Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Bing Qin is a Researcher specializing in computational linguistics, artificial intelligence, and multimodal learning. Their work focuses on enhancing large language models' capabilities in temporal knowledge graph forecasting, cross-lingual alignment, and safety mechanisms. Core Research Areas: Knowledge graphs, multimodal systems, reasoning frameworks Technical Innovations: Analogical replay, gain signal estimation, cross-modal attention intervention Recent Trends: 2025 publications emphasize training-free methods and preference alignment in LLMs
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.
Zhenjie Zhang is a Professor in the Department of Computer Science at East China Normal University's School of Computer Science and Software Engineering, with a distinguished research career spanning nearly two decades. His work bridges theoretical computer science with practical industrial applications, maintaining strong international collaborations with researchers from TU Wien, National University of Singapore, and industry partners including ByteDance. Dr. Zhang's research focuses on the intersection of database systems, machine learning, and industrial applications. His early work centered on database privacy and query processing, evolving toward causal inference, fault diagnosis systems, and industrial AI applications. His recent publications demonstrate a strategic shift toward solving real-world engineering problems using advanced machine learning techniques, particularly in manufacturing, transportation, and cloud systems. The consistent publication trajectory across top venues like IEEE TKDE, VLDB, and ACM Transactions shows sustained research excellence and adaptability to emerging technical challenges. His publication record reveals significant contributions to causal inference methods, evidenced by multiple papers on causal discovery and transfer learning. The research demonstrates practical impact through industrial collaborations, particularly in fault diagnosis systems for mechanical equipment and adaptive control for unmanned vehicles. The recent work shows increasing focus on deploying AI models efficiently in resource-constrained environments, reflecting awareness of practical implementation challenges. Dr. Zhang has mentored numerous junior researchers who have become active contributors in the field, including Ruichu Cai and Zining Zhang. His collaborative network spans multiple continents, indicating strong research leadership and international recognition. The consistent flow of publications in top venues suggests successful grant funding and research group management, though specific grant details aren't provided in the source material.
Mark Last is a Professor at Ben-Gurion University in Beersheba, Israel, with a distinguished career spanning over three decades in computer science research. His work primarily focuses on data mining, machine learning, and natural language processing applications. His research interests encompass stream data mining, text summarization, fuzzy logic systems, and classification algorithms. Last has made significant contributions to developing techniques for analyzing dynamic data streams, multilingual text processing, and applying machine learning to real-world problems in healthcare, social media analysis, and security informatics. His work often bridges theoretical advancements with practical applications, particularly in handling non-stationary data and developing interpretable models. Recent research trends show a continued focus on stream data analysis, with applications expanding into social media monitoring, healthcare prediction systems, and multilingual content analysis. His work demonstrates consistent innovation in adapting machine learning techniques to evolving data environments and practical challenges. Mark Last has maintained a prolific publication record with over 175 publications documented in DBLP, collaborating extensively with researchers including Abraham Kandel, Marina Litvak, and Oded Maimon. His work has been published in top venues including IEEE Access, Machine Learning journal, and Expert Systems with Applications.
Stefan Funke is a researcher at the University of Stuttgart, Germany, with a focus on algorithms and computational geometry. His work spans wireless communication, route planning, and trajectory analysis. Research Interests: Algorithms, Computational Geometry, Wireless Communication, Route Planning, Trajectory Segmentation His recent publications (2024-2025) explore topics like 3D epithelial cell dynamics, graph radius computation, and polyline simplification, emphasizing scalability and efficiency. Earlier works (2017-2019) investigate contraction hierarchies, energy-efficient routing, and trajectory storage systems. Stefan collaborates frequently with Sabine Storandt, Claudius Proissl, and Tobias Rupp. He applies geometric methods to problems in wireless networks, road systems, and data structures, with a recurring emphasis on optimization and robustness.
Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.
Muhammad El-Hindi is a researcher at the Technical University of Darmstadt , focusing on database systems , blockchain technology , and secure data management . His work bridges theoretical innovation with practical applications in cloud computing, trusted execution environments, and decentralized systems.
Prof. Dr. Vera King is Professor of Sociology and Psychoanalytic Social Psychology at Goethe University Frankfurt am Main and Managing Director of the Sigmund Freud Institute Frankfurt. She is affiliated with the Department of Social Sciences and Institute of Sociology, where she leads research on the intersection of sociology and psychoanalysis. Her work bridges theoretical frameworks with contemporary social challenges, particularly in the digital age. King's research focuses on the social-psychological analysis of societal developments, their institutional, intergenerational, and individual psychological consequences. She examines the interplay between psychological and social determinants of individual action, exploring theoretical and methodological connections between sociology and psychoanalysis. Her current research addresses social-psychological analyses of crisis phenomena, psychological consequences of digitalization (e.g., in the 'The Measured Life' project), optimization requirements in changing time relations (e.g., in the DFG research project 'Reproductive Timing'), and shifts in pathology and normality in contemporary society. Her recent publications reveal a clear trend examining how digital measurement and optimization transform social relations and individual psyche. King's work investigates how digital technologies create new 'normalities' and 'pathologies,' particularly in social media use, self-tracking practices, and workplace environments. She analyzes the cultural shift toward quantification and how it affects attention, relationships, and self-perception, revealing both productive possibilities and destructive mechanisms in digitally optimized societies. Prof. King supervises Bachelor and Master theses in sociology and social psychology, requiring prior attendance at her 'Sociology and Psychoanalytic Social Psychology' colloquium. She has led major research projects including 'The Measured Life' (funded by Volkswagen Foundation), 'Reproductive Timing' (DFG research project), and participates in the interdisciplinary research cluster 'ConTrust - Vertrauen im Konflikt. Politisches Zusammenleben unter Bedingungen der Ungewissheit.' She leads the research team at the Sigmund Freud Institute and collaborates with prominent scholars including Hartmut Rosa and Benigna Gerisch. Her team conducts interdisciplinary work examining the psychosocial consequences of migration and flight, adolescent trajectories, and generational dynamics. King's approach integrates sociological theory with psychoanalytic methodology to understand how social structures shape psychological processes and vice versa.
Weierstrass Institute for Applied Analysis and StochasticsGermany
Luca Rizzi is a Full Professor at International School for Advanced Studies (SISSA) in Trieste, Italy, where he serves as the Coordinator of the PhD program in Mathematical Analysis, Modeling, and Applications and Principal Investigator of the ERC Starting Grant Project GEOSUB (2022-2026). His research focuses on Geometric Control Theory and Sub-Riemannian Geometry, with significant contributions to the geometric analysis of spaces with non-holonomic constraints. His research interests include: Geometric Control Theory Sub-Riemannian Geometry Optimal Control Problems Geometric Analysis Rizzi leads the GEOSUB project developing geometric and functional interpolation inequalities for sub-Riemannian manifolds, with implications for geometric analysis on non-smooth spaces, hypoelliptic operators, and geometric measure theory. His work bridges theoretical mathematics with applications in control theory and geometric structures. His scientific recognition includes: ERC Starting Grant for Project GEOSUB (2022-2026) Rizzi mentors PhD students Dario Sterzi and Daniele Tiberio, and supervises postdocs Samuel Borza, Giorgio Stefani, and Ye Zhang. He organizes the Geometric Structures seminar at SISSA and manages a sub-Riemannian community mailing list with over 200 subscribers. He has organized numerous conferences including the Workshop on Geometric Variational Problems in Sub-Riemannian Geometry (2025), Riemann 200: Mathematics and Physics (2026), and the XXXV Convegno Nazionale di Calcolo delle Variazioni (2026). As a member of the editorial board of the Journal of Dynamical and Control Systems, Rizzi actively contributes to advancing research in his field through academic service and international collaboration.
Martin Gronemann is a researcher at the Institute for Computer Science , University of Cologne, Germany. His work focuses on graph algorithms , graph drawing , and network visualization , with projects like the Open Graph Drawing Framework (OGDF) and GEODUAL (Geometric Duality). He has taught courses including Computer Science II and Efficient Algorithms , and led seminars on research-oriented programming in C++ . Research Interests: Graph Algorithms Graph Drawing Network Visualization Computational Geometry Book Embeddings Queue Layouts Publications highlight his expertise in structural graph theory, geometric representations, and algorithm engineering. Recent work includes advancements in map graphs of bounded treewidth , strictly-convex planar drawings , and NP-completeness of DAG page-number recognition . Projects: OGDF - Open Graph Drawing Framework GEODUAL - Geometric Duality Teaching: Lecture on Computer Science II (WS 18/19) Exercises for Efficient Algorithms (SS 17, WS 11/12, WS 10/11, WS 13/14, WS 16/17)
Prof. Dr. Sven Husmann is a Chair Holder and Professor in the Department of Economics and Business Administration at the European University Viadrina Frankfurt (Oder). He serves as Dean and holds prominent roles in the university's governance, including membership in the Presidential Board, Foundation Board, Audit Committee, and Library Commission. His academic work focuses on finance and capital market theory, with a specialization in corporate valuation and risk management. Professor of Business Administration Chair of Finance and Capital Market Theory Dean, European University Viadrina His research explores the intersection of taxation and corporate finance, particularly in discounted cash flow (DCF) methodologies, weighted average cost of capital (WACC) frameworks, and valuation under international accounting standards. Recent publications address tax-optimal financing, investment appraisal, and risk modeling in global markets. Husmann's scholarly output spans peer-reviewed journals, book chapters, and discussion papers. Key themes include tax-adjusted valuation techniques, capital market dynamics, and empirical studies on corporate risk management. He frequently collaborates with researchers like Kruschwitz, Löffler, and Stephan on finance theory and policy. He leads the Chair of Finance and Capital Market Theory team at the European University Viadrina, mentoring students and contributing to academic discourse through publications and conference participation. His office hours are available by appointment, and he provides guidance on bachelor's and master's theses.
Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.
Prof. Dr. Christian Mieke is a Professor of Business Administration specializing in Innovation Management at the Department of Economics of the Brandenburg University of Technology (BTU) in Brandenburg an der Havel, Germany. He has held this position since 2010, following his habilitation at the Brandenburg University of Technology Cottbus in 2009. His office is located in Building A (Business and Economics Center), Room A.2.33 at Magdeburger Straße 50, and he can be contacted at christian.mieke@th-brandenburg.de. Prior to his current position, he served as a Professor at Provadis School of International Management & Technology Frankfurt am Main (2009-2010) and held parallel positions as a Privatdozent at BTU Cottbus (2009-2014) and Guest Professor at Alpen-Adria-University Klagenfurt (2013-2014). Prof. Mieke's educational background includes: Habilitation in Innovation Management at BTU Cottbus (2009) Doctorate in Early Technology Detection at BTU Cottbus (2005) Studies in Industrial Engineering at Technical University of Ilmenau and Technical University of Crete (1996-2001) His research focuses on Innovation Management, Technology Management, Production Management, and Logistics, with particular emphasis on strategic planning of innovation processes, technology roadmapping, and integration of innovation management within production systems. He has developed methodologies for scenario-based process portfolios and technology roadmapping, with recent work increasingly addressing the strategic aspects of factory planning and production networks in the context of Industry 4.0. His publication record demonstrates consistent scholarly output with a notable trend toward developing practical methodological handbooks that translate complex theoretical concepts into actionable business practices. His recent work shows increasing attention to strategic factory planning, management of production networks, and integration of innovation processes within organizational structures, particularly relevant to the digital transformation of production systems. Prof. Mieke teaches courses in Supply Chain Management, Technology and Innovation Management, Production and Materials Management, Value Creation Management, and Technology Management. His teaching responsibilities align closely with his research interests, emphasizing the practical application of theoretical concepts in business administration, particularly in innovation, technology, and production management.
Professor Jörg Hähner holds the Chair of Organic Computing at the University of Augsburg's Faculty of Applied Computer Science within the Institute of Computer Science. He leads a research team focused on evolutionary computation, self-organizing systems, and intelligent computing approaches. His educational background includes computer science studies at TU Darmstadt. His academic career progression shows steady advancement in the field of organic and self-organizing computing systems. Prof. Hähner's research spans multiple interconnected domains in computational intelligence. His primary focus is on Organic Computing, which involves developing systems that can adapt and self-organize in complex environments. Within this framework, he has made significant contributions to Evolutionary Algorithms, particularly Cartesian Genetic Programming and Learning Classifier Systems. His work explores how these techniques can be applied to real-world problems such as predictive maintenance, energy systems optimization, and industrial automation. The research demonstrates a strong emphasis on both theoretical foundations and practical applications of self-adaptive systems. An analysis of his recent publications reveals a strong concentration on evolutionary computation techniques, particularly Cartesian Genetic Programming variants and Learning Classifier Systems. His research group has been actively developing frameworks like CRust_GP and GRAHF to advance modular construction of evolutionary algorithms. There's a clear trend toward applying these techniques to industrial problems including predictive maintenance, resource allocation in networks, and energy management systems. The publications show consistent exploration of fundamental questions about algorithm behavior while maintaining strong connections to practical applications. Prof. Hähner leads an active research group with numerous PhD students and collaborators, including Karen Poloczek, Henning Cui, Victor Gerling, Dr. Michael Heider, Marco Hüller, Neele Kemper, Helena Stegherr, Jonathan Wurth, and Roman Sraj. His team regularly publishes in top-tier conferences and journals in evolutionary computation, intelligent systems, and industrial applications. The Organic Computing research group maintains a strong presence in both theoretical and applied research, with projects spanning from foundational algorithm development to industrial applications in manufacturing, energy systems, and network optimization. The group's work demonstrates a cohesive research vision centered on creating adaptive, self-organizing computational systems that can operate effectively in complex real-world environments.
Prof. Dr.-Ing. Achim Kampker is a faculty member at RWTH Aachen University , serving as the Chair of Production Engineering of E-Mobility Components and Head of Battery Technology & Life Cycle . He is affiliated with the Production Engineering Laboratory (PEM) at Bohr 12, Aachen (52072). Kampker specializes in production engineering, battery technology, and electric mobility, with a focus on sustainable manufacturing systems, fuel cell applications, and life cycle assessment of energy storage solutions. His work integrates Industry 4.0 principles into factory planning and production data science. His research themes include: Battery manufacturing optimization, recycling, and supply chain resilience Electric motor design and thermal management systems Simulation-based process control and defect detection in production Data-driven approaches to second-life battery applications Recent publications analyze fuel cell truck economics, solid-state battery modeling, sodium-ion recycling challenges, and AI applications in battery production. He has contributed to standardization efforts in battery manufacturing and explores dynamic capability frameworks for electric mobility solutions. Contact: a.kampker@pem.rwth-aachen.de
