جست‌وجوی استاد و پژوهشگر

Ou Jihongمشاهده پروفایل

دانشیار

• Analytics for Managers
• Business Process Management
• Production/Inventory Systems
• +۴ مورد دیگر

Ou Jihong serves as Associate Professor of Operations Management at Cheung Kong Graduate School of Business (CKGSB), bringing extensive global academic experience from prior appointments at National University of Singapore Business School, University of Cambridge, University of California at Los Angeles, and University of Illinois. His research bridges theoretical operations research with practical applications in China's logistics industry. His educational background includes a PhD from Massachusetts Institute of Technology. Research interests span Analytics for Managers, Business Process Management, Production/Inventory Systems, Queuing Analysis and Control, Statistics, Stochastic Modeling and Analysis, and Supply Chain Management, with particular expertise in applied research on China's third-party logistics sector. Professor Ou's publication record demonstrates significant contributions to top-tier journals including Management Science and Operations Research , focusing on optimization of production systems, inventory control, queuing networks, and revenue management. His work consistently addresses real-world operational challenges through rigorous mathematical modeling. His scientific contributions include expertise in conducting industry surveys within China's logistics ecosystem and developing computational approaches for complex operational problems. Professor Ou maintains active engagement with industry through his research on supply chain dynamics and operational efficiency in emerging markets.

Ahmet Cürebalمشاهده پروفایل

پژوهشگر

• Operations Research
• Linear Programming
• Mixed-Integer Programming
• +۵ مورد دیگر

Ahmet Cürebal is an External Doctoral Researcher at the Institute of Information Systems, part of the Business School at the University of Hamburg. He holds B.S. and M.S. degrees in Industrial Engineering from Kirikkale University, Turkey, and is currently pursuing his Ph.D. His research focuses on Operations Research with specializations in Linear Programming (LP), Mixed-Integer Programming (MIP), Combinatorial Optimization, Scheduling, Assignment, and Routing problems within Supply Chain contexts. Key research interests include solving real-world optimization challenges such as staff scheduling in retail sectors, driver workload balancing in logistics firms, and pandemic-era security personnel allocation. His work often involves applying metaheuristic methods like Variable Neighborhood Search (VNS) and Fixed Set Search (FSS) to complex operational problems. Notable publications explore applications such as straddle carrier routing in container terminals, bus route optimization for high-speed rail integration, and competency-based scheduling during crises. His methodologies frequently integrate Goal Programming and Analytical Network Process (ANP) for multi-criteria decision-making scenarios. Cürebal’s research demonstrates a strong emphasis on practical implementations across diverse industries, including healthcare, logistics, and retail, with a focus on balancing efficiency and real-world constraints through advanced mathematical modeling and algorithmic approaches.

Paul Mannsمشاهده پروفایل

استادیار

• Mathematical Optimization
• PDE-Constrained Optimization
• Integer Programming
• +۱ مورد دیگر

Paul Manns is an Assistant Professor of Optimization at TU Dortmund University's Department of Mathematics, appointed in 2021. His research specializes in mathematical optimization involving partial differential equations and integer constraints, with emphasis on regularization techniques and trust-region algorithms. Education includes: Ph.D. in Mathematics, TU Braunschweig (2019) Computational Engineering studies, TU Darmstadt Prior research experience includes positions at Heidelberg University, TU Braunschweig, and Argonne National Laboratory (USA), including a James H Wilkinson Fellowship. Recent publications develop novel methods for mixed-integer control problems, domain decomposition, and convergence analysis in non-convex optimization spaces.

Thomas Markwigمشاهده پروفایل

استاد

• Algebraic Geometry
• Computer Algebra
• Singularity Theory
• +۳ مورد دیگر

Prof. Dr. Thomas Markwig is a Professor of Mathematics at the Department of Mathematics, Eberhard Karls University of Tübingen, serving as Dean of Mathematics. His academic affiliations include the Faculty of Mathematics and Natural Sciences, with active involvement in the Collaborative Research Center (CRC TRR 195) and the SINGULAR computer algebra system project. His research focuses on algebraic geometry, tropical geometry, singularity theory, and computer algebra. Key areas of research include tropical floor plans, enumeration of algebraic surfaces, and computational methods in algebraic geometry. He has contributed to the development of algorithms for Gröbner bases, standard bases in mixed rings, and tropical varieties. His teaching spans courses like Analysis, Linear Algebra, and specialized topics in tropical geometry across multiple academic terms. Prof. Markwig has developed software tools such as tropical.lib for SINGULAR, advancing computational tropical geometry. His work bridges theoretical contributions with practical computational frameworks, impacting both research and education in mathematics.

Petra Mutzelمشاهده پروفایل

استاد

• Graph Algorithmics
• Graph Learning
• Combinatorial Optimization
• +۳ مورد دیگر

Petra Mutzel is a Professor of Computer Science at the University of Bonn. Her research focuses on graph algorithmics, temporal networks, and combinatorial optimization with applications in data science and bioinformatics. She holds a PhD in Computer Science from the University of Cologne (1994) and a Diplom in Mathematics from the University of Augsburg (1990). Her work emphasizes algorithmic solutions for complex graph problems, including temporal graph analysis, graph learning, and optimization frameworks for real-world networks. She has contributed to open-source libraries like Tglib for temporal graph processing and frameworks like Scaffold Hunter for medicinal chemistry. Her research spans theoretical foundations and practical implementations, with notable contributions to graph drawing, vehicle routing optimization, and protein complex analysis. Mutzel’s recent publications (2022-2025) explore temporal network dynamics, robust combinatorial optimization, and scalable graph kernel methods. She actively engages in academic leadership, organizing conferences such as WALCOM 2022, and serves on editorial boards for journals in algorithms and computational geometry.

• Optimization
• Mixed-Integer Programming
• Lattice Theory
• +۵ مورد دیگر

Professor Timm Oertel is a leading academic at the Department of Data Science (DDS) at Friedrich Alexander University Erlangen-Nuremberg, where he holds the Chair of Analytics & Mixed-Integer Optimization. His research has established him as a significant contributor to the field of mathematical optimization, with a particular focus on integer programming and its applications. Professor Oertel's research interests center on mathematical optimization , specifically mixed-integer programming , lattice theory , semigroups , and combinatorial optimization . His work explores fundamental questions about sparsity in integer solutions, properties of lattice points in polyhedra, and the theoretical foundations of optimization algorithms. His research bridges pure mathematics with practical applications in operations research and computer science. An analysis of Professor Oertel's publication record reveals a strong thematic consistency with evolving depth. His work consistently addresses sparsity phenomena in integer programming, structural properties of lattices and semigroups, and computational complexity aspects of optimization problems. The publications demonstrate increasing sophistication in handling block-structured problems and parametric optimization scenarios, with recent work extending to colorful variants of classical geometric lemmas applied to integer programming. Professor Oertel has established productive collaborations with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat. His research has been consistently published in top-tier venues including Mathematical Programming , SIAM Journal on Optimization , and the prestigious IPCO (Integer Programming and Combinatorial Optimization) conference series. These publications reflect rigorous theoretical contributions with potential applications across operations research, computer science, and discrete mathematics.

Thomas W. Repsمشاهده پروفایل

استاد

• Program analysis
• Abstract interpretation
• Model checking
• +۷ مورد دیگر

Thomas W. Reps is the J. Barkley Rosser Professor & Rajiv and Ritu Batra Chair in the Computer Sciences Department of the University of Wisconsin-Madison, where he has been a faculty member since 1985. He is also President of GrammaTech, Inc., which he co-founded in 1988 to commercialize research on programming environments. Reps received his Ph.D. in Computer Science from Cornell University in 1982, winning the ACM Doctoral Dissertation Award. His research spans program analysis, abstract interpretation, model checking, programming languages, and computer security. Reps's most influential work includes pioneering program slicing techniques with Susan Horwitz, development of the Cornell Program Synthesizer and Synthesizer Generator with Tim Teitelbaum, and significant contributions to interprocedural dataflow analysis. His recent work extends into quantum computing and advanced program synthesis techniques. The analysis of his publications reveals consistent focus on program analysis techniques, with recent papers exploring quantum circuit verification, abstract transformer synthesis, and program synthesis. His work bridges theoretical foundations with practical applications, often addressing scalability challenges in program analysis. NSF Presidential Young Investigator Award (1986) Packard Fellowship (1988) ACM Fellow (2005) ACM SIGPLAN Programming Languages Achievement Award (2017) Elected foreign member of Academia Europaea (2013) Multiple best paper awards including ETAPS (2004, 2008) Reps has advised numerous students, several of whom have received departmental and national recognition. His work on the Wisconsin Program-Slicing Tool led to commercial products through GrammaTech. He has served on advisory boards for significant projects including Year 2000 remediation for the Department of Defense and F/A-22 avionics software integration.

Lukas Brandlمشاهده پروفایل

پژوهشگر ارشد

• Linear Programming
• Integer Programming
• Combinatorial Optimization
• +۲ مورد دیگر

Lukas Brandl is a Research Fellow at the Department of Operations Research , Technische Universität München (TUM). His research focuses on theoretical and computational aspects of optimization, specifically Linear Programming, Integer Programming, and Decision Diagrams for Optimization. Education: B.Sc. Mathematics (2017–2020), TUM M.Sc. Mathematics (2020–2022), TUM PhD Mathematics (2022–present), TUM Research Interests: Brandl's work addresses problems in Combinatorial Optimization and Complexity Theory, with an emphasis on decision diagrams and polyhedral methods in optimization. His research explores both fundamental theory and practical applications of linear and integer programming. Selected Publications: Brandl, Lukas; Schulz, Andreas S. (2024). Primal Separation and Approximation for the {0,1/2}-closure . Operations Research Letters. Brandl, Lukas; Schulz, Andreas S. (2023). Primal Separation and Approximation for the {0, 1/2}-closure . CoRR. Teaching: In Summer Term 2023, he served as a Teaching Assistant for Applied Discrete Optimization .

Ulrike Bückingمشاهده پروفایل

مدرس ارشد

• Discrete Differential Geometry
• Conformal Geometry
• Discrete Surfaces
• +۳ مورد دیگر

Ulrike Bücking serves as a Senior Lecturer in the Department of Mathematics and Computer Science at Free University of Berlin, where she is Deputy Head of the Primary School Mathematics Group within the Discrete Methods in Algebraic Geometry & Mathematics for Teacher Training working group. Her academic responsibilities include teaching mathematical professional knowledge courses for primary school teacher candidates since 2019, with recent expansion to advanced specialized instruction for mathematics-focused teacher trainees. Her research spans two interconnected domains: theoretical discrete differential geometry focusing on convergence of discrete conformal mappings, rigidity of planar patterns, and discrete minimal surfaces; and mathematics education specifically tailored for primary school teacher preparation. This dual expertise manifests in publications ranging from pure mathematical investigations of circle patterns and discrete Riemann surfaces to innovative pedagogical frameworks for mathematics teacher education. Analysis of her 15 most recent publications reveals a consistent trajectory in discrete complex analysis with increasing integration of mathematics education research since 2020. Her geometric work demonstrates methodological evolution from foundational circle pattern theory toward sophisticated discrete surface constructions, while her educational publications address practical curriculum development for teacher training programs. The 2023 MatheProfIL paper exemplifies her applied approach to bridging theoretical mathematics with classroom pedagogy. Dr. Bücking maintained continuous research activity through her decade-long participation (2012-2024) in the Collaborative Research Center/Transregio 'Discretization in Geometry and Dynamics' (Project A01: Discrete Riemann Surfaces). Her teaching portfolio shows significant evolution from advanced mathematics courses for physicists at TU Berlin (2002-2018) to specialized mathematics education for primary teachers at Free University of Berlin. She actively contributes to the Primary School Mathematics Group and collaborates within the Discrete Methods in Algebraic Geometry & Mathematics for Teacher Training working group, maintaining an office at Arnimallee 3 in Berlin's mathematics institute where she supervises course development and coordinates teacher education initiatives.

Danial Hamedi Jamaliمشاهده پروفایل

پژوهشگر

• Hydrogen Infrastructure
• Renewable Energy Systems
• MINLP Optimization
• +۳ مورد دیگر

Danial Hamedi Jamali is a doctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, affiliated with the Process Systems Engineering group under Prof. Kai Sundmacher. He holds an M.Sc. from the University of Tehran and was awarded scholarships for his bachelor’s and master’s studies, along with the Dean’s Scholarship from the University of Saskatchewan. His research focuses on topology optimization of pipeline networks using mixed-integer nonlinear programming (MINLP), with recent emphasis on hydrogen infrastructure design and retrofitting of natural gas grids. His expertise spans MINLP optimization, energy-exergy analysis, and sustainable system design. He has authored over ten peer-reviewed publications (over 1,000 citations) and serves as a reviewer for Elsevier and Springer journals. Collaborations include international research teams addressing renewable energy systems and low-temperature solar technologies. Research highlights include work on hydrogen pipeline networks, biogas utilization frameworks, and solar-based multi-generation systems. Awards include scholarships for academic excellence and the prestigious Dean’s Scholarship. His contributions bridge theoretical optimization with practical applications in energy transition and infrastructure sustainability.

• Process Systems Engineering
• Optimization
• Chemical Engineering
• +۶ مورد دیگر

Matthew J. Realff is a Professor in the School of Chemical and Biomolecular Engineering at the Georgia Institute of Technology. With an extensive publication record spanning over three decades, his work focuses on the intersection of chemical engineering, process systems engineering, and advanced computational methods. His research has made significant contributions to optimization techniques, supply chain management, and sustainable engineering practices. Dr. Realff's research interests span a broad range of topics in process systems engineering, with particular emphasis on optimization under uncertainty, supply chain modeling, and sustainable engineering practices. His work combines rigorous mathematical approaches with practical engineering applications, particularly in the areas of biorefinery systems, carbon capture technologies, and renewable energy integration. He has pioneered methodologies that bridge traditional chemical engineering with modern computational techniques including machine learning and Bayesian statistics. Analysis of his recent publications reveals a consistent focus on addressing complex engineering challenges through advanced computational methods. His work demonstrates a strong trajectory toward integrating data-driven approaches with traditional process engineering, particularly evident in his recent papers on Bayesian experimental design, uncertainty quantification, and machine learning applications in chemical processes. The interdisciplinary nature of his research connects chemical engineering with operations research, environmental science, and computer science. Throughout his career, Dr. Realff has maintained a productive research program with consistent publication output in top-tier chemical engineering and operations research journals. His collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern engineering research. He has supervised numerous graduate students and contributed significantly to the education and training of future engineers through his academic appointments and research mentorship.

• Optimization
• Convex Optimization
• Nonconvex Optimization
• +۷ مورد دیگر

Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.

Erika Roldan Roaمشاهده پروفایل

استادیار مهمان

• Stochastic Topology
• First Passage Percolation
• Extremal Topological Combinatorics
• +۳ مورد دیگر

Erika Roldán Roa is a mathematician with academic affiliations at Ohio State University (Visiting Assistant Professor, 2019-2020) and Technische Universität München (Marie Skłodowska-Curie Fellow, 2020-2022). She holds a Ph.D. in Probability and Statistics from CIMAT (2018) and has conducted postdoctoral research at institutions like EPFL and Ohio State. Her work bridges stochastic topology, combinatorics, and educational technology, focusing on topics like cubical sliding puzzles, Eden growth models, and extremal topological problems in polyominoes. Education: Ph.D. in Probability and Statistics, CIMAT (2018) M.Sc. in Mathematics, CIMAT (2014) B.Sc. in Mathematics, Universidad de Guanajuato (2010) Research Interests: Her research spans stochastic topology, extremal combinatorics, and applications of discrete geometry to puzzles and games. She explores configuration spaces, topological properties of random structures, and innovative educational approaches like the Music Math Project, which integrates music and mathematics. Awards: Includes the Marie Skłodowska-Curie Fellowship (2020-2022), Arana-Ordaz Prize (2019), and Sofia Kovalevskaia Prize (2019). She has also received grants from CONACYT and CIMAT. Grants & Outreach: Developed workshops in marginalized communities using music and math, and contributed to software tools like the Eden Model analysis package. Active in science popularization through articles and outreach programs. Labs/Teams: Collaborated on projects like the Eden Model (topology analysis software) and extremal animal configurations, often involving interdisciplinary teams in mathematics and computer science.

Alexander Lodemannمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Automated Planning
• Domain Learning
• +۲ مورد دیگر

Alexander Lodemann is a research assistant and PhD student at the Institute of Artificial Intelligence, University of Ulm, since March 2025. Affiliated with the KEMAI Research Training Group under supervisors Prof. Dr. Birte Glimm and Prof. Dr. Ambros J. Beer, he focuses on AI planning with emphasis on domain learning and hierarchical techniques. His research spans Artificial Intelligence, Automated Planning, Domain Modelling, and Hierarchical Task Network Planning. He develops methods for automating planning knowledge acquisition and enhancing hierarchical planning systems to improve accessibility and efficiency in complex AI applications. Recent publications (2024-2025) demonstrate concentrated expertise in Hierarchical Task Network planning, addressing domain learning challenges, unsolvable problem corrections, and integer linear programming optimizations. His work bridges theoretical planning frameworks with practical implementations for complex task structures. Scientific recognition includes: iGEM competition bronze medal (2020) as Team iGEM_UUlm member Teaching contributions feature guest lecturing for "AI Planning" (SoSe 25) and tutoring "Formale Grundlagen" (WiSe 19/20), reflecting active knowledge transfer in core AI curricula. He participates in the KEMAI Research Training Group and previously contributed to the iGEM_UUlm team, leveraging interdisciplinary collaboration for competition success.

Stefan Irnichمشاهده پروفایل

استاد

• Logistics Management
• Operations Research
• Vehicle Routing Problems
• +۵ مورد دیگر

Prof. Stefan Irnich is a Professor at the Chair of Logistics Management, Johannes Gutenberg University Mainz. His research focuses on logistics optimization, vehicle routing problems, and operations research. He leads projects like the DFG-funded 'BiPISh: Bin Packing with Irregular Shapes' (2025), aiming to develop methods for packing irregular 2D shapes. His work bridges theoretical advancements and practical applications, such as integrating drones into routing systems and optimizing last-mile delivery networks. Notably, his former students Dr. Katrin Heßler and Dr. Timo Hintsch won the 2025 EURO Excellence in Practice Award for their BinPACKER project. Dr. Jeanette Schmidt received the Alfred Teves Foundation Prize for her dissertation on routing problem solutions. Irnich also organizes academic events like the SynchroTrans 2013 workshop on synchronization in transport. He maintains an active role in teaching, overseeing Master’s and Bachelor’s seminars in logistics. His research spans algorithm design (e.g., branch-price-and-cut methods), network optimization, and sustainability in transportation. Recent publications address challenges in scattered storage systems, multi-depot routing, and public transport integration.