Florian Leiser is a Professor at the Chair of Information Infrastructures (led by Prof. Dr. Ali Sunyaev) at Technical University of Munich's Heilbronn campus. His research focuses on human-AI collaboration, privacy-preserving algorithms, and explainability in machine learning systems. Current research areas include Hybrid Intelligence, Human-centered Generative AI (LLMs), Federated Learning, and Health Information Systems Recent publications demonstrate expertise in Explainable AI for medical imaging LLM hallucination detection Federated learning architectures Human-in-the-loop systems Healthcare data applications He contributes to teaching through Human-Centered Artifact Design courses Collaborative teaching roles in machine learning Supervising student projects
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.
Helmholtz Institute Ulm for Electrochemical Energy StorageGermany
Zhang Yang is an Associate Professor at the School of Medical Engineering, Harbin Institute of Technology (Shenzhen), with a joint appointment as Visiting Professor at the University of Tokyo starting in July 2024. He holds a PhD from the University of Cambridge's Department of Pathology and an M.Phil. from the University of Hong Kong's HKU-Pasteur Research Center. Previously, he served as an Assistant Professor at Harbin Institute of Technology (Shenzhen) from September 2015 to December 2020. His research integrates computational and experimental approaches to address challenges in pathogen and cancer research. On the computational side, his work focuses on developing AI-powered microscopic imaging systems, applying deep learning to analyze multi-omics data (including proteins, DNA, miRNAs, LncRNAs, and mRNAs), and utilizing deep learning in cheminformatics for drug discovery. On the experimental side, his laboratory combines imaging, high-throughput sequencing, mass spectrometry, and chemical biology to understand disease mechanisms at the molecular level. His publication record demonstrates significant impact, with over 50 SCI-indexed papers in high-impact journals including Nature Communications, Briefings in Bioinformatics, Bioinformatics, Analytical Chemistry, and Trends in Biotechnology. His work has been cited by prestigious journals such as Nature Reviews Methods Primers and Nature Communications, with three ESI highly cited papers. His research spans multiple interdisciplinary fields, combining artificial intelligence with biomedical applications to advance diagnostic and therapeutic approaches. World's Top 2% Scientists 2021 Fellow of the Royal Society of Biology Three ESI Highly Cited Papers Five authorized national invention patents As an academic leader, he serves as Associate Editor for BMC Biology and Frontiers in Microbiology, Academic Editor for PLOS Genetics, Editorial Board Member for Communications Biology, and Guest Editor for a Special Issue on AI in analytical chemistry in Trends in Analytical Chemistry. His laboratory actively collaborates with international institutions, with graduates pursuing further studies at Hong Kong Chinese University, Hong Kong University of Science and Technology, Hong Kong Polytechnic University, Macau University, and the University of New South Wales. He teaches Introduction to Modern Biology for undergraduates and Bioanalytical Chemistry for graduate students.
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Kentaro Inui is a distinguished researcher at Tohoku University , specializing in Natural Language Processing , Computational Linguistics , and Machine Learning . His work focuses on advancing language model behavior through rigorous empirical analysis, including mechanisms for detokenization , entity identification , and numerical reasoning . Inui has pioneered methods to rectify spurious beliefs in LLMs via unlearning techniques and explored the dynamics of reasoning strategies in neural models. His research addresses chat translation quality through metrics like MQM-Chat and investigates repetition neurons responsible for text generation patterns. Inui also contributes to argumentation analysis with annotation frameworks like LPAttack and develops resources such as COPA-SSE for commonsense reasoning. His work on universal graph-based relation extraction and cross-stitching architectures has established new benchmarks in NLP task performance. Inui's publications span top-tier conferences including ACL , EMNLP , and LREC , often involving collaborations with researchers like Benjamin Heinzerling and Jun Suzuki. His methodological innovations in semi-structured explanation generation , position embedding (e.g., SHAPE), and zero pronoun resolution demonstrate his focus on both theoretical and practical NLP challenges. While no direct awards or student mentorship data appear in the provided corpus, his extensive publication record (over 20 papers between 2021-2025) underscores significant contributions to NLP education tools , knowledge base integration , and dialogue system consistency . Current projects like ReCall mechanisms and numerical property encoding directions highlight his ongoing impact on model interpretability and reasoning accuracy.
Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.
Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.
German National Library of Science and TechnologyGermany
Oliver Karras is a researcher, data scientist, and lecturer at the Data Science and Digital Libraries research group at TIB – Leibniz Information Centre for Science and Technology. He holds a BSc, MSc, and PhD in Computer Science from Leibniz University Hannover. His research focuses on FAIR scientific knowledge, Open Research Knowledge Graph (ORKG), and national research infrastructure projects like NFDI-4Ing and FAIR-DS. He is a member of the German Informatics Society (GI) and spokesperson of the Requirements Engineering (RE) group, contributing to conferences and journals as a reviewer. Previously, he was a research associate and PhD student in the Software Engineering group at Leibniz University, leading the DFG project ViViReq on video integration in requirements engineering. His research interests include Data Science, AI, Knowledge Representation, Software Engineering, Requirements Engineering, Empirical Research, and Open Science. He has published over 60 papers in these areas, with notable contributions to knowledge graphs, FAIR data, and reproducibility frameworks. His work emphasizes organizing scientific knowledge for accessibility and collaboration across disciplines. He is actively involved in initiatives like the ORKG, promoting sustainable literature reviews and open science practices. His professional contributions extend to tool development, such as OntoAligner for ontology alignment and SciKGTeX for semantic annotation in LaTeX. Oliver Karras collaborates with institutions like Leibniz University, contributing to the NFDI consortium and energy research projects. His expertise bridges technical innovation and academic rigor, addressing challenges in knowledge management and reproducibility. His current roles include advancing TIB’s research infrastructure and fostering interdisciplinary collaboration through knowledge graph applications.
Chang Liu is a Research Associate at NHR@FAU (Center for National High Performance Computing Erlangen) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he joined the AI group in April 2025 to support AI-oriented projects across diverse research fields. Prior to this position, he was a doctoral researcher at the Pattern Recognition Lab at FAU until March 2025. Chang Liu earned his degree in Medical Engineering at FAU. His academic journey at FAU began in September 2016 as a student, progressed to a researcher at the Pattern Recognition Lab starting in March 2020, and culminated in his doctoral research until March 2025. Dr. Liu's research focuses on medical image processing and analysis, with particular emphasis on the automated segmentation of computed tomography (CT) images and the generation of high-quality CT images. His work bridges computer science and medical applications through artificial intelligence to solve complex healthcare problems. Beyond his core research, he has contributed to applying AI technologies in diverse fields including second language education and nail disease diagnosis, demonstrating his interdisciplinary approach to problem-solving. His expertise spans data augmentation techniques, multi-organ segmentation, CT reconstruction, and radiation dose optimization. His publication record reveals consistent advancement in medical image analysis techniques, particularly in CT imaging and segmentation. His work shows a progression from foundational deep learning applications to more sophisticated approaches incorporating anatomical knowledge and addressing practical clinical constraints like limited annotations and radiation safety. Dr. Liu has mentored numerous students through their thesis work, guiding them in cutting-edge research at the intersection of AI and medical imaging. His advisees have completed projects on breast cancer risk stratification, medical segmentation annotation, U-Net architecture configuration, and other innovative topics in medical image analysis. As part of NHR@FAU, Dr. Liu works with the AI group to enhance research projects using modern high-performance computing systems, applying his expertise in medical image analysis to support diverse research fields across FAU.
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
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
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.
Yan Xia is a Senior Researcher at the Computer Vision Group of the Technical University of Munich (TUM) and a Research Scientist at the Munich Center for Machine Learning. She collaborates with Prof. Daniel Cremers and previously completed her PhD at TUM under supervision of Prof. Uwe Stilla and Prof. Daniel Cremers, with a visiting period at the Visual Geometry Group of the University of Oxford under Dr. João Henriques.
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.
Dr. Olya Hakobyan is a Researcher at the University of Bielefeld, working in the Faculty of Engineering within the Human-Centered Artificial Intelligence Group at the Center for Cognitive Interaction Technology (CITEC). She has been with the university since June 2021 as a research associate in the Multimodal Behavior Processing lab. Dr. Hakobyan's educational background includes: PhD in Computational Neuroscience (2016-2020) from the Institute for Neural Computation at Ruhr University Bochum Master's degree in Cognitive Science (2014-2016) from Ruhr University Bochum Bachelor's degree in Linguistics (2010-2014) from Yerevan Brusov State University of Languages and Social Sciences Her research focuses on the intersection of artificial intelligence, cognitive science, and human-computer interaction. Dr. Hakobyan specializes in affective computing, explainable AI systems, and the development of privacy-preserving data donation platforms. Her work on the Dona platform represents a significant contribution to ethical data collection methods for social interaction analysis. She investigates how AI systems can interpret human emotions through audio and visual channels while maintaining transparency in decision-making processes. Her research also extends to cognitive modeling, particularly in understanding memory processes and recognition mechanisms. Dr. Hakobyan's publication record demonstrates a strong trajectory in developing methods for analyzing social interactions through digital footprints while addressing critical privacy concerns. Her work bridges technical innovation with ethical considerations in data collection. Her scientific contributions have focused on: Privacy-preserving data donation frameworks Explainable AI for emotion recognition systems Analysis of social interactions through digital communication Cognitive modeling of memory processes Ethical considerations in AI and data science
Dr. Patrizia Carmassi is a researcher specializing in medieval liturgy, church history, and manuscript studies. She works at the Herzog August Library, contributing to cataloging projects like the Catalogue of Western Medieval Manuscripts of the Göttingen State and University Library . Her habilitation in book science and ecclesiastical history (2014, extended 2020) and Marie Curie Fellowship (2018-2019) highlight her academic credentials. Liturgy and Church History of the Middle Ages Latin Philology Text and Image Research Palaeography Codicology Library History Her research focuses on manuscript materiality, liturgical traditions, and cultural knowledge transfer, particularly in German and Italian medieval contexts. Recent projects include analyzing time-related medieval texts and the Marquard Gude manuscript collection. Marie Skłodowska-Curie Fellowship (2018-2019): Studied early medieval time concepts. Abilitazione scientifica nazionale (2014, extended 2020): Qualified for Italian professor W2 positions in book science and religious history.
