Peter Palensky is a leading researcher in smart grids, power system cybersecurity, and cyber-physical systems. His recent work focuses on digital twins for power systems, quantum computing applications in energy analysis, and secure blockchain frameworks for distributed energy resources. He collaborates extensively with institutions across Europe, particularly in Dutch and Mongolian grid stability projects. Research areas include Smart grid resilience against cyber attacks Quantum-enhanced power flow analysis Electric vehicle grid integration (V2G) Machine learning for energy systems optimization High-voltage direct current (HVDC) security His publications emphasize practical implementations, such as hardware-in-the-loop testing for photovoltaic systems and real-time simulation models for energy storage. Recent articles explore large-scale synthetic data generation for grid analysis, dynamic tariff impacts on EV charging, and advanced control strategies for offshore MMC grids.
Flemming Dahlke leads the Junior Research Group in Ecology of Living Marine Resources at University of Hamburg's Institute of Marine Ecosystem and Fisheries Sciences. His work examines climate change impacts on aquatic ecosystems. Research focuses on: Thermal bottlenecks in fish life cycles Ocean acidification effects Species distribution shifts Sustainable aquaculture solutions Publications demonstrate integrative approaches combining experimental physiology, ecological modeling, and climate projections. Notable contributions include identifying critical thermal thresholds during early developmental stages.
Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with Faculty II – Mathematics and Natural Sciences and the Department of Mathematics. He leads the research group in Discrete Optimization and holds editorial roles at journals like the International Journal of Game Theory and Operations Research Forum . His academic journey includes a PhD in Mathematics from TU Berlin (2012), followed by roles as an Assistant Professor at Humboldt-Universität zu Berlin and Head of the Junior Research Group at the Einstein-Center for Mathematics. His research focuses on mathematical optimization, game theory, and mechanism design applied to multi-agent systems in traffic, telecommunications, and economics. Recent work addresses equilibrium computation in congestion games, stochastic optimization, and algorithmic challenges in network design. Key projects include Combinatorial Network Flow Methods for Gas Markets and the Math+ projects on mechanism design and evolutionary models for networks. Teaching responsibilities include courses on Discrete Optimization, Algorithmic Game Theory, and introductory mathematical courses. His research has been funded by DFG, Einstein Center, and Math+ initiatives. Notable contributions include advancements in parametric flow algorithms, impartial selection mechanisms, and reconstructing historical road networks using cost-benefit models.
Dr. Katharina M. Jörger is a researcher at Ludwig Maximilian University of Munich, specializing in the phylogeny and evolution of meiofaunal molluscs. Her work focuses on understanding the evolutionary relationships and ecological roles of small marine organisms, particularly within the Mollusca phylum. She holds a Ph.D. from LMU Munich, where her thesis explored molecular phylogeny of Acochlidia, a group of marine and freshwater gastropods. Her research integrates anatomical, genetic, and ecological approaches to address questions in systematic zoology and evolutionary biology. Key areas of expertise include meiofaunal molluscs, molecular systematics, and cryptic species identification. She has contributed significantly to the study of Aplacophora (Solenogastres) and Acochlidimorpha, elucidating their phylogenetic relationships and ecological adaptations. Her work often emphasizes the development of novel methods for studying small, elusive organisms, such as 3D microanatomy and hybridization-based genetic capture techniques. Dr. Jörger has conducted fieldwork in diverse locations, including the Azores, Antarctic regions, and the Caribbean, to document meiofaunal biodiversity. Her publications span topics like mitochondrial genome analysis, freshwater slug invasions, and deep-sea predation dynamics. She collaborates internationally on projects exploring meiofaunal ecology and evolutionary patterns. Her research also addresses broader ecological questions, such as how meiofauna contributes to marine biodiversity and how taxonomic methods can better capture cryptic diversity. Despite no explicitly listed awards, her prolific publication record underscores her impactful contributions to mollusc systematics and evolutionary biology.
Prof. Michael Beer is the Executive Director of the Institute for Risk and Reliability at Leibniz University Hannover. He holds a professorship in the Faculty of Civil Engineering and Geodetic Science and serves on the Faculty Council. His research focuses on structural reliability, uncertainty quantification, and risk analysis with applications in civil engineering systems. He leads the Collaborative Research Centres (CRC) 871 and 1463, addressing regeneration of complex capital goods and offshore megastructure design, respectively. His work integrates machine learning, Bayesian methods, and stochastic modeling to address challenges in seismic vulnerability, geotechnical systems, and reliability-based design optimization. Beer is also a member of the Leibniz Research Centre Energy 2050, emphasizing interdisciplinary energy systems research. Beer's research interests span probabilistic modeling of dynamic systems, uncertainty propagation in engineering systems, and data-driven methods for reliability assessment. His recent publications emphasize computational methods for reliability, machine learning applications, and seismic risk analysis. He actively contributes to academic leadership roles, including editorial boards and research center management.
Maribel Acosta is a Professor of Databases and Information Systems at Ruhr-Universität Bochum's Faculty of Computer Science. She holds affiliations with the Institut für Neuroinformatik (INI), focusing on interdisciplinary research combining natural and artificial cognitive systems. Her work spans databases, semantic web technologies, artificial intelligence, and machine learning applied to knowledge graphs. Acosta earned her Ph.D. (Summa Cum Laude, 2017) and Master's in Computer Science from Karlsruhe Institute of Technology (KIT). She has held roles including Junior Professor at RUB (2020–present), Deputy Professor at KIT (2019–2020), and Assistant Lecturer at Heidelberg University (2020). Research Interests: Her primary research focuses on efficient querying of knowledge graphs, integration of machine learning in data management, and semantic web technologies. Key areas include knowledge graph population, federated query processing, and stability in neural networks. Recent work explores applications in automotive systems and social science data integration. Awards: Notable recognitions include a 2022 WWW Best Paper nomination, Aminer's Top-100 Influential Scholars (2020–2021), and multiple teaching excellence awards from KIT (2017–2020). Teaching & Labs: She teaches courses on database systems, knowledge graphs, and artificial intelligence at RUB. Her lab contributes to projects like SMART-KG and has pioneered federated SPARQL query frameworks. She actively supervises doctoral research in knowledge graph applications and machine learning.
Prof. Zakhar Kabluchko is a faculty member at the University of Münster, affiliated with the Institut für Mathematische Stochastik and the Mathematics Münster cluster of excellence. His research focuses on stochastic processes, convex and integral geometry, and probabilistic number theory. He holds a professorship and has contributed to high-impact publications in areas like random polytopes, stochastic geometry, and extreme value theory. His research interests include the study of random analytic functions, stochastic processes in high dimensions, and geometric probability. Notably, he has explored beta-star polytopes, Poisson zero cells, and the interplay between convex hulls and random walks. Kabluchko has also investigated applications of stochastic geometry in statistical mechanics and number theory. Recent work includes studies on high-dimensional limit theorems, propagation of chaos in spin systems, and the geometry of random simplices. He collaborates actively with researchers like Christoph Thäle and Vladimir Vysotsky, contributing to advancements in geometric probability and stochastic analysis.
Julien Poisat is a Lecturer (equivalent to Assistant Professor) at CEREMADE, Paris-Dauphine University, where he has been affiliated since 2014. Previously, he was a postdoctoral researcher at Leiden University (2012-2014) and completed his Ph.D. at Lyon 1 University (2008-2012). His research focuses on probability theory and statistical mechanics, with specific interests in disordered systems, polymers, random walks, and large deviations. He investigates phenomena such as localization, pinning, and phase transitions in various models including copolymers, charged polymers, and random environments. His recent publications primarily explore rigorous analyses of stochastic systems, with recurring themes including large deviations for random walks, critical behavior of polymer models, and asymptotic properties of disordered systems. Research often involves mathematical techniques from renewal theory, potential theory, and weak convergence methods. He leads the ANR LOCAL grant (2022-2027) focused on localization phenomena in polymers and random walks. Currently advises two doctoral students: Nicolas Bouchot (2021-2024) and Elric Angot (2022-2025). Active in academic community, recently co-organized the 2023 Workshop on Random Walks, Localization and Reinforcement in Paris.
Dr. Setareh Maghsudi is a Professor in the Learning Technical Systems group at the Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum. She joined Ruhr-University Bochum in August 2023 after serving as an Assistant Professor at the University of Tübingen (2020-2023) and at the Technical University of Berlin (2017-2020). Her academic journey began with an M.Sc. from Kiel University (2008-2010), followed by her Ph.D. and postdoctoral work at Technical University of Berlin (2011-2015), Yale University (2016-2017), University of Manitoba (2015-2016), and Kyushu University (2019). Dr. Maghsudi's research focuses on the application of machine learning to communication networks and distributed systems, with particular emphasis on bandit algorithms, federated learning, and resource allocation in dynamic environments. Her work bridges theoretical machine learning with practical networking challenges, developing algorithms that can adapt to non-stationary environments with partial information. She has made significant contributions to multi-armed bandit frameworks for wireless communications, edge computing, and network optimization. Her recent publications (2023-2025) demonstrate a strong trend toward addressing challenges in integrated sensing and communication (ISAC), federated learning for edge networks, and non-stationary decision-making problems. The publications show expertise spanning theoretical machine learning foundations, wireless communications engineering, and practical implementation for real-world networked systems. Her work increasingly incorporates causal reasoning and robustness considerations into learning frameworks for communication systems. Dr. Maghsudi leads the Learning Technical Systems research group at Ruhr-University Bochum, where she supervises PhD students and postdoctoral researchers working at the intersection of machine learning and communication systems. Her research is supported by various grants focusing on AI for future communication networks. Current projects include developing AI-driven solutions for next-generation communication systems with emphasis on robustness, efficiency, and adaptability in dynamic environments.
Arne Grauer is a researcher in probability theory at the Department of Mathematics, University of Cologne. His work focuses on geometric random graphs, percolation, and stochastic processes in random environments. He completed his PhD in 2022 under Prof. Peter Mörters, exploring ultrasmallness and chemical distance in scale-free geometric random graphs. Education: PhD in Mathematics (2022, University of Cologne), Master’s in Mathematics (2017, University of Münster), Bachelor’s in Mathematics (2015, University of Münster). Research interests include understanding network structures and dynamics, particularly in scale-free and spatially embedded networks. His studies analyze ultrasmall graph distances, infection spread via contact processes, and preferential attachment models. Publications focus on mathematical physics and network science, with contributions to Communications in Mathematical Physics and Journal of Statistical Physics . He co-organized workshops on random geometric graphs and spatial networks.
Tatjana Wingarz is a Research Associate and PhD student in the IT-Security and Security Management (ISS) research group at the University of Hamburg's Department of Computer Science (MIN). She holds a Master's degree in IT-Security from Ruhr-University Bochum (2020) and a Bachelor's in Media Communication and Computer Science from Rhine-Waal University of Applied Sciences (2017). Her research focuses on Secure Machine Learning and Privacy-preserving Data Processing , addressing challenges in data integrity, cryptographic protocols, and network security. Recent publications highlight contributions to QUIC-aware load balancing, privacy-preserving data sharing, and edge computing middleware. Team & Collaborations: She collaborates closely with Prof. Mathias Fischer and colleagues like Dr. Heiko Bornholdt, Liliana Kistenmacher, and Kevin Röbert. Her work spans interdisciplinary projects in network security, functional encryption, and educational innovation in computer science pedagogy. Contact: tatjana.wingarz@uni-hamburg.de | Office F 624
Dr. Armin Nurkanović is an interim professor at the Technical University of Braunschweig's Department of Mathematical Optimization, where he teaches courses on dynamic optimization and numerical methods. Previously, he completed his PhD at the University of Freiburg under Prof. Moritz Diehl, focusing on optimal control of nonsmooth dynamical systems. His research emphasizes numerical methods for hybrid systems, real-time optimization, and applications in robotics and renewable energy systems. He has received the IEEE Control Systems Letters Outstanding Paper Award (2022) and was a finalist for the 2024 European Systems & Control PhD Thesis Award. Education: Bachelor's in Electrical Engineering (University of Tuzla, 2015) Master's in Electrical Engineering and Information Technology (Technical University of Munich, 2018) PhD in Control (University of Freiburg, 2023) Research Interests: Optimal control of hybrid and nonsmooth systems (e.g., Filippov systems, switched systems) Real-time optimization for model predictive control (MPC) Robust control theory and stochastic optimization Applications in robotics and renewable energy systems Teaching & Software: Developed open-source tools nosnoc and nosnoc_py for optimal control Teaching courses on numerical optimization and optimal control at TU Braunschweig Collaborations & Students: Open to academic and industry collaborations Supervises Bachelor's/Master's theses in mathematics, engineering, and computer science
Prof. Tim W. Dornis is a Professor at Leibniz University Hannover's Faculty of Law, holding dual affiliations with the German, European and International Civil and Commercial Law Section and the Institute of Legal Informatics. He serves as Deputy Representative for Professors in the Faculty Council, bridging legal academia and administrative governance. His research focuses on intellectual property law, artificial intelligence ethics, and private international law, with notable contributions to AI-generated content copyright issues, unitary patent systems, and digital market regulation. Dornis has authored over 50 publications since 2001, including works on FRAND licensing, standard-essential patents, and the legal implications of generative AI. He is also a qualified practitioner in private international law, addressing cross-border legal conflicts and jurisdictional challenges. His expertise spans doctrinal legal analysis, socio-economic perspectives on creativity rights, and the evolving intersection of technology with civil law frameworks. Research Interests: Intellectual Property Law and AI Unitary Patent System Challenges Unfair Competition in Digital Markets Private International Law Harmonization Recent Articles Highlight: Explores Generative AI's copyright implications , EU patent package ambiguities , and cross-border legal conflicts . Active in legal reform discussions around algorithmic accountability and automated contracting systems.
Prof. Dr. Peter Dietrich is a University Professor of Environmental and Engineering Geophysics at the Eberhard Karls University of Tübingen and Head of the Department of Monitoring and Exploration Technologies at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. He holds a joint appointment between the university and UFZ, reflecting his dual role in academic teaching and applied environmental research. Institution: Helmholtz Centre for Environmental Research - UFZ Department: Monitoring and Exploration Technologies Academic Affiliation: University of Tübingen Email: peter.dietrich@ufz.de His research spans environmental and engineering geophysics , with a focus on hydrogeophysics, geophysical monitoring, and exploration technologies for soil and groundwater systems. He applies methods such as electrical resistivity tomography, seismic techniques, and direct push sensing to study subsurface processes in ecological, archaeological, and environmental contexts. His work supports sustainable water management, pollution monitoring, and landscape preservation. The 15 most recent publications highlight a consistent trend in applied geophysics , particularly in non-invasive subsurface characterization , groundwater dynamics , and interdisciplinary environmental monitoring . These studies integrate field experiments, sensor networks, and modeling to address challenges in hydrology, geoarchaeology, and contaminant transport. Keywords across these works include geophysics, hydrogeology, remote sensing, and environmental monitoring, with subfields such as GPR imaging, nitrate plume analysis, peatland carbon storage, and 3D subsurface modeling. No specific scientific awards are listed in the provided text. Prof. Dietrich leads a research team and collaborates extensively with scientists across disciplines, including environmental engineers, hydrologists, and archaeologists. His leadership in the UFZ’s Department of Monitoring and Exploration Technologies underscores his role in advancing smart models and monitoring systems within the broader Helmholtz Research Program 'Changing Earth – Sustaining our Future'.
Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.
