Dr. Goratz Beobide Arsuaga is a Research Scientist in Climate Modelling at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences. His research focuses on climate variability, extreme weather events, and predictive modeling, particularly concerning European heatwaves and Atlantic climate systems. Research Interests: Specializes in climate dynamics with emphasis on heatwave mechanisms, ocean-atmosphere interactions, and climate model development. Current investigations include precursor signals for extreme events and decadal climate predictability. Publication Trends: Research demonstrates strong focus on atmospheric dynamics and climate extremes, with recent work advancing heatwave prediction through sea surface temperature analysis and model ensemble techniques. Articles show consistent methodological innovation in statistical climate analysis.
Prof. Tobias Knopp is a Professor of Experimental Biomedical Imaging at Hamburg University of Technology (TUHH) and the University Medical Center Hamburg-Eppendorf (UKE). He leads the Institute for Biomedical Imaging and serves as Editor-in-Chief of the International Journal on Magnetic Particle Imaging (IJMPI). His expertise spans tomographic imaging, image reconstruction, and signal processing with a focus on Magnetic Particle Imaging (MPI). Education: Diplom in Computer Science (University of Lübeck, 2007) PhD in Biomedical Imaging (University of Lübeck, 2010) awarded the Klee Prize (2011) Research Interests: Tomographic imaging methods, MPI system development, model-based reconstruction, and hardware optimization. His work emphasizes MPI applications in clinical imaging and tracer development. Awards: Klee Prize from DGBMT (2011) for groundbreaking MPI research. Key Contributions: Pioneered the first commercial MPI system, developed open-source reconstruction frameworks (e.g., MRIReco.jl), and advanced MPI for real-time clinical applications. His research bridges theoretical models with practical hardware implementations.
Stephan Hartmann is Chair of Philosophy of Science and Alexander von Humboldt Professor at Ludwig-Maximilians-Universität München (LMU Munich), where he also serves as Head of the Munich Center for Mathematical Philosophy. His academic journey spans multiple prestigious institutions across Europe, establishing him as a leading figure in contemporary philosophy of science. Hartmann received his academic training at Justus-Liebig University Giessen, where he completed a Diploma in Physics (1991), a Master in Philosophy (1991), and a PhD in Philosophy (1995). Prior to joining LMU Munich in 2012, he taught at Tilburg University, the London School of Economics, and the University of Konstanz, with visiting appointments at the University of California at Irvine, Lund University, and the Center for Philosophy of Science at the University of Pittsburgh. Hartmann's research program centers on the application of formal methods to philosophical problems, particularly through Bayesian frameworks. His work bridges philosophy of science, philosophy of physics, formal epistemology, social epistemology, and cognitive science. He has developed influential models for understanding reasoning, argumentation, deliberation, and scientific explanation using probabilistic and mathematical approaches. His current research focuses on the philosophy and psychology of reasoning and argumentation, the philosophy of physics (especially open quantum systems), and formal social epistemology (particularly models of deliberation). His extensive publication record reveals a consistent trajectory of applying formal methods to philosophical questions, with increasing emphasis on Bayesian approaches across multiple domains. Hartmann's work demonstrates remarkable integration of mathematical precision with philosophical depth, particularly in addressing traditional problems in epistemology and philosophy of science through computational and probabilistic frameworks. His research shows strong continuity in exploring how formal models can illuminate fundamental questions about scientific reasoning, evidence, and rationality. Alexander von Humboldt Professor President of the European Philosophy of Science Association (2013-2017) President of the European Society for Analytical Philosophy (2014-2017) Hartmann leads the Munich Center for Mathematical Philosophy, which serves as a hub for interdisciplinary research at the intersection of philosophy, mathematics, and formal methods. Under his direction, the center has become a prominent institution for advancing mathematical approaches to philosophical problems, fostering collaborations between philosophers, mathematicians, physicists, and cognitive scientists. His work has significantly influenced how formal methods are applied to traditional philosophical questions, particularly in epistemology and philosophy of science.
Prof. Manfred Reichert is a full professor at the University of Ulm, serving as Director of the Institute of Databases and Information Systems and Dean of Studies at the Faculty of Engineering and Computer Science. He holds dual expertise in Computer Science and Mathematics, with academic leadership roles including chairing examination boards and strategic research initiatives. His interdisciplinary affiliations include co-opted membership in the Faculty of Mathematics and Economic Sciences. Reichert's research focuses on Business Process Management (BPM), IoT-driven processes, service-oriented architectures, and e-health applications. Notable contributions include co-developing the ADEPT process management system and pioneering object-centric process modeling. He has led over 20 international research projects (EU FP7, DFG, Industry) and authored >200 peer-reviewed papers, earning an h-index of 60+ and prestigious awards like the Merckle Forschungspreis. Education: PhD in Computer Science & Mathematics Diploma Leadership: Former associate professor at University of Twente, former director of CTIT research center Service: Conference chair for BPM, CoopIS, EDOC; steering committee member for GI SIG Databases Recent work explores IoT integration in BPM, generative AI for process models, and neuroscience-informed usability studies. His research bridges technical innovation with human-centric design, addressing challenges in process lifecycle management, federated learning, and mobile health interventions. Awards: IFIP TC2 Manfred Paul Award, doIT Software Award Key Projects: BPMN extensions for IoT, data-driven healthcare solutions
Armin Feldhoff is an Extraordinary Professor (apl. Prof.) at the Faculty of Natural Sciences of the Leibniz University Hannover , leading the Thermo-Iono-Electronic Materials and Microstructure Analysis Group within the Institute of Physical Chemistry and Electrochemistry . He has held this position since 2012 and also serves as Department Student Advisor for the Chemistry M.Sc./M.Ed. program. Academic Career : Habilitation in Physical Chemistry (2009), Leibniz University Hannover Ph.D. in Physics (1997), Martin Luther University Halle-Wittenberg Diploma in Physics (1994), Westfälische Wilhelms-University Münster His research focuses on thermoelectric materials , mixed ionic-electronic conductors , and oxygen transport membranes , with expertise in high-resolution electron microscopy (HRTEM, EFTEM, STEM-HAADF) and X-ray diffraction . He has developed advanced ceramic composites for energy harvesting and CO2 conversion systems, emphasizing microstructure engineering and material sustainability . Recent publications highlight trends in: Textured and asymmetric ceramic membranes Electrospun nanoribbons for thermoelectrics Spark plasma sintering/texturing techniques Microemulsion-based synthesis Hydrogen-tolerant oxygen transport systems Mixed-phase stability analysis Scientific awards include the ACerS Global Ambassador (2022), DT Rankin Award (2022), and Luther Medal (1998). He serves on editorial boards for the Journal of the American Ceramic Society , Entropy , and Energy Harvesting and Systems .
Dennis Dreiskämper is Professor of Sport and Exercise Psychology at TU Dortmund University's Faculty of Arts and Sports Sciences since August 1, 2024. Previously, he served as study program coordinator at the University of Münster's Institute of Sports Science (2017-2024), becoming a senior lecturer in higher education in 2020. His career includes research positions at Münster's Department of Sport Psychology (2011-2017) and international stays at Kingston University London (2014) and Deakin University Melbourne (2017). His academic credentials include: 2022: Habilitation with Venia Legendi for Sports Science, University of Münster 2015: PhD (Dr. phil.) in Sports Science, University of Münster (DFG Research Training Group Trust and Communication in a Digitized World) 2011: Master of Arts and first state examination in sports and Latin (third subject history), University of Münster 2009: Bachelor of Arts in Sport and Latin (third subject History), University of Münster Dreiskämper's research centers on physical self-concept development, childhood health through physical activity, psychosocial aspects of exercise, and physical activity interventions. He investigates motor and psychosocial development trajectories in children and adolescents, develops motor diagnostics tools, and creates assessment instruments for physical self-concept. His work addresses trust dynamics in sports teams and anti-doping initiatives within sports associations, with particular focus on questionnaire development and measurement validation. His extensive publication record demonstrates strong focus on team cognition in sports, anti-doping legitimacy, talent selection processes, and pandemic impacts on youth physical activity. Dreiskämper employs innovative methodologies including virtual reality assessments of shared mental models and cross-cultural comparisons between German and Australian children. His work frequently bridges traditional sports psychology with emerging esports research. Professional affiliations include: Member of the German Sports Youth (dsj) research network since 2022 Member of the NRW Children and Youth Sport Research Network since 2019 His research has practical applications for sports clubs, educational settings, and public health initiatives focused on youth development through physical activity. The 'Move for Health' project represents a significant collaborative effort generating findings on mental health and physical activity among German children and adolescents.
Prof. Hans-Joachim Wunderlich is a Professor at the University of Stuttgart's Institute of Computer Architecture and Computer Engineering, within the Faculty of Computer Science, Electrical Engineering, and Information Technology. His research focuses on hardware reliability, fault tolerance, and testing methodologies for VLSI circuits and embedded systems. He specializes in areas such as delay fault testing under PVT variability, approximate communication, and aging-aware design. Key research interests include robust testing techniques for small delay faults, error-tolerant communication protocols (e.g., RAPPER and Gray code-based approaches), and GPU-accelerated simulation of faults. His work addresses challenges in functional safety, interconnect reliability, and securing reconfigurable architectures against security violations. He explores energy-efficient iterative solvers for approximate computing environments and develops methods for predicting device aging and early life failures. Prof. Wunderlich’s contributions span academic and industrial applications, with a focus on real-time systems and dependable multi-processor systems-on-chip (MPSoCs). His research integrates formal verification, machine learning for defect detection, and hybrid protection schemes for reconfigurable scan networks. Recent work emphasizes stress-aware testing strategies and optimization of sensor data streaming in resource-constrained environments.
Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.
Peter Zahn is a Science Manager for Nanoelectronics and Coordinator of the Research School NanoNet and HZDR Career Center at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). He holds the academic title of PD Dr. habil., reflecting his expertise in advanced materials research. His primary affiliation is within the Institute of Ion Beam Physics and Materials Research , specifically leading the Nanoelectronics FWIO department. Zahn’s work focuses on nanomaterials, thermoelectrics, and spintronics, with a strong emphasis on computational modeling and experimental characterization of electronic and thermal transport phenomena in novel materials systems. His research interests span nanoelectronic devices, including molecular junctions and nanowires, as well as the thermoelectric properties of layered materials and superlattices. Zahn has pioneered studies on the electrical and mechanical behavior of germanium nanowires and the adsorption dynamics of organic molecules on metallic surfaces. His contributions to understanding spin-dependent transport phenomena, including the extrinsic spin Hall effect and magnetoresistance in metallic systems, are also notable. Key trends in his articles include advancements in nanomaterial fabrication techniques, computational simulations of molecular and electronic systems, and the optimization of thermoelectric materials for energy conversion. His work bridges theoretical predictions with experimental validation, often employing first-principles calculations and atomistic modeling. Zahn’s coordination roles involve fostering interdisciplinary collaborations through the NanoNet research school and supporting early-career researchers via HZDR’s Career Center. He has contributed to annual reports highlighting the institute’s achievements in ion beam physics and materials innovation. His research infrastructure includes state-of-the-art nanofabrication facilities and advanced characterization tools at HZDR, enabling cutting-edge studies in nanoelectronics and materials science.
Dr. Christian R. Wick is a Principal Investigator and Coordinator of the EAM Unit Computational Advanced Materials and Processes (CAMP) at Friedrich-Alexander-University Erlangen-Nürnberg. He holds a Dr. rer. nat. in theoretical physics and focuses on multiscale modeling of materials, particularly in mechanochemistry, catalysis, and polymer networks. His research integrates computational methods like molecular dynamics and DFT to study reaction mechanisms in materials science and enzymology. He completed his education at FAU with a B.Sc. (2009), M.Sc. (2011), and Ph.D. (2015) in Molecular Science and Theoretical Physics. His work bridges theory and experiment, addressing challenges in low-temperature catalysis (e.g., water-gas shift reactions) and functional materials design. Notable contributions include advancements in SILP catalysts, epoxy resin modeling, and mechanochemical reactivity prediction. Wick has been recognized with the Lecture Award at the 28th Molecular Modelling Workshop (2014). His research outputs span over 28 publications, with key topics including ionic liquid behavior, polymer cross-linking dynamics, and computational enzyme modeling. He collaborates widely, contributing to projects like the GRK 2423 FRASCAL initiative on fracture mechanics across scales. Wick’s interdisciplinary approach involves teams in materials science, catalysis, and computational physics, with ongoing projects on advanced polymer materials, mechanochemical reaction engineering, and enzyme activity modeling. His lab (CAMP) emphasizes innovative methodologies for simulating complex material behaviors under mechanical and thermal stresses.
Barbara Kirchner is a Professor of Theoretical Chemistry at the University of Bonn. Her research focuses on the structural and dynamic properties of ionic liquids, employing advanced computational techniques such as ab initio molecular dynamics and cluster analysis. She explores applications in energy storage, electrochemistry, and environmental science, including drug interactions with nanoplastics and solvation phenomena in novel solvents. Her work also addresses methodological challenges in simulations and data reliability, contributing to tools like TRAVIS and CONAN for analyzing molecular trajectories in confined spaces. Her research interests span the theoretical and computational study of ionic liquids, with emphasis on their behavior in confined environments, interfacial effects, and interactions with other molecules. She investigates topics like proton transfer mechanisms (Grotthuss diffusion), catalytic processes, and the environmental implications of nanoplastics. Methodological contributions include refining cluster weighting algorithms and quantum equilibrium theories to predict thermodynamic and spectroscopic properties accurately. Barbara Kirchner has not explicitly mentioned scientific awards in the provided texts. Her recent articles highlight trends in understanding ionic liquid electrolytes, drug-nanoplastic interactions, and the development of simulation tools. She collaborates on interdisciplinary projects, such as solvent design for organic reactions and the study of magnesium battery systems, reflecting her commitment to bridging theory and practical applications in chemistry and materials science.
Benedict Herzog is a Researcher at the Bochum Operating Systems and System Software (BOSS) group at Ruhr-Universität Bochum (RUB). Previously, he was part of the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on energy-efficient systems, operating systems optimization, and embedded computing. Herzog holds a Master's degree in Computer Science from FAU, where his thesis explored energy demand estimation using artificial neural networks, and a Bachelor's degree in Energy-aware error correction in wireless sensor networks. His research interests span energy-aware computing, system software design, and machine learning applications for optimizing edge and embedded systems. He actively participates in academic activities, serving as a reviewer for conferences like TECS, SBESC, and EMSOFT. Herzog has taught courses such as 'Systemnahe Programmierung in C' and 'Energy-Aware Computing Systems' at FAU, and advised multiple students on topics ranging from interrupt handling overhead to Meltdown/Spectre mitigation impacts. Key technical contributions include frameworks for energy measurement integration (EnergyBudgets), system-call aggregation (AnyCall), and automated OS configuration optimization. His work bridges hardware-software co-design challenges in energy efficiency, with applications in edge computing and real-time systems.
Ali Sina Safari is a Researcher affiliated with Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), contributing to interdisciplinary projects such as the Bavarian State Ministry-funded research on endometriosis diagnostics and treatments. His work bridges network theory, biological systems analysis, and materials science. Research Interests: Exploring hierarchical network structures in biological systems, including brain connectivity and materials science. Developing graph-theoretical methods to analyze damage in hierarchical materials and functional brain networks. Investigating topological dimensions' impact on activity patterns in modular networks. Publications highlight themes like network modeling in biological systems, thermodynamic analysis of distillation units, and probabilistic graphical models for brain connectivity extraction from fMRI data. He is actively engaged in FAU's research community, contributing to projects at the intersection of computational biology, materials science, and theoretical physics.
Dr. David Peacock is a researcher affiliated with the Department of Structural Geology and Geothermics at the University of Göttingen, Germany. He is a member of the MEET group and focuses on structural geology, geothermics, and fracture analysis. His work integrates field observations, geophysical data, and theoretical models to study fault systems, geothermal reservoirs, and fluid flow dynamics. Research interests include fracture mechanics, fault network evolution, and the application of structural analysis to geothermal exploration. He has contributed to studies in the Harz Mountains and other regions, emphasizing the interplay between tectonics, fluid dynamics, and subsurface processes. Dr. Peacock’s methodologies involve high-resolution seismic analysis, Mohr diagram stress modeling, and virtual outcrop databases. His publications span structural terminology, fracture sequencing, and geothermal reservoir characterization. Collaborations with the MEET group highlight interdisciplinary approaches to energy and subsurface challenges.
Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI), Germany. He holds a joint appointment, bridging academic research and industrial application in artificial intelligence. He is a Fellow at BIFOLD and ELLIS Unit Berlin, and a Principal Investigator in several DFG projects including DeSBi and BIOQIC. PhD (Dr. rer. nat.) with distinction, Technical University of Berlin, 2014 Studies in Computer Science, Humboldt University, Heriot-Watt University, University of Edinburgh His research centers on Explainable AI (XAI) , Trustworthy Deep Learning , and Efficient AI . He pioneered Layer-wise Relevance Propagation (LRP), a foundational method for interpreting deep neural networks. His work spans model interpretation, robustness against adversarial attacks, neural network compression, federated learning, and applications in healthcare, communications, and multimedia. The recent articles highlight a strong trend toward extending explainability beyond deep models , with research on concept-level explanations, unsupervised learning interpretability, and XAI-driven model improvement. There is a consistent focus on robustness, privacy, and efficiency in distributed learning settings, particularly for federated and edge AI. Applications in medical AI and neuroscience are prominent, emphasizing safety and interpretability in high-stakes domains. Best Paper Award, Pattern Recognition (2020) Digital Signal Processing Best Paper Prize (2022) Fellow, BIFOLD - Berlin Institute for the Foundation of Learning and Data Member, Germany's Platform for Artificial Intelligence Senior Editor, IEEE TNNLS; Associate Editor, Pattern Recognition, Digital Signal Processing, PLoS ONE Area Chair, NeurIPS, ICML, NAACL Program Chair, IEEE MLSP 2023 Contributor to ISO/IEC MPEG-17 NNC standard Prof. Samek advises a large group of PhD students and leads a vibrant research team at Fraunhofer HHI and TU Berlin. His group has secured significant funding through DFG, BIFOLD, and industrial collaborations. He has co-authored over 200 peer-reviewed papers, many of which are ESI Hot or Highly Cited. He is deeply involved in organizing workshops and tutorials on XAI, federated learning, and neural compression at top venues like NeurIPS, ICML, CVPR, and IEEE conferences. His lab develops open-source tools such as the LRP Toolbox , Keras Explanation Toolbox , DeepCABAC , and Quantus , promoting reproducibility and adoption of interpretable and efficient AI methods. The team is actively working on next-generation AI that is not only accurate but also transparent, robust, and trustworthy.
