Prof. Dr. Oliver Stein is a faculty member at the Karlsruhe Institute of Technology within the School of Business , specifically the Department of Operations Research . His research focuses on Continuous Optimization , Non-smooth Optimization , and Multiobjective Optimization , with applications in Operations Research , Game Theory , and Engineering Design . Stein has contributed extensively to semi-infinite programming , bilevel optimization , and mixed-integer nonlinear optimization . His work includes theoretical advancements in constraint qualifications , projected gradient flows , and epigraph reformulations , alongside practical applications in gemstone cutting and modular system design . His 15 most recent publications span topics such as non-convex Nash equilibrium problems , granularity in polynomial optimization , and branch-and-bound algorithms , reflecting a blend of theoretical rigor and real-world impact. Stein has received prestigious awards including the Heisenberg fellowship (2005-2006) and Feodor Lynen fellowship (1999-2000). He serves on editorial boards of journals like the Journal of Global Optimization and Optimization .
Prof. Dr. Michael Rath is Professor for Building Energy Technology at Hochschule Bochum since February 2022, affiliated with the Department of Civil and Environmental Engineering. He serves as Deputy Chairman of the Committee for Renewable Energy Systems, member of the Faculty Council, and member of several interdisciplinary institutes including the Interdisciplinary Institute for Applied AI and Data Science Ruhr (AKIS), DigiTeach Institute, and the Foundation Institute for the Energy Transition Institute (EnWI). His educational background includes parallel Diplom studies in Physics and Mathematics at Westfälische Wilhelms-Universität Münster (2004-2010), followed by doctoral research at Philipps-Universität Marburg (2010-2015) where he completed his dissertation on turbulence models. Prof. Rath's research spans building energy technology with a strong focus on renewable energy integration, particularly geothermal and solar systems. His work addresses climate-neutral energy systems through innovative approaches to district heating networks, thermal storage solutions, and machine learning applications for energy optimization. He investigates urban heat transition strategies, building automation, and sector coupling to achieve sustainable energy solutions for both new and existing buildings. His publication portfolio reveals consistent research output focused on practical energy solutions, with recent work emphasizing geothermal heat pump optimization, machine learning applications in energy systems, and district heating network design. The research demonstrates a clear trajectory toward integrated, climate-neutral urban energy systems with particular attention to forecasting methods, market-oriented operation, and thermal storage solutions. Prof. Rath actively supervises numerous bachelor's and master's theses, with recent students working on topics including thermal network modeling, sustainable hot water supply, summer heat protection measures, and photovoltaic integration effects on electricity prices. He also serves as second supervisor for doctoral candidates working on decarbonized district heating systems and hierarchical control strategies. He leads the Competence Center for Integrated Building Energy Technology at Fraunhofer IEG and is involved in significant research projects including EnOB: ARCHE (focusing on self-optimizing control systems for distributed energy systems) and the NRW Heat Study by LANUV, where he contributes to regional heat planning with a focus on developing cost frameworks for renovation measures.
Prof. Dr. Günter Leugering holds the Chair of Applied Mathematics 2 at Friedrich-Alexander University Erlangen-Nuremberg (FAU). His academic work focuses on mathematical optimization and control theory with applications to complex systems. His research interests span Optimization with Partial Differential Equations , Optimal Control , and Mathematical Modeling of physical systems. Specific focus areas include traffic flow modeling, gas network optimization, and multiscale simulation approaches. His work bridges theoretical mathematics with practical engineering applications, particularly in infrastructure systems. Prof. Leugering has been actively involved in numerous coordinated research programs including SFB-TRR 154 (Modeling, Simulation and Optimization Using Gas Networks), DFG-SPP 1253 (Optimization with Partial Differential Equations), and the DFG Cluster of Excellence Engineering of Advanced Materials (EAM). His public lectures at venues like the Planetarium Nuremberg demonstrate his commitment to science communication, with topics ranging from traffic jam mathematics to optimization for new materials. His research team participates in several major initiatives: SFB-TRR 154 subprojects on mixed integer-continuous dynamic systems and decomposition methods EAM Research Area A3 on Multiscale Modeling and Simulation DFG-SFB 603 subproject on mathematical optimization for registration problems EU Frame program STRAP: PLATO-N
Sasanka Potluri serves as Professor of General Computer Science and Medical Informatics at Karlshochschule (Karlsruhe University of Education) since September 2025. He is actively engaged in teaching and research within the Department of Computer Science and Medical Informatics, focusing on the intersection of artificial intelligence and healthcare applications. His academic leadership spans multiple research projects aimed at transforming healthcare delivery through technological innovation. His educational background includes: Dr.-Ing. in Electrical Engineering and Information Technology from Otto-von-Guericke University Magdeburg (Germany) Dipl.-Ing. in Information Technology from Alpen-Adria University Klagenfurt (Austria) B. Tech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Kakinada (India) Professor Potluri's research spans the cutting edge of artificial intelligence applications in healthcare, with particular expertise in machine learning, deep learning, and generative AI. His work bridges technical innovation with practical healthcare solutions, focusing on clinical decision support systems, biomedical statistics, and digital signal processing. He has developed novel approaches for healthcare logistics optimization, synthetic health data generation, and addressing digital health equity issues. His research methodology combines theoretical rigor with practical implementation, often working at the intersection of computer science, medical informatics, and systems engineering. His publication record reveals a consistent trajectory from industrial control systems security toward healthcare applications of AI. Early work focused on intrusion detection in industrial control systems using deep learning techniques, while recent publications demonstrate a strategic shift toward healthcare logistics, patient transportation optimization, and blood product management. This evolution reflects both his technical expertise in AI and his commitment to addressing critical challenges in healthcare delivery systems. His research increasingly incorporates generative AI approaches to solve complex healthcare resource allocation problems. Professional service includes: Member and Reviewer at GMDS (German Society for Medical Informatics, Biometry and Epidemiology) since 2024 Reviewer for European Federation for Medical Informatics since 2024 Reviewer for IEEE Transactions on Network and Service Management since 2020 Reviewer for Elsevier Journals including Engineering Applications of Artificial Intelligence since 2017 Professor Potluri actively supervises B.Sc, M.Sc, and PhD students in medical informatics, AI applications, generative AI, clinical decision support systems, and healthcare logistics. His current research projects focus on hospital resource and process optimization, synthetic health data generation, digital health equity studies, and generative AI in healthcare. He previously held research positions as Junior Research Group Leader at University Hospital Jena, Project Leader at Otto von Guericke University Magdeburg, and Research Assistant for EU Projects, building a strong foundation for his current interdisciplinary work.
Ambros Gleixner is a Professor at HTW Berlin since 2020 and an affiliated researcher at the Zuse Institute Berlin (ZIB) since 2008. His research focuses on computational aspects of mixed-integer linear and nonlinear programming, with emphasis on exact rational arithmetic and algorithm verification. PhD in Mathematics (2015), Technische Universität Berlin Diplom (MSc) in Mathematics (2008), Technische Universität Berlin Vordiplom (BSc) in Mathematics (2004), Universität Bayreuth His work spans mathematical optimization, operations research, and computational mathematics. At ZIB, he leads projects like developing the MINLP solver SCIP , the LP solver SoPlex , and verifying integer programming results through VIPR . Recent publications highlight advancements in exact rational MIP, GPU-parallel algorithms, and energy system optimization. Scientific Awards : MERIT Visiting Scholar at University of Melbourne (2013) Teaching : Offers bachelor's theses in optimization and computational mathematics. Requires students to have attended relevant seminars and possess programming skills. Office hours by email appointment through Ambros.Gleixner@HTW-Berlin.de . Labs & Teams : Principal investigator at ZIB's Mathematical Algorithmic Intelligence division, Research Campus MODAL , and Linear, Integer, and Constraint Programming project.
Professor Marc Goerigk holds the Chair of Business Decisions and Data Science at the Faculty of Economics, University of Passau, a position he has held since 2023. He previously held academic positions at TU Kaiserslautern, Lancaster University Management School, and the University of Siegen. He earned his doctorate in applied mathematics from the University of Göttingen and is recognized as a leading researcher in robust optimization. PhD in Applied Mathematics, University of Göttingen Research and teaching at TU Kaiserslautern, Lancaster University, University of Siegen His research centers on robust combinatorial optimization, focusing on decision-making under uncertainty. He develops mathematical models and algorithms that yield solutions resilient to data uncertainties, with applications in traffic, logistics, and corporate planning. He emphasizes abstract problem structures over specific instances, seeking generalizable optimization frameworks. His work bridges operations research, data science, and algorithm design, aiming to enhance decision robustness in complex systems. The recent publications highlight a strong trend in robust optimization, particularly in multi-stage and recoverable models, data-driven scenario generation, and interpretable optimization. His work increasingly integrates machine learning concepts with classical optimization, especially in explainability and preference modeling. Applications span scheduling, routing, project management, and logistics, demonstrating both theoretical depth and practical relevance. Scientific Awards: Most research-intensive business professor under 40 in the German-speaking world (WirtschaftsWoche, 2024) Professor Goerigk leads a research group focused on optimization under uncertainty. He supervises doctoral and master's students in seminars on optimization and data science. He teaches courses such as Decision Making Under Uncertainty, Combinatorial Optimization, and Artificial Intelligence and Optimization. His work is supported by ongoing research in robust modeling and algorithm development, with future directions likely involving deeper integration of AI and optimization for real-world decision support systems. No specific grants are mentioned, but his prolific output suggests active funding. He leads the Chair of Business Decisions and Data Science at the University of Passau, where his team works on theoretical and applied aspects of robust optimization, scenario modeling, and decision support systems.
Christian Kirches is a full professor at the Institute for Mathematical Optimization within the Carl-Friedrich-Gauß-Fakultät (Faculty of Mathematics, Technische Universität Braunschweig). His research focuses on nonlinear optimization , mixed-integer optimal control , and robust optimization for dynamic systems. He was awarded the Klaus-Tschira prize (2011) for public science communication and the Hengstberger prize (2014) for junior researchers, and received an ERC Consolidator Grant (2022) for his work on optimization under uncertainty. Alumni of Heidelberg University (Diploma, Doctorate, Habilitation) Former resident associate at Argonne National Laboratory and postdoctoral appointee at the University of Chicago Leader of a junior research group (2013–2017) at Heidelberg University His recent publications highlight advancements in mixed-integer nonlinear programming , real-time control systems , and optimization for sustainable energy and transportation . He collaborates with researchers on projects like wind farm control, hydrogen aviation networks, and chromatography process optimization. His methodological work on sum-up rounding , trust-region algorithms , and combinatorial integral approximation has been published in journals such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Control Systems Letters. Kirches also serves as area coordinator for Optimization Online and was associate editor for OR Spectrum (2022–2024). Scientific Awards: Klaus-Tschira Prize (2011) Hengstberger Prize (2014) ERC Consolidator Grant (2022) He is an elected member of the COIN-OR initiative and contributes to open-source optimization software. His lab at TU Braunschweig develops algorithms for dynamic systems under uncertainty, with applications in energy management, autonomous traffic, and industrial processes.
Prof. Dr. Maximilian Merkert is a Junior Professor for Optimization and Uncertainty in Mobility at the Institute for Mathematical Optimization, Technische Universität Braunschweig, since October 2021. Previously, he was a Postdoc at Otto von Guericke University Magdeburg (2017-2021) and a PhD student/research assistant at Friedrich-Alexander-Universität Erlangen-Nürnberg (2012-2017). His research focuses on mixed-integer nonlinear programming, network optimization, polyhedral combinatorics, and bilevel optimization with applications in mobility, logistics, and medicine. Research Interests: Mixed-Integer Nonlinear Programming Network Optimization Polyhedral Combinatorics Bilevel Optimization Game Theory Mobility Systems Teaching: Lectures and seminars on discrete optimization, multi-level optimization, and mathematical optimization Supervised computer labs and advanced optimization courses Publications: Contributions to mathematical programming journals and conferences (e.g., ICLR, Operations Research Proceedings) Focus on control languages, neural networks, wind farm optimization, and medical applications
Prof. Clemens Thielen holds the Professorship for Optimization and Sustainable Decision Making at TUM Campus Straubing, Technical University of Munich. He previously served as Junior Professor at TU Kaiserslautern (2013–2019) and was appointed to the Professorship for Complex Networks at TUM Campus Straubing in 2019. His research focuses on discrete mathematical optimization, including network optimization, approximation algorithms for multiobjective problems, and practical applications like healthcare scheduling and infrastructure planning. He earned his PhD in Mathematical Optimization from TU Kaiserslautern in 2010, with studies at the University of Cambridge. Notable awards include the 2024 EURO Prize for OR for the Common Good and a 2018 teaching nomination. His work bridges theoretical advancements and real-world applications such as flood mitigation, traffic emission reduction, and crane logistics optimization. Education: PhD in Mathematical Optimization, Technical University of Kaiserslautern (2010) Studies in Mathematics at Technical University of Kaiserslautern and University of Cambridge Research Interests: Network optimization and approximation algorithms Multiobjective decision-making and sustainable resource allocation Applications in healthcare, transportation, and infrastructure Awards: EURO Prize for OR for the Common Good (2024) Nomination for Teaching Award of Rhineland-Palatinate (2018) Labs/Teams: Active in the Optimization and Sustainable Decision Making research group at TUM Campus Straubing, collaborating on projects such as municipal flood mitigation and healthcare scheduling.
Prof. Dr. Marc Pfetsch is a full professor of Discrete Optimization at the Technical University of Darmstadt, holding the W3 chair since 2012. He leads the Optimization Group within the Department of Mathematics and has served as Dean of the Department from October 2022 to September 2024. His research focuses on optimization methodologies, particularly in gas network modeling, discrete and mixed-integer programming, and computational algorithms. He is a core developer of the SCIP Optimization Suite, a leading solver for mixed-integer programming problems. Education : Mathematics studies at the University of Heidelberg (1992–1997) Operations Research at Cornell University (1997–1998, via Fulbright Scholarship) PhD in Mathematics from TU Berlin (2002) Habilitation in Computational Aspects of Combinatorial Optimization (2008) Research Interests : Discrete and combinatorial optimization Gas network optimization and resilience design Symmetry handling in mixed-integer programming Algorithm development for SCIP and optimization software Key Projects : Transregio/SFB 154: Mathematical Modeling, Simulation, and Optimization of Gas Networks SCIP Optimization Suite development Clean Circles: Iron as an energy carrier for climate-neutral systems Awards : EURO Excellence in Practice Award 2016 for "Evaluating Gas Network Capacities" Grants and Labs : Principal investigator in multiple DFG projects (e.g., SPP 2298, Matheon) BMWi-funded projects on flexible heating networks and resilient systems
Navid Ansari is a doctoral researcher at the Max Planck Institute for Informatics (MPI-INF) and Saarland University, affiliated with the Artificial Intelligence Aided Design and Manufacturing group under the Computer Graphics department. His work bridges academia and industry, with internships at Amazon AWS and the Max Planck Institute for Brain Research. PhD in Computer Science (2021–Present), Saarland University & MPI-INF MSc in Visual Computing (2018–2021), Saarland University BSc in Electrical Engineering (2013–2017), Shiraz University Navid's research focuses on deep learning, generative models, and optimization techniques. His work includes mixed-integer optimization for neural networks, Bayesian design optimization, uncertainty quantification in AI systems, and applications in computational manufacturing and molecular design. He has contributed to top-tier venues like AAAI, NeurIPS (Spotlight), and SIGGRAPH. His publications address trends in AI-aided design optimization, uncertainty-aware modeling, sparsity in large language models, and inverse molecular design aligned with molecular dynamics. This work spans applications in manufacturing, computational chemistry, and natural language processing. Navid has collaborated with institutions such as Amazon AWS (LLM sparsification) and the Max Planck Institute for Brain Research (behavioral neuroscience). He is part of the Saarland Informatics Campus, a hub for visual computing and AI research.
Prof. Dr. Stefan Weltge is a Professor of Discrete Mathematics at the Department of Mathematics, Technical University of Munich (TUM). He holds a PhD in Mathematics from Otto von Guericke University Magdeburg (2016) and was a postdoctoral researcher at ETH Zurich. His research focuses on combinatorial optimization, integer programming, and polyhedral combinatorics, with notable contributions to extension complexity and mixed-integer programming. He has received multiple teaching awards at TUM, including the TUM Supervisory Award (2022) and Best Lecturer recognitions in 2019 and 2021/22. His work has been published in leading journals such as the Journal of the ACM and Journal of Combinatorial Theory B. Prof. Weltge’s academic contributions include groundbreaking research on the complexity of mixed-integer programs, polyhedral representations, and combinatorial optimization problems. He has organized conferences like OR 2024 and the Cargese Workshops on Combinatorial Optimization, and serves on program committees for IPCO, MIP, and ISCO. His research is supported by DFG grants, including the Individual Grant (NextGen) and the AdONE PhD Program. He advises PhD students working on topics such as integer programming, algorithm design, and optimization theory. His publications span theoretical advancements in convex optimization, linear programming relaxations, and applications in logistics and operations research. Notable articles include work on bounded subdeterminants in integer programs and the complexity of stable set problems. His research bridges discrete mathematics with practical algorithmic solutions, influencing both theoretical and applied domains.
Prof. Dr. Manuel Ostermeier leads the Chair of Resilient Operations at the Center for Climate Resilience and the Faculty of Business and Economics at the University of Augsburg. His research focuses on sustainable and resilient logistics concepts in supply chains, with applications in food waste prevention , autonomous vehicles , and data-driven optimization . He collaborates internationally with institutions like the Polytechnic University of Porto and industry partners on projects such as the Green Hospital initiative , which addresses healthcare supply chain sustainability. The chair offers interdisciplinary opportunities for research and teaching. Contact: manuel.ostermeier@uni-a.de | rop@wiwi.uni-augsburg.de Address: Universitätsstraße 12, 86159 Augsburg Research Trends : Analysis of the 15 most recent articles reveals expertise in last-mile logistics (robotics, autonomous vehicles), food waste reduction in retail, and multi-compartment vehicle routing for efficient distribution. Quantitative methods (e.g., mixed-integer programming, heuristic algorithms) are applied to grocery retail , quick commerce , and environmental sustainability .
Bodo Rosenhahn is a Full Professor at Leibniz University Hannover, heading the Institute for Information Processing since September 2008. His research focuses on automated image interpretation with profound expertise in Computer Vision, Machine Learning, and Big Data Analysis. He has established himself as a leading researcher through extensive contributions to the field and successful industry transfer of his work. Rosenhahn received his Computer Science education at the University of Kiel, earning his Dipl.-Inf. in 1999 and Dr.-Ing. in 2003. His academic journey included a postdoctoral position at the University of Auckland (2003-2005), funded by the German Research Foundation, followed by senior researcher work at the Max-Planck Institute for Informatics in Saarbruecken (2005-2008). His research interests span multiple cutting-edge areas including Computer Vision, Machine Learning, 3D Human Pose Estimation, Motion Capture, Object Tracking, Anomaly Detection, and Reinforcement Learning. His work bridges theoretical foundations with practical applications, particularly in medical imaging, autonomous systems, and industrial quality control. The group he leads has developed innovative approaches for video-based motion capture, semantic scene analysis, and multi-object tracking that have achieved state-of-the-art results in numerous challenges. His most recent publications demonstrate strong trends toward explainable AI systems, uncertainty quantification in vision models, robust multi-model fitting techniques, and the integration of quantum principles with machine learning. These works reflect his commitment to developing both theoretically sound and practically applicable computer vision solutions that address real-world challenges in industry and medicine. DAGM-Prize 2002 Dr.-Ing. Siegfried Werth Prize 2003 DAGM-Main Prize 2005 ERC-Starting Grant 2011 (EUR 1.43 million) CVPR 2017 Multi-Object Tracking Challenge PhysRev-A Editors Suggestion 2023 TÜV-Süd Innovation award 2018 As head coach of the LUH AI competition team, Rosenhahn has mentored numerous students who have achieved success in international competitions. His research has been supported by prestigious grants including the ERC Starting Grant and POC Grant. He has also received the Erskine Fellowship for research at the University of Canterbury. Since 2023, he serves as associate editor for IEEE TPAMI, the highest-ranked journal in computer science. Rosenhahn leads a vibrant research group focused on automated image interpretation with multiple ongoing projects including Multiple People Tracking, Relational Object Tracking, Physics-based modeling, Video-based Motion Capture, and Quantum Learning. His group has developed significant datasets such as the Multimodal Motion Capture Indoor Dataset (MPI08) and Multimodal Motion Capture Dataset (TNT15) that have become valuable resources for the computer vision community. The group maintains strong industry connections, successfully transferring research into practical applications while continuing to push the boundaries of fundamental research in computer vision and machine learning.
Prof. Dr. Christian Almeder serves as Professor and Head of the Chair of Supply Chain Management within the Faculty of Business Administration and Economics at Viadrina European University (Frankfurt (Oder), Germany). His research focuses on operations research applications in production planning, logistics, and supply chain optimization, with particular expertise in lot sizing, scheduling, and perishable goods management. He maintains active research output with publications spanning from 1997 to 2023. Almeder's research centers on mathematical modeling of complex production and logistics systems. His primary contributions involve developing heuristic and metaheuristic solutions for capacitated lot sizing problems, multi-level scheduling, and integrated production-distribution planning. Key specialties include handling perishability constraints, lead time uncertainties, and batch processing requirements using genetic programming, simulation-based optimization, and clearing function approaches. His work bridges theoretical operations research with industrial applications in supply chain management. Analysis of his 15 most recent publications (2013-2023) reveals a consistent focus on lot sizing and scheduling, with increasing emphasis on integrated supply chain problems and perishable goods logistics. Methodologically, he combines metaheuristics (genetic programming, simulated annealing) with simulation techniques to address real-world complexities like stochastic processing times and limited buffers. His work demonstrates strong application in production planning parameter tuning, vehicle routing integration, and robust operational planning under uncertainty.
