Dragan Jankovic is a prominent researcher at the University of Niš, Faculty of Electronic Engineering , Department of Computer Science and Informatics. His work spans multiple disciplines with a focus on Medical Information Systems , IoT for Healthcare , and Multi-Valued Logic applications. Collaborating extensively with researchers like Petar Rajkovic and Aleksandar Milenkovic, Jankovic has contributed to the evolution of resource-aware systems, software development methodologies, and digital logic optimization.
Stefan Leue is a Professor for Software and Systems Engineering at the Department of Computer and Information Science at the University of Konstanz since 2004. He serves as a member of the Extended Directorate of the Centre for Human | Data | Society (elected in 2022) and is part of the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour since 2018. His academic background includes a doctorate from the University of Bern (1995) and prior professorial positions at the University of Freiburg (2000-2004) and University of Waterloo (1995-2000). Leue's research focuses on formal methods in software engineering, particularly in the design and analysis of complex systems. His expertise spans embedded software systems, verification of AI-based software, automotive software engineering, causality analysis, and safety-critical systems. He leads several research projects including Neural Network Repair, Causality in Systems (QuantUM and CausCheck), SCADNet, TarTar, and DiRePro. His research group has produced significant work in model checking, directed search algorithms, real-time systems verification, and probabilistic system analysis. Current research trends emphasize the application of formal methods to AI safety, particularly in neural network verification and repair, as well as extending formal techniques to collective behavior modeling and safety-critical automotive systems. Steering Committee Member, SPIN Symposium on Model Checking of Software since 2007 Member, Cluster of Excellence Centre for the Advanced Study of Collective Behaviour since 2018 IEEE Computer Society member ACM member Gesellschaft für Informatik (German Informatics Society) Professor Leue has supervised numerous PhD and Master's students, many of whom now hold prominent positions in industry and academia. His research group maintains active collaborations with industry partners in automotive and safety-critical systems domains. Current work focuses on bridging formal methods with machine learning to address safety challenges in autonomous systems.
Felix Binkowski is a researcher at the Zuse Institute Berlin within the Modeling and Simulation of Complex Processes department and Computational Nano Optics group. His work focuses on computational methods for photonic systems and quantum technologies. Position: Researcher Email: binkowski@zib.de Research Interests include: modal analysis of nanophotonic devices, resonance phenomena in non-Hermitian systems, Purcell effect optimization for quantum emitters, and application of Riesz projections to eigenvalue problems. His projects span from theoretical developments to experimental validation of optical materials. Key methodologies: AAA rational approximation, Riesz projections, Gaussian process optimization Application areas: photovoltaics, nanolasers, plasmonic systems Recent Publications (2024-2025) demonstrate expertise in: computational resonance extraction, pole-zero analysis of photonic systems, and uncertainty-guided design optimization. Notable works include software frameworks for resonance expansion (RPExpand) and studies on Purcell enhancement in 2D material-based nanoresonators. Education includes a doctoral degree (2023) from Freie Universität Berlin under Christof Schütte, and a Master's (2017) from Technische Universität Berlin with advisors Jörg Liesen and Martin Weiser.
Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.
Yulan He is an active researcher in Natural Language Processing and Computational Linguistics with numerous publications in top-tier conferences including ACL, EMNLP, and COLING from 2023-2025. Their work spans both theoretical advancements in Large Language Model architectures and practical applications in healthcare, social media analysis, and information retrieval. Research interests focus on Large Language Model optimization , including improving faithfulness in rationale generation, enhancing reasoning capabilities, personalizing outputs to user preferences, and optimizing computational efficiency. Significant contributions include frameworks for debiasing opinion summarization, improving depression detection in clinical interviews, and developing methods for Theory-of-Mind reasoning in LLMs. Their work addresses critical challenges in LLM reliability, interpretability, and efficiency. Analysis of recent publications reveals consistent focus on bridging the gap between theoretical LLM capabilities and practical applications , with particular attention to healthcare contexts, social media analysis, and complex reasoning tasks. Their research demonstrates how to make LLMs more reliable, efficient, and aligned with human needs across diverse domains. Scientific contributions include: Novel frameworks for LLM faithfulness and reasoning (Drift, EnigmaToM) Efficient inference methods (SCOPE, PECAN) Bias mitigation techniques (LASS, Rehearse With User) Personalization approaches (PROPER) Healthcare applications (Explainable Depression Detection) As evidenced by senior authorship positions across numerous publications, Yulan He leads research projects and likely supervises graduate students in NLP research. Their work demonstrates strong technical expertise combined with practical problem-solving approaches to real-world NLP challenges.
Francois Le Gall is a Professor at Nagoya University's Graduate School of Mathematics, where he leads the Quantum Algorithms and Complexity Group. His research focuses on quantum computation, complexity theory, and distributed algorithms. Research Interests: Quantum algorithms (including Grover's algorithm optimizations and Shor's algorithm generalizations), quantum complexity theory, communication protocols, and distributed computing frameworks. His work bridges theoretical computer science and practical quantum implementations. Publications: Recent articles explore quantum interactive proofs, distributed quantum advantage, and dequantization techniques. A consistent focus is observed on overcoming computational limits in quantum systems and establishing complexity boundaries. Awards: NISTEP Award 2017 ISSAC 2014 Distinguished Paper Award Teaching & Advising: Teaches courses in Linear Algebra, Quantum Computing, and Mathematical Perspectives. Mentors 11+ PhD/Master's students in quantum algorithms and complexity. Leads the QLEAP-AI project since 2020. Group Leadership: Heads a research team developing quantum algorithms for graph problems, matrix operations, and communication complexity, with collaborations across global institutions.
Jin Zhang is a Researcher in the Department of Quantitative Methods at the Faculty of Business and Economics, University of Basel. He holds a PhD in Computational Finance from the University of Essex, UK, awarded under the European Commission's Marie Curie Actions program, and has academic backgrounds in Engineering and Applied Statistics from institutions in China and Australia. Education: Bachelor of Engineering, Beijing University of Posts and Telecommunications, China M.Sc. in Mathematics and Applied Statistics, University of Wollongong, Australia PhD in Computational Finance, University of Essex, UK (Marie Curie Actions grant recipient) Jin Zhang's research lies at the intersection of computational methods and financial modeling. His work emphasizes trading strategy design , portfolio optimization , and derivative pricing using advanced numerical and computational techniques. He applies methods such as Laplace transforms, finite difference schemes, clustering algorithms, and copula modeling to solve complex financial problems. His interdisciplinary approach bridges applied mathematics, statistics, and finance. The analysis of his publications reveals a consistent focus on enhancing financial decision-making through computational innovation. His work spans algorithmic option pricing, risk-aware portfolio construction, and the application of machine learning-inspired clustering in finance. A recurring theme is the use of numerical and heuristic optimization to improve accuracy and stability in financial models. Scientific Awards and Honors: Marie Curie Actions Grant, European Commission Jin Zhang has been actively involved in research projects supported by competitive funding, notably the EC’s Marie Curie program. While no formal advising roles are listed, his collaborations with researchers like Dietmar Maringer and Songping Zhu indicate active participation in academic research teams. His publications in journals such as Expert Systems with Applications and Applied Mathematics and Computation reflect strong technical contributions to quantitative finance. Laboratories and Research Teams: Jin Zhang is affiliated with COMISEF (Computational Optimization and Modeling for Financial and Economic Applications), a European research network focused on training and knowledge transfer in computational finance. He contributes to working papers and collaborative research under this network, indicating engagement with an international academic community.
Erika Ábrahám is a University Professor in the Department of Computer Science at RWTH Aachen University, Germany, where she leads the research group on the Theory of Hybrid Systems. Her work is centered on formal methods for cyber-physical systems, with a strong focus on hybrid and probabilistic systems, SMT solving, and symbolic computation. Her research interests include Formal Methods, Hybrid Systems, Probabilistic Systems, Satisfiability Modulo Theories (SMT), Symbolic Computation, Cyber-Physical Systems, Model Checking, Reachability Analysis, Automated Reasoning, Verification of Safety-Critical Systems, Robotics, Energy Optimization, and Artificial Intelligence . She develops theoretical foundations and practical tools for the analysis and verification of complex systems, especially in safety-critical domains like automotive and robotics. Her recent publications demonstrate a consistent focus on advancing SMT solving techniques, particularly in real algebra and cylindrical algebraic decomposition, and on analyzing probabilistic hybrid systems, including reachability and hyperproperties. She frequently contributes to and organizes major conferences and workshops in her field. Scientific Awards: No specific awards were mentioned in the provided text. Erika Ábrahám actively supervises students and researchers, with advisees including Jasper Kurt Ferdinand Nalbach, Valentin Maxim Promies, Stefan Schupp, and others. She has been involved in projects related to railway timetables, energy load control, and AI-based robotics. She also contributes to academic service through editing conference proceedings and promoting gender equality in software engineering. She is a key contributor to the HyPro library for hybrid systems reachability analysis and continues to publish in top venues such as LNCS, Springer, and Elsevier journals. Her recent work spans from core theoretical advances in SMT to applied research in sonar object detection and robotics.
Niklas Windhuis is a researcher in the Nonlinear Optimization Group at the Faculty of Mathematics, University of Duisburg-Essen. He works under the supervision of Prof. Dr. Arnd Rösch and is actively engaged in advancing theoretical and computational aspects of nonlinear optimal control problems. His research focuses on: No-gap optimality conditions for optimal control problems Algorithmic methods for solving semilinear optimal control systems Mathematical analysis of control constraints and regularity conditions The work contributes to the broader domain of applied mathematics and optimization theory, particularly in scenarios where classical optimality conditions fail to provide sufficient guarantees—addressing these 'gaps' through refined analytical frameworks. No scientific awards or honors are currently listed in the available information. Niklas Windhuis has not yet supervised any known students and there is no mention of grant leadership or external funding roles. However, his integration into an active research group suggests involvement in ongoing collaborative research projects. He is a member of the Nonlinear Optimization Group at the Department of Mathematics, University of Duisburg-Essen, contributing to the group's mission of developing rigorous mathematical tools for complex control systems.
Anastasia Bauer is a linguist and postdoctoral researcher at the Department of Linguistics, University of Cologne, serving as Junior Principal Investigator in the Research Center 'Skills and Structures in Language and Cognition' (SSLAC) and Principal Investigator for the DFG Priority Programme 2329 ViCom project 'Gestures or signs?' She earned her PhD in 2014 from the University of Cologne in Applied English Linguistics with specialization in Sign Language Linguistics. Bauer's research explores human language capacity through multimodal and cross-linguistic analysis of face-to-face interaction. Her primary focus areas include: Corpus Linguistics methodologies Sign Language Linguistics (German, Russian, Polish, Ukrainian) Multimodal Communication systems Gesture Studies in spoken and signed contexts Morphosyntactic structures Bimodal Language Contact phenomena Slavic Languages interfaces Her recent publications demonstrate methodological innovation combining pose estimation, corpus analysis, and cross-linguistic comparison to investigate feedback signals, head movements, and mouthings across sign languages. This work reveals systematic patterns in multimodal expressivity and phonetic differentiation within Slavic language contexts. Bauer actively contributes to DFG-funded research through SSLAC and her ViCom project, developing corpus-driven approaches to compare manual and non-manual constructions in co-speech gesture and sign language. Her work bridges linguistics, computer vision, and cognitive science through interdisciplinary collaboration. She maintains active research leadership within SSLAC and her DFG project team, advancing methodologies for multimodal data collection and analysis while fostering international scholarly exchange in sign language and gesture studies.
Tobias Körner is a Scientific Assistant and Research Associate at the Department of Microwave Systems within the Faculty of Electrical Engineering and Information Technology at Ruhr University Bochum. He focuses on advanced research in microwave and millimeter-wave technologies, THz systems, and radar applications. His work addresses challenges in high-frequency signal propagation, antenna design, and sensor integration for communication and sensing systems. His research interests include optimizing radar performance in challenging environmental conditions, developing novel THz true time delay mechanisms, and creating efficient frameworks for millimeter-wave measurement setups. He collaborates on projects like KI-ROJAL and 6GEM, advancing 6G communication and robotic antenna calibration. Recent publications highlight contributions to FMCW radar-based SLAM algorithms, environmental impact studies on sub-THz signals, and ultrawideband robotic antenna measurements. His work bridges theoretical microwave engineering with practical applications in autonomous systems and next-generation communication networks.
Mina Abadeer is a Researcher affiliated with the University of Münster, Germany. She is based in Room 710 at Einsteinstraße 62, 48149 Münster. Her work focuses on agent-based modeling, disaster management simulations, and machine learning integration into complex systems. Key projects include contributions to the CrowdSim system for crowd behavior analysis and the PACXX framework for parallel computing. She actively participates in teaching and research activities, including courses on distributed systems and interactive simulations. Her research interests emphasize the practical application of computational models to real-world challenges, such as pandemic response, evacuation planning, and network optimization. She has published widely on topics like QoS-aware routing mechanisms in SDN networks and interactive simulation frameworks for disaster recovery scenarios. Her work bridges theoretical computer science with applied systems engineering, addressing both technical and societal challenges through interdisciplinary approaches.
Mohamed I. Ibrahem is an active academic researcher specializing in cybersecurity applications for smart grid infrastructure and Internet of Things systems. His work bridges computer science, electrical engineering, and artificial intelligence with a particular focus on privacy-preserving techniques and secure machine learning applications. Dr. Ibrahem's research interests center on smart grid security , where he has developed innovative approaches for electricity theft detection, false data injection prevention, and privacy-preserving monitoring systems. His work in federated learning security addresses critical vulnerabilities in distributed machine learning systems, particularly against Trojan attacks and evasion techniques. Additional research areas include explainable AI applications in healthcare diagnostics, IoT security mechanisms, and deep learning optimization for resource-constrained environments. His recent publication trends reveal a growing emphasis on practical security implementations for real-world energy infrastructure, with increasing attention to explainability and robustness in AI-driven security systems. The interdisciplinary nature of his work connects cybersecurity principles with specific domain challenges in power systems and healthcare applications. Dr. Ibrahem has secured research funding for multiple projects focused on smart grid protection systems and privacy-preserving machine learning frameworks, though specific grant details are not visible in the publication metadata. His collaborative research network spans multiple international institutions with strong connections to Middle Eastern universities.
Dr. Philipp Warode is a research assistant at the Faculty of Economics, Humboldt University of Berlin, specializing in AI and network optimization. His work bridges computational economics, algorithm design, and operations research. Education: PhD in Mathematics (2022), TU Berlin His research focuses on parametric optimization of network flows, congestion games, and scheduling algorithms, with notable projects like Unsplittable Flows and collaborations with Martin Skutella and Max Klimm. Recent publications address atomic splittable congestion games, non-clairvoyant scheduling, and queuing system optimization. Teaching: Regular instructor of Mathematics I/II lectures and preparatory courses for bachelor/master students since 2019. Contact: philipp.warode@hu-berlin.de | Room 325, Spandauer Straße 1, 10178 Berlin | ORCID: 0000-0002-2878-6872
Malte von Scheven is a Senior Researcher and Deputy Director at the Institute of Structural Analysis and Dynamics at the University of Stuttgart. He holds a Dr.-Ing. degree (2009) and specializes in adaptive structures, fluid-structure interaction, and computational mechanics. Research Focus: Redundancy matrices for structural assessment, high-performance computing, actuator placement optimization Teaching: Finite element methods, computational mechanics, nonlinear structural analysis Leadership: Deputy Director since 2006, conference organizer for ECCOMAS and SMART symposia His work bridges structural mechanics with bio-inspired design, including studies on sea urchin skeletons as models for segmented shells. He has supervised numerous theses on SFRP composites, topology optimization, and adaptive systems. Scientific Engagement: Published 15+ papers on redundancy matrices and FSI Organized mini-symposia at international conferences (ECCOMAS 2024, SMART 2023) Active in university governance through Faculty Council and TIK committee Recent research investigates mechanical modeling of adaptive structures, with applications in civil engineering and architectural geometry. His redundancy matrix framework provides novel performance indicators for robust design and assemblability assessment.
