Dr. Constantin Christof is a Lecturer (Akademischer Rat auf Zeit) at the Department of Mathematics , Technische Universität München , with prior roles as a W2 Stand-in Professor at Universität Augsburg and Research Associate at TUM and TU Dortmund. His research focuses on Optimal Control of PDEs , Variational Inequalities , and Nonsmooth Optimization , with applications in Non-Newtonian Fluids and Neural Networks . May 2015 - July 2018: Dr. rer. nat. in Mathematics, TU Dortmund Oct. 2013 - July 2014: MAST (Part III of Mathematical Tripos), University of Cambridge Oct. 2009 - Sept. 2012: B.Sc. in Technomathematics and Mathematics, TU Dortmund Christof's work bridges Finite Element Error Analysis , Sensitivity Analysis , and Physics-Guided Machine Learning , particularly in problems involving Contact Mechanics and Parabolic PDE Constraints . His recent publications address challenges in Semilinear Elliptic PDEs , Obstacle Problems , and Nonsmooth Superposition Operators , with a focus on theoretical and numerical advancements. Scientific awards include the Dissertation Award and Best Graduate Award from TU Dortmund, and the Award for Academic Excellence by the Minister President of North Rhine-Westphalia. He has supervised 11 theses at the Master's and Bachelor's levels, covering topics from Neural Network Surrogate Models to Bingham Fluid Simulations .
PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.
Lakhmi C. Jain is a distinguished academic affiliated with the University of South Australia. As a Professor, she has made significant contributions to the fields of Artificial Intelligence, Computational Intelligence, and Fuzzy Systems. Her research spans neural networks, decision support systems, robotics, and data analysis, with a focus on interdisciplinary applications. Her career includes over 445 publications, including books like Complex Networks in Software, Knowledge, and Social Systems (2019) and E-Learning Systems - Intelligent Techniques for Personalization (2017). She has held editorial roles in journals such as the International Journal of Intelligent Decision Technologies (IDT) and the Journal of Intelligent & Fuzzy Systems. Jain's work emphasizes practical applications of computational intelligence, including efforts in software development, biomedical signal processing, and multi-agent systems. She has collaborated extensively with researchers globally, contributing to advancements in AI-driven technologies and decision-making frameworks.
Prof. Dr. Sven Oliver Krumke is a Full Professor of Mathematics and currently serves as Dean of the Faculty of Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU). His research focuses on Combinatorial Optimization, Approximation Algorithms, Online Algorithms, Complexity, Algorithmic Game Theory, and Graph Theory with Applications. He leads the Optimization Group (AG Optimierung) within the Department of Mathematics. Education and Academic Path: PhD (Dr. rer. nat.) in Mathematics, University of Würzburg (1997) Habilitation in Mathematics, Technische Universität Berlin (2002), Thesis: 'Online Optimization: Competitive Analysis and Beyond' Professor at RPTU since 2004 Research Projects: Principal investigator in the Research Training Group (GRK 2982) 'MIMO: Mathematics of Interdisciplinary Multiobjective Optimization' Project lead for 'ONE PLAN: Optimization in Emergency Medical Services in Rhineland-Palatinate' Teaching includes courses on Analysis, Mathematical Modeling in Economics, and Reading Courses. He supervises Bachelor's and Master's theses in Optimization-related topics. He has held roles such as Scientific Researcher at the Konrad-Zuse-Zentrum Berlin (1998–2004), and has extensive international collaborations, including a Fulbright-funded stay at SUNY Albany (1992–1993). His work emphasizes practical applications in healthcare logistics, emergency services optimization, and algorithmic solutions for real-world problems.
G. Thippa Reddy is a prolific researcher with a focus on advanced technologies such as artificial intelligence, machine learning, and blockchain, particularly in healthcare, IoT, and cybersecurity domains. His work spans interdisciplinary areas including federated learning, edge computing, and smart city infrastructure. He has collaborated extensively with researchers like Praveen Kumar Reddy Maddikunta, Gautam Srivastava, and Mamoun Alazab, producing over 150 publications in high-impact journals like IEEE Access, IEEE Internet Things Journal, and IEEE Transactions on Industrial Informatics. His research emphasizes practical applications of AI in real-world scenarios, such as privacy-preserving medical systems, secure UAV networks, and inclusive education for individuals with disabilities. He explores cutting-edge topics like the Metaverse's role in Industry 5.0, blockchain-enhanced security frameworks, and the integration of large language models into intelligent transportation systems. Key contributions include frameworks for federated learning in healthcare, optimized routing protocols for underwater communications, and explainable AI (XAI) methods for industrial automation. His work often addresses challenges in scalability, privacy, and ethical deployment of emerging technologies.
Bennet Gebken is a researcher at the Department of Mathematics , Technical University of Munich , affiliated with the Chair of Mathematical Optimization led by Prof. Ulbrich. His work focuses on nonsmooth and multiobjective optimization, particularly in PDE-constrained problems and numerical continuation methods. Research Interests: Bennet specializes in nonsmooth optimization, multiobjective optimization, and regularization techniques. His recent work explores convergence analysis in nonsmooth settings, second-order gradient sampling, and inverse optimization methods for data-driven decision criteria. Publications: His research spans topics like PDE-constrained multiobjective optimization, L1 penalty terms, and the hierarchical structure of Pareto critical sets. Key trends include reduced-order modeling, computational efficiency, and the interplay between optimization and machine learning. Contact: bennet.gebken@tum.de . Based at Boltzmannstr. 3, Garching b. München, Germany.
Professor Ralf Werner serves as Professor of Business Mathematics at the University of Augsburg, where he leads the Computational Statistics and Data Analysis working group within the Institute of Mathematics at the Faculty of Mathematics, Natural Sciences and Technology. His academic career spans both theoretical research and practical industry applications in quantitative finance. Werner's research interests encompass: Computational Statistics and Data Analysis Optimization under Uncertainty Financial Engineering and Risk Management Actuarial Science and Insurance Mathematics Portfolio Optimization and Asset Allocation His scholarly output demonstrates a consistent focus on robust mathematical methods applied to financial problems, particularly in replicating portfolios for insurance applications, credit risk modeling, and statistical approaches to financial risk management. Werner's publications appear in leading journals across operations research, mathematical finance, and actuarial science. Professional qualifications include his habilitation at the Karlsruhe Institute of Technology (2011) and doctorate from Friedrich-Alexander University Erlangen (2001). He maintains active industry connections through his role as Scientific Advisor for DEVnet since 2010. Werner serves as Internship Coordinator and DAV (German Actuarial Society) correspondent, supporting students pursuing actuarial careers. He is an active member of multiple professional organizations including the Society for Operations Research (GOR), German Mathematical Society (DMV), and German Society for Insurance and Financial Mathematics (DGVFM).
Lakhmi C. Jain is a distinguished academic affiliated with the University of South Australia. They specialize in Artificial Intelligence, Neural Networks, Fuzzy Logic, and Intelligent Systems, with a focus on applications in robotics, data mining, and biomedical engineering. Their work often bridges theoretical advancements and practical implementations, contributing to fields like computational intelligence, decision-making systems, and multi-agent frameworks. As an editor for multiple journals, including the International Journal of Intelligent Decision Technologies, Jain has significantly shaped academic discourse in AI and related domains. Roles: Editor-in-Chief for several journals, researcher in AI and computational intelligence. Affiliations: University of South Australia. Research interests include neural networks, fuzzy logic systems, and their applications in robotics, biomedical signal processing, and smart technologies. Their publications emphasize interdisciplinary approaches to solving complex problems in engineering and computer science. Articles highlight contributions to multi-agent systems, decision support systems, and risk assessment models, reflecting a commitment to both theoretical rigor and practical relevance. Despite extensive contributions, no specific awards or student advisees are explicitly documented in the provided data.
Matthias Thiele is a Research Associate at the Institute of Precision Engineering and Electronic Design, Dresden University of Technology. His work focuses on electromigration-aware integrated circuit design, photonic integrated circuits, and mechatronic systems reliability. He leads projects funded by the DFG (Scalable Ising Machines in Silicon) and BMBF (Robust Mechatronic Systems Simulation). Research Associate, Dresden University of Technology DFG-funded project on CMOS-based photonic Ising machines BMBF-funded project on mechatronic system reliability His research spans electromigration analysis, finite element method applications, wear and aging effects, 3D-IC integration, and layout design optimization. Publications emphasize reliability challenges in future technologies and security implications of aging effects. Current projects analyze electromigration using advanced simulation techniques and explore photonic circuits for scalable Ising machines. Earlier work includes wear simulation tools and multiobjective optimization for 3D-ICs. Contact: matthias.thiele@tu-dresden.de
Adel Mhamdi is a Professor at RWTH Aachen University's Department of Chemical Engineering within the Faculty of Mechanical Engineering. His research focuses on process systems engineering with emphasis on nonlinear model predictive control, hybrid modeling, and sustainable process design for chemical and biochemical systems. He leads research in electrified biodiesel production, distillation optimization, and polymerization process control. His research interests span chemical process control, biodiesel production optimization, distillation modeling, hybrid mechanistic-data-driven approaches, polymerization processes, and heat transfer optimization. Mhamdi develops advanced control strategies including economic NMPC, distributed control architectures, and chance-constrained optimization to address challenges in flexible operation of energy-intensive processes. His work integrates computational fluid dynamics with process modeling for reactor design and optimization. Analysis of his recent publications reveals strong trends in electrification of chemical processes, particularly biodiesel production with heat integration. His research increasingly incorporates machine learning for hybrid modeling and employs advanced computational techniques like Bayesian optimization for reactor geometry design. Key application areas include renewable energy systems, polymer manufacturing, and separation processes. Mhamdi actively supervises doctoral researchers including M. El Wajeh, J.M. Faust, and P.J. Joy who appear as first authors on multiple publications. His research group collaborates extensively with the Mitsos group at RWTH Aachen, securing publications in top journals including Industrial & Engineering Chemistry Research and Computers & Chemical Engineering . Current projects focus on real-time optimization of electrified processes and development of open-source modeling platforms like HybridML.
Prof. Günter Rudolph is a Professor of Algorithmic Foundations and Education in Computer Science at the Technical University of Dortmund's Department of Computer Science. He leads Chair 11: Algorithm Engineering, focusing on Computational Intelligence (CI), Evolutionary Algorithms, and their applications in optimization and gaming. His research emphasizes theoretical analysis of CI methods, practical guidelines for operationalization, and applications in engineering, energy systems, and entertainment. Key research areas include multi-objective optimization, evolutionary robotics, and computational intelligence in games such as StarCraft and car racing simulations. He has advised numerous students on topics ranging from autonomous driving systems to procedurally generated game content. Rudolph collaborates internationally, including with CINVESTAV-IPN (Mexico) on multiobjective control methods and BMWi-funded projects on energy systems and forming process predictions. His work spans academic publications in journals like Genetic Programming and Evolvable Machines and conferences such as IEEE CIG. Notable projects include developing adaptive car racing controllers, optimizing energy supply systems in industrial parks, and advancing AI strategies in real-time strategy games. Rudolph's group actively contributes to game AI research through competitions like the StarCraft AI Competition and the Simulated Car Racing Championship, emphasizing the intersection of computational intelligence and entertainment.
Prof. Dr.-Ing. Christian Grimme is an Associate Professor and Extraordinary Professor in the Department of Information Systems at the University of Münster. He leads the Computational Social Science and Systems Analysis research group. His roles include acting professorships, research group leadership, and academic co-direction of the ERCIS Competence Center for Social Media Analytics. He holds a Dr.-Ing. in Computer Science and has extensive postdoctoral and habilitation experience. Education Timeline: 2015–2018: Habilitation and venia legendi in Information Systems 2006–2012: PhD in Computer Science (Dr.-Ing.) 1999–2006: Diploma in Computer Science Research Interests focus on Multiobjective Evolutionary Computation, Social Media Analysis, Disinformation Detection, and AI Ethics. His work bridges algorithmic innovation (e.g., optimization algorithms) with societal challenges (e.g., automated propaganda detection). Recent projects include analyzing Large Language Models' role in disinformation mitigation and real-time social media content analysis using human attention mechanisms. Awards include the Best Teaching Award (2024), PPSN XIV Best Paper Award (2016), and multiple travel grants from ACM and DAAD. He actively participates in conferences like GECCO and EMO, contributing to both theoretical and applied research. Advising and grants highlight his role in guiding over 20 theses, spanning Master's and Bachelor's projects in IS/WI. Notable grants include DAAD-funded collaborations and internal university funding for projects like MODERAT! (moderation tools) and ERCIS SMA Competence Center. Labs/Teams: Leads the Computational Social Science & Systems Analysis group, collaborating with global partners via ERCIS. Engages in initiatives like CLAIRE and the Integrity & Security Initiative to address AI ethics and information security challenges.
Gabriele Eichfelder is a full Professor at the Institute of Mathematics , Technische Universität Ilmenau, Germany, leading the Group for Mathematical Methods in Operations Research . She was recently elected as a EUROPT Fellow 2024 for her contributions to multiobjective optimization, joining an elite group of international laureates. Her research focuses on: Theoretical and numerical methods for multiobjective optimization Set optimization and set-valued analysis Non-linear and mixed-integer optimization algorithms Applications in medical engineering, energy systems, and mechanical engineering The EUROPT Fellowship follows her recent recognition as a Successful Woman in Mathematics by the JCW (AMS/SIAM), highlighting her leadership in applied mathematics. Scientific Awards : EUROPT Fellow 2024 Successful Woman in Mathematics (JCW/AMS/SIAM)
Dr. Agnes Steinert serves as Deputy Head of the Institute of Flight System Dynamics at the Technical University of Munich (TUM), where she holds the rank of Professor. Her work centers on advancing aerospace control systems through theoretical innovation and practical implementation in flight vehicles. Her research spans Flight Control, Flight Guidance, Nonlinear and Adaptive Control, Trajectory Optimization, Navigation Systems, and Flight Safety. She pioneers Incremental Nonlinear Dynamic Inversion (INDI) methodologies for robust aircraft control, with recent focus on electric vertical take-off and landing (eVTOL) systems, trajectory generation under dynamic constraints, and sensor-actuator synchronization challenges. Analysis of her 15 most recent publications (2022-2025) reveals a consistent trajectory toward safety-critical autonomous flight systems. Key themes include real-time trajectory optimization for eVTOLs, stability analysis of nonlinear controllers with actuator dynamics, and robustness enhancement through multiobjective parameter tuning—bridging theoretical control frameworks with aerospace operational requirements. No scientific awards were documented in the provided materials. Dr. Steinert actively mentors graduate researchers, having supervised over 20 theses covering airship/hydrofoil control, flight envelope protection, and adaptive controller design. Her leadership extends to experimental validation through TUM's flight test infrastructure, including collaborations on electric aircraft autonomy projects. The Institute of Flight System Dynamics maintains comprehensive research facilities including manned/unmanned aircraft, flight simulators, and GNC subsystems. Current infrastructure supports experimental work on trajectory optimization, sensor fusion, and safety-critical flight control validation for next-generation aviation systems.
Prof. Dr. Martin Middendorf is a faculty member at the Department of Computer Science , Faculty of Mathematics and Computer Science , Leipzig University , Germany. He leads the Swarm Intelligence and Complex Systems Group and focuses on interdisciplinary research at the intersection of computational methods and biological systems. Fields of Interest Swarm Intelligence Bioinformatics Genome Rearrangement Analysis Combinatorial Optimization Evolutionary Algorithms Task Allocation in Multi-Agent Systems His recent research emphasizes mitochondrial genome annotation , predator-prey dynamics in swarm systems , and metaheuristic algorithms for dynamic optimization . Key trends include de-Bruijn graph applications , pheromone-dependent movement modeling , and automated behavior tracking in social insects . Supervised Students Dr. Nicolas Wieseke Dr. Hoang Thanh Le Dr. Fatma Turna Tobias Jagla Carsten Seemann Prof. Middendorf's group develops tools like DeGeCI 1.1 for mitochondrial gene annotation and explores swarm-controlled emergence in ant clustering systems. They apply swarm intelligence principles to solve real-world problems in vehicle routing , sewer network design , and biomedical signal processing .
