جست‌وجوی استاد و پژوهشگر

Kevin-Martin Aignerمشاهده پروفایل

پژوهشگر

• Optimization under Uncertainty
• Power Network Optimization
• Data-Driven Optimization
• +۳ مورد دیگر

Kevin-Martin Aigner serves as an Academic Councillor (Akad. Rat) at the Department of Data Science within the Faculty of Science at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is affiliated with the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Liers, focusing on mathematical optimization methods for energy systems and power networks. His work bridges theoretical optimization with practical energy transition challenges. His research centers on optimization under uncertainty, with key interests in distributionally robust optimization, data-driven methods, and sector-coupled energy system modeling. He develops novel approaches for handling renewable energy uncertainties in power grids, solar feed-in variability, and multi-sector energy integration. His methodologies emphasize explainability in optimization processes and robust decision-making under complex uncertainty structures. Recent publications reveal consistent focus on power network optimization (2021-2025), with increasing emphasis on distributionally robust frameworks, explainable AI integration, and regional multi-sector energy modeling. His work demonstrates strong interdisciplinary connections between operations research, machine learning, and sustainable energy systems engineering. Scientific recognition includes: GOR Dissertation Award for "Data-driven Optimization under Uncertainty for Power Networks" Dr. Aigner actively contributes to major research initiatives including: CRC TRR 154: Mathematical Modeling, Simulation and Optimization using Gas Networks (2022-2026, DFG-funded) ESM-Regio: Multi-sector Coupled Energy System Modeling on Regional Level (2021-2024, BMWE-funded) Optimal Control of Electrical Distribution Networks with Uncertain Solar Feed-in (2018-2021) He participates in the Optimization under Uncertainty & Data Analysis (OUDA) research group and has organized academic events including the TRR 154 Summer School on Optimization, Uncertainty and AI (2024) and Women in Optimization 2024. His teaching includes Discrete Optimization I and specialized seminars on Mixed-Integer Nonlinear Optimization.

• Computational Optimization
• Mathematical Optimization
• Numerical Analysis
• +۴ مورد دیگر

Alberto De Marchi is a Research Associate at the Institute of Applied Mathematics and Scientific Computing at Universität der Bundeswehr München (UniBw M) in Germany. He holds a doctoral degree (Dr.rer.nat.) in Applied Mathematics from UniBw M (2021), an M.Sc. in Mechatronics Engineering (2016), and a B.Sc. in Industrial Engineering (2014) from the University of Trento (UniTn) in Italy. In Fall 2022, he was a Visiting Research Associate with Ryan Loxton at Curtin University, Australia. Education Dr.rer.nat., Applied Mathematics, UniBw M (2021) M.Sc., Mechatronics Engineering, UniTn (2016) B.Sc., Industrial Engineering, UniTn (2014) His research spans computational optimization, mathematical modeling, numerical analysis, and control systems, with a focus on developing robust numerical optimization tools. Recent work includes applications in IoT digital twins, blockchain-based anonymity frameworks, and hybrid optimal control methods. His 2025–2023 publications emphasize nonlinear and mixed-integer optimization techniques, constrained composite optimization, and advanced algorithms for control systems. These works explore topics like augmented Lagrangian methods, proximal gradient approaches, collision avoidance, and blockchain ethics. Scientific Awards COAP 2022 Best Paper Prize Alberto collaborates internationally and is intellectually curious about interdisciplinary topics, including the philosophy of mind and literature.

Winnifried Wollnerمشاهده پروفایل

استاد

• Optimization
• Applied Mathematics
• PDE-Constrained Optimization
• +۷ مورد دیگر

Prof. Dr. Winnifried Wollner is a Professor of Optimization at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on optimization with partial differential equations (PDEs), numerical methods for PDEs, and computational mechanics, particularly in the context of shape optimization and fracture mechanics. He holds a PhD from Heidelberg University (2010) and has held academic positions at TU Darmstadt and the University of Hamburg. He is actively involved in editorial boards of journals like the SIAM Journal on Control and Optimization and the OPTPDE Problem Collection. His work emphasizes adaptive numerical methods, phase-field fracture modeling, and optimization under constraints. Awards include the 2014 Teacher of the Summer Term award at Hamburg. He teaches courses on optimization, numerical methods, and PDE-constrained problems, and leads research projects funded by DFG and the Excellence Cluster CLICCS. Professional roles include membership in the MIN Faculty Council, the Department's Equal Opportunities Team, and leadership of the GAMM Student Chapter. His research spans interdisciplinary applications in engineering and computational science, with over 66 publications in top journals. Current projects include the DFG Research Training Group 2583 on fluid dynamics and optimization, and the SPP 1962 project on phase-field fracture simulation.

Frank Alsmeyerمشاهده پروفایل

استاد

• Sustainable Energy Systems
• Energy Efficiency
• Sector Coupling
• +۱۱ مورد دیگر

Prof. Dr.-Ing. Frank Alsmeyer is a faculty member in the Department of Mechanical and Process Engineering at Niederrhein University of Applied Sciences, where he leads research in Energy and Process Systems Engineering. He is affiliated with the Institute for Energy Technology and Energy Management (SWK E²) and supervises a wide range of student projects and theses focused on sustainable energy systems, optimization, and process engineering. His research interests include: Sustainable energy system technology Climate-friendly restructuring of energy supply Sector coupling across buildings, industry, transport, and commerce Non-fossil energy sources such as hydrogen and biogenic substances Computer-aided simulation and optimization using MILP and phenomenological models Improving process sustainability, quality, and cost-effectiveness His recent publications and research projects (e.g., Bilinear Optimization of Energy Systems, Green Heat for Krefeld) reflect a strong focus on modeling, simulation, and optimization of integrated energy systems, particularly in urban and industrial contexts. The work spans thermodynamics, energy cadastres, waste-to-energy integration, and digital education tools for engineering. There is a clear trend toward applied research with real-world implementation, especially in Krefeld and surrounding regions. Scientific awards include: Best FB04 Bachelor Project 2016/17 for the 'Energiebaukasten' interactive energy visualization tool Prof. Alsmeyer actively supervises numerous student theses and projects, covering topics such as energy efficiency, heat integration, hydrogen systems, and process optimization in industrial settings. He is involved in multiple funded research initiatives, including Bilinear Optimization of Energy Systems (2023–2026), Herzogenrath Energy Park, and OK!Thermo, an open educational resource for thermodynamics. His work bridges engineering fundamentals with practical applications in sustainability and digitalization. He leads the development of digital tools for energy analysis and education, such as interactive touch tables and online learning platforms. He is associated with the following labs and research groups: Institute for Energy Technology and Energy Management (SWK E²) Research group on Computer-Aided Analysis, Simulation and Optimization of Energy and Process Systems Team developing OK!Thermo and other OER materials for engineering education

• Operations Research
• Optimization
• Computational Geometry
• +۳ مورد دیگر

Igor S. Litvinchev is a researcher in the fields of Operations Research , Optimization , and Computational Geometry . His work focuses on mathematical modeling, algorithm design, and applications in industrial engineering, particularly packing and placement problems under geometric constraints. Research Interests: Sphere/ellipse packing, convex hull optimization, Lagrangian heuristics, and applications in additive manufacturing and wireless networks. Recent Collaborations: Co-authoring with Tatiana E. Romanova, Luis Infante, and Aleksandr V. Pankratov on packing problems and facility location models. Key Publications: 2024 studies on parabolic containers , quasi-containment , and soft polygon packing , alongside 2023-2020 works on 3D clusters, circular layouts, and R&D portfolio optimization. Editorial Contributions: 2022 editorial on digitization in organizations, reflecting his interest in real-time systems and academic collaboration.

Günter Woznyمشاهده پروفایل

استاد

• Process Optimization
• Process Simulation
• Integrated Processes
• +۷ مورد دیگر

Prof. Dr. Günter Wozny is a Professor at Technische Universität Berlin, affiliated with Faculty III and the Institute for Process and Plant Engineering. He served as a former member of the UniCat Cluster of Excellence's Executive Board (2008-2012). His research focuses on process optimization, simulation, integrated processes, information management, system dynamics, and thermal separation techniques. His work spans fields like oxidative coupling of methane (OCM), membrane reactors, CO₂ capture, and process intensification. Key contributions include advancements in reactor engineering, separation technologies, and energy-efficient processes. Notable awards include the Cross of Merit of the Republic of Poland (2016) for fostering scientific exchange between Germany and Poland. He has supervised prominent students like Mariano Nicolas Cruz Bournazou, who received the Chorafas Prize for his thesis work. Collaborations with industry partners like Siemens and Endress+Hauser have driven applied research. Wozny leads the FG Dynamik und Betrieb group and has pioneered the development of UniCat’s miniplant for OCM process studies. His publications emphasize robust optimization methods, CFD modeling of fluidized beds, and techno-economic evaluations of industrial processes. He has authored over 220 papers and holds patents for OCM-related technologies, such as oxidative conversion methods and reactor designs.

Paul Stephanمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Variational Problems
• Functional Analysis
• +۳ مورد دیگر

Paul Stephan is a researcher at the University of Konstanz, Department of Mathematics and Statistics, specializing in partial differential equations and variational problems on limiting function spaces. He focuses on coercive inequalities, boundary value problems with critical data, and applications in energy systems modeling. Supervised by Prof. Dr. Franz Gmeineder Collaborates with Prof. Dr. Robert Denk Former student research assistant at HTWG Konstanz (Faculty of Mechanical Engineering) His research bridges advanced mathematical analysis with practical applications in energy systems. Recent work includes partial regularity for quasiconvex functionals, KMS inequalities, and decentralized energy grid modeling. Scientific recognition includes the MPI Prize and MfO Prize for best presentation at Stukon (2024), LUKS Prize for teaching excellence (2024), and VEUK Prize for outstanding thesis (2023). Teaching roles span Analysis I/II, Function Theory, and mathematics preparatory courses. He also organizes seminars on Fourier analysis and contributes to science communication through Campuls.Online articles.

• Continuous Optimization
• Robust Optimization
• Mixed-integer nonlinear optimization
• +۷ مورد دیگر

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 .

• Mathematical Optimization
• Nonlinear Optimization
• Mixed-Integer Optimal Control
• +۳ مورد دیگر

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.

• Mixed-Integer Nonlinear Programming
• Network Optimization
• Polyhedral Combinatorics
• +۵ مورد دیگر

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

Marc Pfetschمشاهده پروفایل

استاد

• Discrete Optimization
• Mathematical Programming
• Operations Research
• +۳ مورد دیگر

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

Ambros Gleixnerمشاهده پروفایل

استاد

• Mathematical Optimization
• Operations Research
• Computational Mathematics
• +۷ مورد دیگر

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.

Christian Almederمشاهده پروفایل

استاد

• Supply Chain Management
• Operations Research
• Production Planning and Scheduling
• +۳ مورد دیگر

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.

Florian Röselمشاهده پروفایل

پژوهشگر

• Optimization under Uncertainty
• Data Science
• Operations Research
• +۴ مورد دیگر

Dr. Florian Rösel is a researcher affiliated with the Department of Data Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He works under the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Frauke Liers, focusing on data-driven optimization techniques and their applications in aviation logistics and complex system modeling.

Marco Lübbeckeمشاهده پروفایل

استاد

• Operations Research
• Mixed Integer Programming
• Branch-and-Price Algorithms
• +۳ مورد دیگر

Marco Lübbecke is a Professor at the Faculty of Business and Economics of RWTH Aachen University , Germany. He serves as the chairholder of the Lehrstuhl für Operations Research and holds the position of Studiendekan (Dean of Studies) for his faculty. His professional contact is marco.luebbecke@rwth-aachen.de, with additional administrative contact at luebbecke@or.rwth-aachen.de. Current academic rank: Professor Faculty: Business and Economics Department: Operations Research Marco Lübbecke's research focuses on Operations Research and Mixed Integer Programming . He develops and analyzes algorithms like Branch-and-Price , Dantzig-Wolfe Reformulation , and Decomposition Methods to solve complex optimization problems in industrial, transportation, and political science applications. Core research areas: Combinatorial Optimization, Mathematical Programming Application domains: Logistics, Rail Transport, Redistricting, Manufacturing Systems Methodological interests: Column Generation, Cutting Planes, Algorithm Engineering His recent publications highlight advancements in optimization software frameworks like the SCIP Optimization Suite and algorithmic techniques for solving large-scale linear and integer programs. Key research trends include automated decomposition methods, structural analysis of MIPs, and hybrid approaches combining classical optimization techniques with machine learning insights. Marco Lübbecke actively participates in academic conferences and serves as a co-author in numerous technical reports and journal publications, contributing to the development of efficient optimization algorithms and their practical implementation.