game-Based Learning (GBL). He pioneered the CoDi algorithm (Cellular Automata-based neural modeling) during his work at ATR (Japan) and received ATR’s Best Patent Award. His research includes virtual microbiology laboratories, dialogue systems for avatars, and neural network applications in drug development and 3D data classification. He co-leads the Data Science Group at BHT and has been a fellow in the “Excellence in Teaching” project since 2011, emphasizing virtual lab simulations. Notable projects include Brain Bots (NLP-driven lab assistants) and GBL in microbiology. Education: Diploma in Physics, Leibniz University Hannover PhD in AI Systems (LSTM), EPFL (Switzerland) Research Interests: Neural Networks (RNN/LSTM) Natural Language Processing Virtual Laboratories and GBL Industrial AI Applications Key Projects: Brain Bots: Voice-enabled lab assistants in virtual microbiology labs CoDi Algorithm: CA-based neural structure simulation GBL in the Lab: Game-based microbiology experiments Awards: ATR Best Patent Award (CoDi) Labs/Teams: Member of the Data Science Group and Online Learning Lab at BHT.

Angela SchreyerView profile

Professor

• Content Management
• Document Management
• Information Systems
• +4 more

Prof. Dr. Angela Schreyer is a Professor of Content Management at the Department of Information Science, University of Applied Sciences Potsdam. She holds a doctoral degree in Arabic Studies from the University of Leipzig and has extensive training in scientific documentation and computer science. Her academic roles include Deputy Chair of the Audit Committee and participation in the DIN-NID working group on Records Management and the Brandenburgische Arbeitsgemeinschaft Information (BRAGI). Her research focuses on content management systems (CMS), digital asset management, records management, and document lifecycle processes. She has published extensively on topics like ISO-compliant appraisal frameworks, digital archives, and interdisciplinary applications of photographic archives. Her teaching spans Information Systems, Records Management, and Digital Asset Management across bachelor’s and master’s programs. Prof. Schreyer has supervised over 100 theses since 2004, covering areas like digital heritage preservation, contract management systems, and accessibility standards. She has contributed to projects such as the Philipp Holzmann AG image archive and the development of Typo3-based CMS at her institution. Her work bridges technical systems, archival practices, and educational innovation in information science. Education: University of Leipzig (Certified Interpreter), TU Dresden (Computer Science) Professional Training: Advanced documentation studies at FH Potsdam Key publications include works on risk-oriented records management (2021), curriculum redesign (2016), and digital archive systems (2010). Her research emphasizes practical applications of information systems in public and corporate contexts.

• Deep Learning
• Multimedia Data Analysis
• Artificial Intelligence
• +2 more

Hicham Bellafkir is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Mathematics and Computer Science (Fb12) and the Distributed Systems group (AG Freisleben). He initiated his PhD in 2021 under Prof. Bernd Freisleben's supervision, focusing on deep neural networks and the analysis of multimedia data. His work integrates advanced machine learning techniques with applications in biodiversity monitoring, wildlife data analysis, and sensor networks. Current research interests include leveraging neural networks for image and audio recognition in ecological contexts. Contact: Office 04C02 (H|04 Institutsgebäude), reachable at hicham.bellafkir@uni-marburg.de . Research contributions span bioacoustics, camera trap image processing, and disruption-tolerant networking, with publications emphasizing interdisciplinary applications of AI in environmental science. Education: M.Sc. in Computer Science (exact degree not specified) Key Projects: Nature 4.0 sensor systems, QUICL networking protocols, bird species recognition frameworks Labs/Teams: AG Freisleben's Distributed Systems Group

Paras MehtaView profile

Researcher

• Spatial Data Mining
• Geoinformatics
• Information Retrieval
• +3 more

Dr. Paras Mehta is a Researcher at the Department of Databases and Information Systems, part of the Computer Science faculty at Freie Universität Berlin. His work focuses on spatial data mining, geoinformatics, and spatio-temporal analysis with applications in disaster response systems and social media analytics. He contributes to projects such as MoveSafe and CliniScale, addressing challenges in real-time data processing and location-based services. Research interests include geotagged post analysis, spatio-textual data retrieval, and distributed computing for large-scale spatial datasets. His recent work emphasizes continuous summarization of streaming data and hotspot detection using frameworks like Apache Spark. Notable projects involve developing systems like μTOP for trending topic detection in microblogs and LocXplore for urban region profiling. His publications span spatial keyword queries, trajectory aggregation, and disaster response frameworks. Paras Mehta holds a PhD and has contributed to both academic and applied research in database systems. Despite no listed awards or grants in the provided texts, his research portfolio demonstrates strong engagement with spatial computing challenges. He collaborates within the Databases and Information Systems group at Freie Universität Berlin, contributing to both theoretical advancements and practical software solutions.

• Semantic Computing
• Multimedia Recommender Systems
• Video Surveillance
• +5 more

Fabio Persia is a researcher affiliated with the University of Naples Federico II , with a focus on Semantic Computing , Multimedia Recommender Systems , and Event Detection in Video Surveillance . His work spans interdisciplinary applications in Healthcare , Robotics , and Social Network Analysis , often collaborating with co-researchers like Daniela D'Auria, Mouzhi Ge, and Giovanni Pilato. His research interests include leveraging Artificial Intelligence and Complex Event Processing to enhance patient monitoring and healthcare systems . He has contributed to frameworks for social sensing , surveillance , and personalized recommendations . Publications highlight his expertise in semantic indexing , multimedia analysis , and intelligent systems . Key works involve AI-driven patient monitoring , risk-aware route planning , and social network behavior modeling . As a co-author in numerous journals and conferences, he has co-edited special issues in the International Journal on Semantic Computing and contributed to Clinical and Collaborative Robotics advancements. His current role remains active, with recent 2025 publications indicating ongoing research.

• Computer Science
• Machine Learning
• Image Processing
• +5 more

Oge Marques is a Professor in Computer Science with expertise in machine learning, image processing, and multimedia systems. Their research focuses on applying computer vision to medical diagnostics, biometric recognition, and healthcare data analysis. Key research areas include: Deep learning for skin cancer and tuberculosis diagnosis Bias assessment in medical AI models Biometric systems for fingerprint and iris recognition Wearable healthcare technologies 3D video coding and autostereoscopic displays Marques has contributed to 15+ journal articles and book chapters since 2002, collaborating with researchers in Austria, India, and the US. Their work appears in journals like Multimedia Tools and Applications and Journal of Digital Imaging , covering topics from spiking neural networks to compound figure separation in scientific publications.

• Database Systems
• Heterogeneous Computing
• GPU Acceleration
• +4 more

David Broneske is a Researcher at the Otto von Guericke University of Magdeburg , Germany. His work spans Database Systems , Heterogeneous Computing , and Machine Learning Applications , with a focus on GPU/FPGA Acceleration and Non-volatile Memory (NVM) Optimization . He has contributed to projects like ADAMANT (co-processor integration) and GridTables (H2TAP data stores). Key Research Areas : Database acceleration via specialized hardware, Graph database applications in clinical/biological domains, and AutoML for domain-aware model selection. Collaborations : Frequent co-author with Gunter Saake, Bala Gurumurthy, and Sajad Karim on topics like NVM Storage and GPU-based Query Execution . Publications : Over 105 papers (2012–2025) covering Protein Identification Systems , Entity Resolution , and Software Evolution Datasets . Workshops : Co-organized the Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC) and contributed to standards like Backlogs/Interval Timestamps for temporal graph queries.

Michael HoedingView profile

Professor

• Databases
• Electronic Business
• Data Warehousing
• +10 more

Michael Hoeding is a Professor at the Brandenburg University of Technology in the Department of Economics . With expertise in network-based applications for commerce and electronic business, his work bridges databases, telecommunications, and enterprise systems. Specialization in database integration and legacy system modernization Key contributions to service-oriented architectures , XML , and SAP ERP optimization Focus on data warehousing (SAP-BI/SAP-BW) and wiki technology for knowledge management His research explores multimedia techniques in training and marketing, Oracle administration , and flash storage applications in database systems. He received the Forschungspreis (research award) from Otto-von-Guericke University Magdeburg in 1998. Notable recent work includes mobile learning applications , single-page website design , and Android development frameworks . His publications span topics from context-based querying to cooperative information systems , reflecting 25+ years of interdisciplinary research. Scientific Awards : Forschungspreis (1998) Grants : Research funding through Fraunhofer Institute participation Teaching : Courses in database systems , software engineering , and mobile applications

Rainer LienhartView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Rainer Lienhart is a tenured Professor at the University of Augsburg, where he holds the Chair for Machine Learning & Computer Vision within the Faculty of Applied Computer Science's Institute of Computer Science. He has served as the executive director of the Institute for Computer Science since April 2010. His research group, the Machine Learning and Computer Vision Lab (MLCV Lab), focuses on large-scale image, video, human pose, sensor and data mining algorithms; object, human pose and action detection/recognition; and image, video, human pose and action retrieval. Dr. Lienhart received his PhD in Computer Science from the University of Mannheim, Germany in 1998. Prior to joining the University of Augsburg, he worked as a Staff Researcher at Intel's Microprocessor Research Lab in Santa Clara, California from August 1998 to July 2004. He has also taken sabbaticals at FXPAL (August 2017 to March 2018) and Willow Garage (2009). His research spans multiple areas of computer vision and machine learning with particular emphasis on object detection/recognition, human pose estimation, image/video captioning, automated visual inspection, medical image analysis, deep learning, statistical computing, and explainable AI. His work on multimedia data mining and signal processing has led to significant contributions in video content analysis, including text detection/recognition, commercial detection, face detection, shot and scene detection, and automatic video abstraction. The publication record shows a consistent research trajectory focusing on human pose estimation, winter sports analysis, scene graph generation, and segmentation techniques, with recent work emphasizing transformer-based architectures, 3D pose estimation, and physics-informed computer vision approaches. His research demonstrates a strong interdisciplinary focus, connecting computer vision with sports science, medical applications, and multimedia content analysis. Prof. Lienhart has served as general co-chair of ACM Multimedia 2017 and 2007, as well as SPIE Storage and Retrieval of Media Databases 2004 and 2005. He has served on editorial boards of three international journals and was vice chair of SIGMM from July 2009 to June 2017. His professional service includes committee membership for major conferences including ACM Multimedia, IEEE ICME, and SPIE Storage and Retrieval of Media Databases. His laboratory, the MLCV Lab, maintains an active research program with numerous collaborators and students working on cutting-edge computer vision problems. Current research directions include human pose estimation in sports contexts, winter sports equipment segmentation, scene graph generation, and medical image analysis applications.

• Multimedia Databases
• Workflow Systems
• Business Intelligence
• +3 more

Prof. Dr.-Ing. Robert Müller is a faculty member at the Faculty of Computer Science and Media of Leipzig University of Applied Sciences (HTWK Leipzig). His expertise lies in Multimedia Databases , with a focus on advanced data modeling and workflow integration techniques. Collaborated on industry projects like FoodBroker for synthetic datasets in business analytics Pioneered graph-grammar approaches for medical context representation Developed rule-based workflow systems for healthcare domains His research spans Workflow Systems , Knowledge-Based Modeling , and Graph-Based Analytics , as evidenced by publications in both computer science and health informatics. He actively contributes to academic projects through his technical leadership in database and process engineering.

Ludger BeckerView profile

Researcher

• Database Systems
• Geographic Information Systems
• Spatio-Temporal Data Management
• +3 more

Ludger Becker is an Academic Director at the University of Münster, where he has been working since December 2003. He currently serves as Head of the Information Security Unit since June 2023 and as CISO of the University of Münster since April 2023. Previously, he was Head of IVV5 from January 1998 to December 2023 and held positions as Senior Academic Councillor and Academic Councillor at the Institute of Computer Science at the University of Münster. Dr. Becker received his Doctor of Engineering (Computer Science) from the University of Siegen in July 1992 and completed his Diploma in Computer Science from the University of Dortmund in April 1989. His research spans several key areas in computer science, with particular expertise in database systems , geographic information systems , and software engineering . Dr. Becker has made significant contributions to spatio-temporal data management, developing frameworks for representing moving objects and efficient index structures for spatial queries. More recently, his work has expanded into health informatics, particularly in clinical data generation and literature database analysis. His teaching focuses on software engineering principles, databases, and object-oriented programming, as evidenced by his leadership of the Software Internship course. Dr. Becker's publication record shows a clear evolution from foundational work in spatial databases and GIS in the 1990s and early 2000s, through educational technology applications in the early 2000s, to more recent contributions in health informatics. His research consistently demonstrates expertise in data management challenges across multiple domains. Dr. Becker regularly teaches Software Internship, Databases, and Advanced Database Concepts courses at the University of Münster. His teaching materials show particular attention to software design processes, object-oriented principles, and modern development tools and frameworks, including detailed documentation on implementing @pre, @post, and @inv tags in Javadoc for specifying software contracts. As Head of the Information Security Unit and CISO, Dr. Becker oversees critical information security infrastructure and policies for the University of Münster, applying his technical expertise to protect academic data and systems across the institution's extensive network.