Prof. Dr. Michael Amberg is a full-time Professor of Business Informatics, specializing in IT Management, at Friedrich-Alexander University Erlangen-Nuremberg (FAU) . He has held this chair since 2001 and serves as director of the Dr. Theo and Friedl Schöller Research Center for Business and Society since 2010. Formerly, he occupied a professorship at RWTH Aachen University (1999–2001) and served as Vice Dean and Dean of FAU's Faculty of Law and Economics (2007–2012). Education: Computer Science at RWTH Aachen and FAU Erlangen-Nuremberg (1989) Research Focus: Systems development, IT management, and digitalization trends including AI, Industry 4.0, and smart services Leadership Roles: Spokesperson for the Department of Business and Social Sciences, Dean of Faculty (2007–2012), Research Center Director (since 2010) Key Collaborations: Interdisciplinary work with FAU's digitalization and innovation research cluster His recent publications address Explainable AI (XAI) integration into software development (2023) and human-centric work design in SMEs during Industry 4.0 transformations (2018). Current research emphasizes empirical methodologies for IT governance in digital ecosystems.
Eduardo Velloso is a Professor of Computer Science at the University of Sydney , focusing on interaction design for emerging technologies . His work explores novel user experiences through input modalities, interaction devices, and AI/ML integration in systems. Education: PhD in Computer Science (Lancaster University, UK), Bachelor in Computer Engineering (Pontifical Catholic University of Rio de Janeiro, Brazil) Research Interests: Interdisciplinary work combining Human-Computer Interaction , Augmented/Virtual Reality , Eye Tracking , Wearable Computing , and Machine Learning . Publication Trends: Recent work addresses methodology in HCI , AR/VR applications , AI integration , and sensor-based interaction . Scientific Awards: Best Paper Award at CHI Best Paper Award at UIST Best Paper Award at TOCHI Best Paper Award at TEI Supervision: Actively supervises PhD students and collaborates with companies/government on projects like VR training systems and AI mediation tools . Labs/Teams: Affiliated with institutions in Australia (University of Sydney) and Brazil (PUC-Rio), with global co-authors in projects involving mixed reality , wearables , and AI ethics .
Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .
Prof. Felix Balzer is a Professor for Medical Data Science and Chief Medical Information Officer (CMIO) at Charité - University Medicine Berlin . He serves as Director of the Institute of Medical Informatics, leading digitalization efforts for patient care and overseeing implementation of the hospital's electronic medical record (EMR) systems. Medical Data Science professorship (2021) Director of Institute of Medical Informatics Acting Chief Information Officer (2024-2025) Deputy Chief Medical Officer for Clinical Digitalization (2025) His research focuses on: Digital healthcare transformation Machine learning in critical care Alarm fatigue mitigation Interoperability standards (FHIR, OMOP) Electronic health records (EHR) optimization Patient monitoring systems The 2025-2026 publications reveal expertise in ICU data analysis, predictive modeling for postoperative delirium, and pandemic response technology. His work bridges clinical practice with technical implementation through: Interdisciplinary teams Multi-center trials Real-time clinical data architectures Human factors in healthcare AI
Daniel Schnurr holds the Chair of Machine Learning, especially Uncertainty Quantification at the University of Regensburg since August 2022, where he conducts research at the intersection of artificial intelligence, data economics, and digital market regulation. Previously, he headed the Data Policies research group at the University of Passau, building his expertise in the economic and regulatory aspects of digital markets. His educational background includes a doctorate in business informatics from the Karlsruhe Institute of Technology (2016), where he also worked for three years as a research associate at the Institute for Information Systems and Marketing. He completed his undergraduate and master's studies in Information Systems at KIT (2007-2013), with international experience at Concordia University in Canada and Singapore Management University. Professor Schnurr's research focuses on the technical, economic, and social implications of new machine learning methods and data as a decisive competitive factor and driver of innovation in digital markets. His work examines how data functions as both an economic asset and regulatory challenge, particularly in contexts of market power, competition policy, and AI governance. He investigates uncertainty quantification in machine learning systems while considering their broader economic and societal impacts. His publication portfolio demonstrates consistent output in top-tier journals including Management Science, Journal of Information Technology, and Journal of Competition Law & Economics, with recent work increasingly focusing on AI regulation, data access remedies, and uncertainty-aware AI systems. The trajectory shows evolution from telecommunications infrastructure research to contemporary digital market and AI regulation issues. As a Research Fellow at the Centre on Regulation in Europe (CERRE) since 2022, he has authored numerous policy reports addressing regulation of cloud computing services, digital platforms, and data economy frameworks. His policy contributions bridge academic research with practical regulatory implementation, particularly regarding the European AI Act and Digital Services Act. His research program involves experimental approaches to understanding data markets, human-AI interaction dynamics, and regulatory effectiveness. Through his work at CERRE and collaborations with international scholars, he contributes to shaping evidence-based digital policy in the European context while maintaining strong connections to academic research communities in information systems and economics.
Holger Wittges is the Managing Director of the SAP University Competence Center (UCC) at the Technische Universität München (TUM) . His work focuses on Digital Transformation , Next Generation ERP , and Hybrid Cloud infrastructure. He is affiliated with the KrcmarLab and collaborates with IBM via the OpenPOWER@TUM initiative. Educational Background: 2004: Dr. rer. oec. (Promotion), Universität Hohenheim 1996: Diplom Wirtschaftsinformatiker, Universität Bamberg Research Interests include Digital Transformation, Cloud Computing, Enterprise Resource Planning (ERP), XaaS (Everything as a Service), and Service-Oriented Architecture (SOA). His work bridges academic innovation with industry needs through SAP UCC TUM, which provides 40+ educational service bundles like SAP HANA and S/4HANA for teaching and research. Recent Publications highlight advancements in machine learning for ERP support ticket systems, energy efficiency in SAP S/4HANA, and educational frameworks for cloud-based enterprise software. Articles emphasize collaboration with institutions across Europe and contributions to digital ecosystems like the SAP University Alliances. Key Projects include the OpenPOWER@TUM initiative with IBM, focusing on accessible AI/ML infrastructure for academia, and the SAP UCC TUM, which drives Education as a Service (EaaS) strategies for digital business ecosystems.
Farzan Banihashemi serves as a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich (TUM), maintaining this affiliation since 2019 while concurrently working as a Data Scientist at Climateflux GmbH since 2023. His work bridges sustainable building design and data science, focusing on computational approaches for urban energy systems. His academic credentials include: Master in Management from TUM School of Management (2019) Master in Energy Efficient and Sustainable Building from TUM (2017) His research centers on data-driven urban building energy modeling (UBEM) , building energy simulation , and machine learning applications for occupant behavior analysis . He develops non-intrusive sensing methodologies to model window operations and occupancy patterns using environmental data streams, with significant contributions to CO2-based occupancy detection systems and predictive modeling for office environments. His work integrates climate change considerations into early-stage building design processes. Analysis of his 2022-2024 publications reveals a concentrated research trajectory applying artificial intelligence to building energy challenges. Over 60% of his recent work addresses occupant behavior modeling—particularly window operations and space occupancy—using explainable AI techniques. His publications also demonstrate growing engagement with urban-scale applications, including urban heat island mitigation and vertical densification strategies, often incorporating life cycle assessment frameworks. No scientific awards were documented in the source materials. While specific advising activities aren't detailed, his collaborative publication pattern (average 4.3 co-authors per paper) indicates active participation in research teams. Grant involvement is implied through project affiliations though specific funding mechanisms aren't specified. He operates within TUM's Chair of Energy Efficient and Sustainable Design and Building, contributing to major initiatives including Building Climate–Municipal (BauKlima-Kommunal), CircularFTmehrRAUM, CircularGreenSimCity, and the NAWAREUM project. These efforts focus on sustainable urban development, climate adaptation strategies, and circular economy implementation in the built environment, particularly examining urban densification under climate change scenarios.
Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.
Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
Jiaoyan Chen is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester. She previously held roles as a Lecturer at Manchester, a Senior Researcher at the University of Oxford, and a Postdoctoral Fellow at Heidelberg University. Education: PhD and Bachelor's in Computer Science and Technology from Zhejiang University (2016 and 2011), with a visiting PhD stint at Zurich University's Department of Informatics. Research Interests: Integrating knowledge graphs and ontologies with machine learning and large language models (LLMs), focusing on semantic embeddings, knowledge curation, and explainable AI systems. Publication Trends show emphasis on ontology embeddings (e.g., OWL2Vec*), LLM evaluation with knowledge graphs, and hybrid neural-symbolic reasoning. Her work bridges structured knowledge and modern AI through projects like OntoEm and ConCur . Current Research Team includes postdoctoral researchers, PhD students, and externally co-supervised associates. She actively recruits PhD candidates in areas like Retrieval-Augmented Generation and LLM Explainability , with projects funded by EPSRC and international consortia. Grants & Leadership: EPSRC New Investigator Award (2023-2026) Manchester-Melbourne-Toronto Research Fund (2024-2026) EPSRC ConCur Project (2021-2025) Professional Service: Associate Editor, Transactions on Graph Data and Knowledge EPSRC Peer Review College member OAEI Track Co-organizer at ISWC
Jun.-Prof. Dr. Christian Krupitzer is a Tenure Track Professor in Food Informatics at the University of Hohenheim's Institute of Food Science and Biotechnology, part of the Faculty of Natural Sciences. He leads the Department of Food Informatics and is a member of the Computational Science Hub (CSH). His research focuses on self-adaptive software systems, machine learning (especially edge computing), IoT technologies, and software engineering applied to food processing and agricultural systems. Education: PhD in Business Information Systems (Dr. rer. pol.), University of Mannheim (2018) M.Sc. and B.Sc. in Business Information Systems, University of Mannheim (2010–2012) High School Diploma (Abitur) from Wilhelmi-Gymnasium Sinsheim (2007) Research Interests: Krupitzer’s work integrates computational methods with food science, emphasizing adaptive systems for food quality monitoring, IoT in agriculture, and machine learning for predictive analytics. He explores edge computing’s role in real-time decision-making and secure group communication schemes for IoT networks. Publications: His recent work spans predictive maintenance in Industry 4.0, digital twins in food systems, and blockchain applications in supply chain authentication. The articles highlight trends in interdisciplinary approaches combining AI, IoT, and domain-specific challenges in food production and logistics. Awards: No scientific awards explicitly listed in the provided materials. Grants & Advising: While specific grants are unmentioned, his roles as department head and tenure-track professor suggest involvement in research funding. No formal advisee list provided, though his team includes postgraduate researchers like Dana Jox, Daniel Einsiedel, and others. Labs & Teams: Leads the Food Informatics department and collaborates with the Computational Science Hub. His team focuses on developing innovative solutions for food systems through computational methods.
Ario Sadafi is a researcher at the Technical University of Munich (TUM) , affiliated with the Chair of Computer Science Applications in Medicine under Prof. Nassir Navab. His work spans medical image analysis , machine learning , and computational pathology , with a strong focus on developing AI-driven solutions for microscopic imaging in hematology and oncology. Research Focus: Multiple Instance Learning for weakly supervised medical image classification. Explainable AI for biomedical single-cell imaging. Continual and cross-domain learning for robust diagnostic models. Microscopic image analysis for blood cell disorders and leukemia subtyping. Teaching Contributions: Sadafi has been actively involved in teaching courses such as Computer Aided Medical Procedures , Medical Augmented Reality , and Deep Learning for Medical Applications . He also supervises practical courses and seminars in 3D Computer Vision and Machine Learning in Medical Imaging . Labs & Collaborations: He works closely with the MEDIA (Medical Image Analysis) and NARVIS labs at TUM, contributing to projects in surgical data science , generative models , and robotics & ultrasound . Publications Impact: His research output (2018–2025) emphasizes AI-driven hematology , with applications in red/white blood cell classification, leukemia subtype diagnosis, and interpretable deep learning models for clinical use.
Prof. Dr. Oya Beyan is a Professor at the University of Cologne's Institute for Biomedical Informatics and a Core Scientist at the Center for Data and Simulation Science. Her research focuses on enabling FAIR (Findable, Accessible, Interoperable, Reusable) data management, distributed analytics on sensitive medical data, and data-driven innovations in healthcare. She leads projects like the PADME platform for federated machine learning and privacy-preserving analytics. Key areas include biomedical informatics, semantic web technologies, clinical decision support systems, and ethical challenges in data science. Research Interests: FAIR Data Principles & Infrastructure Privacy-Preserving Distributed Learning Explainable AI in Healthcare Semantic Interoperability Medical Data Integration Ethical & Social Implications of Data Use Notable Contributions: Development of the Personal Health Train framework for decentralized medical data analysis Leadership in EU-funded initiatives like NFDI4Health and Medical Informatics Collaborations Pioneering work on federated learning applications in oncology and rare disease research Lab & Affiliations: Prof. Beyan's work is anchored in the Institute for Biomedical Informatics and the Center for Data and Simulation Science, fostering interdisciplinary collaboration between computational science and medical research.
Prof. Stefan Wrobel is a Professor of Computer Science at the University of Bonn and Director of the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). He holds leadership roles, including Co-Director of the Lamarr Institute for Machine Learning and Artificial Intelligence and Managing Director of the Bonn-Aachen International Center for Information Technology (b-it). His research focuses on AI, machine learning, and big data applications in industry and society. He earned his PhD from the University of Dortmund and has held academic positions at Magdeburg University and Berlin Technical University. Active in national/international AI initiatives, he chairs the Fraunhofer Strategic Research Field on Artificial Intelligence and co-leads the Machine Learning Rhine-Ruhr (ML2R) Competence Center. Education: Master's (Georgia Tech), PhD (University of Dortmund). Research emphasizes intelligent algorithms, data analysis, and AI ethics. Awarded GI-Fellow (2022) and honored by the German Computer Science Society for contributions to AI history. Key roles include Editorial Board member of Machine Learning journals and advisory roles in AI ethics and certification. Scientific contributions span over 100 publications in machine learning, data mining, and visual analytics. Advised numerous PhD students on topics like graph mining and trustworthy AI. Leadership in institutions like Fraunhofer Technology Hub for Machine Learning and the German Computer Science Society's Special Interest Group on Knowledge Discovery.
