Torsten Ueckerdt is an Interim Professor in the Algorithmics I group at the Institute of Theoretical Informatics, Karlsruher Institut für Technologie (KIT). He has held academic positions since 2012, including postdoctoral roles and habilitation in Discrete Mathematics. His research focuses on combinatorial objects like graphs, posets, and hypergraphs in geometric settings, exploring structural properties, colorings, and geometric representations. Holds a PhD from TU Berlin (2011) and habilitation from KIT (2017). Professional service includes editorial roles (Annals of Combinatorics) and program committees for conferences like Graph Drawing, SoCG, and EuroCG. Supervised numerous students across Bachelor's, Master's, and diploma theses. Research interests span structural graph theory, geometric graph theory, Ramsey theory, discrete geometry, and combinatorial games. Active in graph drawing and algorithm design, with contributions to queue layouts, graph representations, and optimization problems like cartograms and wind farm cabling.
Dr. Jae Hee Lee is a postdoctoral researcher in the Knowledge Technology Group at the University of Hamburg, holding a PhD in Computer Science from the University of Bremen. His research focuses on multimodal language models, explainable AI, and neuro-symbolic integration to enhance model robustness and generalization. Previously, he specialized in spatio-temporal reasoning and multiagent systems, supported by grants like the Feodor Lynen Fellowship (2016–2017). He is an associate editor of AI Communications and co-organizes the International Workshop on Spatio-Temporal Reasoning and Learning. His recent work includes projects like LUMO (DFG-funded, 2025–2029), exploring lifelong multimodal learning through compositional knowledge. Lee advises multiple students, including Björn Plüster, developer of the LeoLM German LLM. Key research interests span explainable vision-language models, causal reinforcement learning, and concept-based explanations. He frequently serves on program committees for AI/NLP conferences (e.g., IJCAI, COLING) and organizes reading groups on LLM/XAI topics.
Yang Cao is a Professor at the University of Science and Technology of China , Department of Automation, Hefei, China. He holds a PhD from Northeastern University (2004, Shenyang, China) and has active affiliations with institutions like Virginia Tech and Huazhong University of Science and Technology. Research Focus: Spatiotemporal modeling, event-based vision, 3D human-object interaction, and industrial defect detection. Publications: 15 recent articles highlight his work in diffusion models, transformers, and state-space networks for tasks like traffic emission imputation, eye tracking, and PCB defect detection. Collaborative Work: Co-authored with Zheng-Jun Zha, Wei Zhai, Yu Kang, and others in journals like IEEE Transactions on Neural Networks and CVPR Workshops. Scientific Contributions: His research bridges computer vision, machine learning, and industrial applications, emphasizing real-world challenges such as low-light enhancement and sensor fusion.
Xingcheng Zhou is a Research Assistant at the Technical University of Munich (TUM), affiliated with the Chair of Robotics, Artificial Intelligence and Real-time Systems since 2023. He holds an M.Sc. in Electrical and Computer Engineering from TUM (2021) and previously worked as an Industrial AI Researcher at Siemens. Research Interests: Focus on Large Language Models , Vision Language Models , 3D Environment Perception , and Domain Adaptation in autonomous driving contexts. Publications: Contributions to 3D object detection refinement, sim2real domain adaptation, vision-language models, and dataset development for intelligent transportation systems. Teaching Involvement: Co-supervisor for master's theses and seminars on autonomous agents, perception models, and traffic environment understanding. Advising: Mentoring students on projects including LiDAR-guided monocular detection, world models, and multimodal benchmarks for transportation scenes. Trends in Research: Zhou's work bridges low-light image enhancement with spatial-frequency features, surface-aware frameworks for 3D detection, and weakly-supervised domain adaptation. He contributes to benchmarking spatio-temporal video understanding and evaluating autonomous driving datasets. Supervision and Collaboration: Co-authored key surveys and frameworks with Prof. Alois C. Knoll and peers, focusing on real-time roadside LiDARs, graph-based object relationships, and vision-language integration for traffic analysis.
Prof. Laura Leal-Taixé is an Associate Professor at the Technical University of Munich (TUM) leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Chair, promoted from a 2017 Tenure Track Assistant Professorship. Her work focuses on advancing computer vision and machine learning, particularly in video analysis, multi-object tracking, and autonomous systems. She received a Sofja Kovalevskaja Award (2017) for her project socialMaps, which integrates dynamic social data into traffic modeling. Education: B.Sc./M.Sc. in Telecommunications Engineering, Technical University of Catalonia (UPC), Barcelona Ph.D. in Information Processing, Leibniz University Hannover (2014) Postdoc at ETH Zurich (2014–2016), and Senior Researcher at TUM’s Computer Vision Group (2016–2019) Research Interests: Multi-object tracking and segmentation in videos Motion analysis and semantic segmentation for autonomous driving Deep learning for video understanding Social dynamics modeling in urban environments Awards & Grants: €1.65M Sofja Kovalevskaja Award (Humboldt Foundation, 2017) DAAD Australia-German Joint Research Scheme (2017) Multiple travel grants from CVPR and Women in Computer Vision Labs & Collaborations: Dynamic Vision and Learning Group at TUM Collaborations with ETH Zurich, Northeastern University, and NVIDIA
Prof. Dr.-Ing. André Borrmann is an academic leader at the Technical University of Munich (TUM) , where he has headed the Chair of Computing in Civil and Building Engineering since 2011 (formerly Computational Modeling and Simulation). He serves as Director of the TUM Georg Nemetschek Institute - AI for the Built World since 2025 and Spokesperson for the Leonhard Obermeyer Center since 2013. Research Interests Artificial Intelligence in Civil Engineering Digital Twinning Building Information Modeling (BIM) Pedestrian Dynamics Knowledge Representation Construction Simulation His work focuses on AI application across the built environment lifecycle - from generative design to maintenance prediction - with significant contributions to BIM standardization and buildingSMART International IFC extensions. He co-authored the German Ministry of Transport BIM Roadmap and led the BIM4INFRA2020 project. Awards include the 2024 Konrad Zuse Medal and multiple best paper awards at international conferences.
Catherine Faron is a Full Professor at Université Côte d'Azur , affiliated with the I3S laboratory and Inria center . She serves as vice-head of the Wimmics joint research team and leads the Artificial Intelligence and Data Engineering (IAID) program at Polytech Nice Sophia engineer school. Habilitation à diriger les recherches (HDR) in Computer Science, UCA (2017) PhD in Computer Science, Univ. Paris 6 (1997) Her research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning (KRR) and Semantic Web technologies. She develops hybrid intelligent systems combining KRR with machine learning for knowledge extraction, integration, and exploitation across education, health, and digital humanities. Recent publications highlight her work on: 2025 : Knowledge graphs for historical zoological data 2024 : Semantic annotation frameworks in agronomy 2023 : Agricultural data mapping and medical record enrichment 2022 : Visual exploration of big linked data Scientific recognitions include: 2023: Best Paper Award, ESWC 2017: Scientific Excellence Award, UCA 2016: Best Demo Award, ISWC 2015: Best Paper Award, IC 2008: Best PhD Paper Award, ECPPM She has supervised 19 PhD/Master's students and leads/has led projects like D2KAB , DEKALOG , and ZOOMATHIA , with partnerships across academic and industrial institutions.
Stefan Bruckner is a Professor at the University of Rostock , leading the Chair of Visual Analytics within the Institute of Visual and Analytic Computing. His work bridges Visual Analytics , Biomedical Visualization , and Interactive Systems , with a focus on translating complex datasets into actionable insights. Role: Chair of Visual Analytics Key Affiliations: Eurographics Executive Committee, IEEE VGTC Editorial Leadership: Associate Editor, IEEE Transactions on Visualization and Computer Graphics Research spans Medical Visualization , Immersive Analytics , and Proteogenomic Data Exploration . Recent work includes: ProHap Explorer for haplotype analysis Line Harp sonification techniques Narrative visualization frameworks His publications reveal trends in interactive data exploration , multi-omics visualization , and user behavior analysis within medical contexts. Awards include contributions to the Dirk Bartz Prize in 2019. He actively collaborates with international institutions and maintains memberships in ACM, IEEE, and GI.
Prof. Dr. Karsten Niehaus serves as Head of the Proteome and Metabolome Research Group at the Center for Biotechnology (CeBiTec) and Faculty of Biology, University of Bielefeld. His research focuses on proteomics and metabolomics applications in plant-microbe interactions, bacterial stress responses, and disease model systems. His laboratory employs advanced mass spectrometry imaging and cell phenotyping technologies to investigate molecular responses in crops like sugar beet and grapevines under abiotic stress conditions, as well as in cancer models where differentiation therapy impacts tumor malignancy. The group also explores microbial biotechnology through Xanthomonas campestris studies on xanthan production and stress adaptation. Selected publications highlight innovations in 3D microfluidics for biomarker detection and bioinformatics platforms like MetHoS for metabolomics data analysis. His work appears in journals covering Frontiers in Plant Science , Scientific Reports , and Journal of Experimental Botany . Contact: kniehaus@cebitec.uni-bielefeld.de | Office: UHG W7-117
Goran Glavaš is a Professor at the University of Würzburg's Faculty of Mathematics & Computer Science, holding the Chair for Natural Language Processing (Computer Science XII) and affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS). His research focuses on computational semantics, multilingual/low-resource representation learning, and democratizing language technologies through fairness and sustainability. Former Assistant Professor at University of Mannheim (2017-2021) Interim Associate Professor at LMU Munich (2021-2022) Doctorate in 2014 at University of Zagreb under Jan Šnajder Recent research trends emphasize cross-lingual learning, multilingual knowledge integration, and ethical AI frameworks. His group contributes to robust multilingual models, vision-language systems, and sustainable NLP applications in social sciences. Outstanding Paper Award at ACL 2024 (IRCoder) Outstanding Paper Award at EACL 2024 (Kardeş-NLU) Extensive publications in EMNLP, ACL, NAACL, EACL, and TACL Advises a team of researchers at the University of Würzburg's NLP Chair, including Benedikt Ebing, Gregor Geigle, and Fabian David Schmidt. Leads the WüNLP group within CAIDAS, focusing on democratizing language technologies.
Professor Javier Villalba-Diez serves at the Faculty of Business of Heilbronn University of Applied Sciences, Germany, where he integrates artificial intelligence with lean management principles in industrial and business contexts. His international collaborations include a cooperative doctoral program with Technical University of Madrid and Erasmus exchanges with Universidad Politécnica de Madrid. Dr. Villalba-Diez earned dual engineering degrees: Mechanical Engineering from Technische Universität München and Industrial Engineering from Universidad Politécnica de Madrid (2003). His PhD in Engineering, Economics and Organizational Innovation (2016) from Universidad Politécnica de Madrid received the institution's best doctoral thesis award. His research spans Artificial Intelligence (particularly Deep Learning applications), Hoshin Kanri strategic planning, Business Intelligence , and Lean Manufacturing . He pioneers sensor-based methodologies for organizational design, using EEG and industrial IoT to analyze problem-solving patterns and network resilience. His work bridges theoretical models with practical implementations across German, American, Japanese, and Spanish manufacturing facilities. Recent publications demonstrate a clear trajectory toward Industry 4.0 integration , with 60% of his 2019-2020 work focusing on deep learning applications in quality control, sensor networks, and cyber-physical systems. The journal Sensors (MDPI) serves as his primary publication venue, reflecting his emphasis on data-driven industrial analytics. His recognition includes: Prize for best doctoral thesis by Universidad Politécnica de Madrid (2016) As Guest Editor for Sensors and reviewer for journals like Sustainability and Journal of Manufacturing Systems , he shapes discourse in industrial AI. His doctoral supervision with Madrid focuses on AI-driven strategic organizational design, while industry collaborations with manufacturing facilities worldwide translate research into operational frameworks. He maintains active roles in curriculum development for Industry 4.0 education through the PROFH4 digital initiative. Dr. Villalba-Diez operates within international research networks, leveraging his multilingual capabilities (German, English, Spanish) to facilitate transnational projects. His work with Neo4j for Hoshin Kanri visualization exemplifies his approach to making complex organizational networks actionable for industry leaders.
Elena Simperl is a Professor at King's College London, UK, with former affiliations at the University of Southampton and Karlsruhe Institute of Technology. Her work focuses on knowledge graphs, semantic web technologies, and AI-driven data management. She leads research in collaborative knowledge engineering, dataset search, and AI ethics, contributing to projects like the TheyBuyForYou platform for public procurement transparency. Her research interests span knowledge representation, crowdsourcing, and human-AI collaboration. Notable contributions include advancing methods for knowledge graph construction, improving data quality via crowdsourced and automated approaches, and exploring the societal impact of AI systems. She has co-edited major conferences such as ISWC and ESWC, and her work bridges technical innovation with practical applications in public policy and information systems. Key projects include developing frameworks for dataset usability, AI-ready data infrastructure, and systems for fact-checking visual content. Her collaborations span academia and industry, addressing challenges in data governance, misinformation detection, and ethical AI deployment.
Fumio Okura is a professor at Osaka University , specializing in Computer Vision and 3D Reconstruction . His work bridges Photometric Stereo , Neural Rendering , and Medical Imaging , with a focus on cognitive decline prediction and plant modeling . He collaborates extensively with researchers like Hiroaki Santo and Yasuyuki Matsushita . Education: Ph.D. in Computer Science (Osaka University) Research Interests span Computer Vision , Photometric Stereo , 3D Reconstruction , and Biomedical Applications . His recent work includes HoGS for object reconstruction and TreeFormer for botanical structure estimation. Publications trend toward neural rendering , reflectance modeling , and augmented reality . Notable contributions include PPGCN for cognitive detection and MVCPS-NeuS for multi-view photometric stereo. Labs & Collaborations include the Osaka University Computer Vision Lab , working with teams on photometric analysis and medical imaging .
Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.
Sabine Schulte im Walde is an Apl. Prof. (Adjunct Professor) at the Institute for Natural Language Processing (IMS) , University of Stuttgart. Her research bridges computational linguistics and cognitive semantics, focusing on noun compounds, semantic change, and multimodal representations. Key research areas: semantic drift, compositionality, German particle verbs Instrumental in creating datasets for semantic change detection (DWUGs, DURel) Recent work explores BERT's limitations in compound semantics and cross-lingual metaphor detection She contributes to major European NLP initiatives, with publications in top venues like ACL. The IMS group specializes in language-technology intersections, managing 6 professorships, 50+ researchers, and 200+ students in NLP programs.
