جست‌وجوی استاد و پژوهشگر

Martin Brombergerمشاهده پروفایل

پژوهشگر

• Automated Reasoning
• Theorem Proving
• Linear Arithmetic
• +۳ مورد دیگر

Dr. Martin Bromberger is a Senior Researcher at the Max Planck Institute for Informatics, specializing in Automated Reasoning , Linear Arithmetic , and Theorem Proving . He is affiliated with the Automation of Logic research group (RG1), focusing on combinations of theories and arithmetic reasoning. His recent work includes publications at top venues like TACAS, FroCoS, and VMCAI. He has developed critical SMT solvers such as SPASS-IQ and SPASS-SATT , advancing constraint-solving techniques in linear arithmetic. His research spans Arithmetic Decision Procedures Datalog Applications Bernays-Schoenfinkel Fragment Cube-Based Arithmetic Optimization He received awards at SMT-COMP 2018, SMT-COMP 2019, and the Best Student Paper Award at CADE-27 for his contributions to SMT solving.

Stephan Mescherمشاهده پروفایل

پژوهشگر

• Algebraic Topology
• Differential Geometry
• Robot Motion Planning
• +۵ مورد دیگر

Stephan Mescher is a Researcher at the Institute of Mathematics within the Faculty of Natural Sciences II at Martin Luther University Halle-Wittenberg. Based at the Georg Cantor House on Theodor-Lieser-Strasse in Halle (Saale), he conducts advanced research at the intersection of algebraic topology and differential geometry with applications in robotics. His research focuses on: Topological complexity and spherical complexities in motion planning Geodesic analysis in Riemannian and Finsler manifolds Morse theory and A-infinity algebra structures Closed geodesics and critical point theory Geometric properties of configuration spaces for robots Recent publications reveal a cohesive trajectory toward sequential topological complexity and fibered decompositions of cut loci, demonstrating how abstract topological invariants solve concrete problems in robot navigation. His work bridges pure mathematics with engineering applications through rigorous analysis of manifold structures. No scientific awards were documented in available sources. Information regarding student supervision, research grants, or collaborative teams was not provided in the source materials.

• Communication Networks
• Time-Sensitive Networking (TSN)
• Network Technologies
• +۵ مورد دیگر

Manuel Eppler is a doctoral student and research assistant at the University of Tübingen , affiliated with the Faculty of Mathematics and Natural Sciences and the Department of Computer Science , specifically the Chair of Communication Networks . He holds a B.Sc. in Computer Science (2020), a B.Sc. in Mathematics (2021), and an M.Sc. in Computer Science (2022), all from the University of Tübingen. Education B.Sc. in Computer Science (2016-2020) B.Sc. in Mathematics (2018-2021) M.Sc. in Computer Science (2020-2022) His research focuses on communication networks , particularly Time-Sensitive Networking (TSN) and scheduling algorithms . Key contributions include work on Integer Linear Programming (ILP) for TSN scheduling and clustering algorithms for BIER-based multicast scalability. He also explores integrating container platforms with TSN for industrial IoT applications. Recent publications highlight advancements in TAS (Time-Aware Shaper) performance optimization and container-adjacent network management. His work bridges theoretical modeling (e.g., ILP, clustering) with practical implementations in industrial networks. Eppler collaborates with Siemens AG 's Industrial Networks division, contributing to lectures on topics like network simulation , cybersecurity , and IT service management . He is associated with the Chair of Communication Networks at the University of Tübingen, which provides a hub for his research activities.

Michael Schusterمشاهده پروفایل

پژوهشگر ارشد

• PDE Constrained Optimization
• Optimization under Uncertainty
• Probabilistic Constraints
• +۳ مورد دیگر

Michael Schuster is a Research Fellow at the Chair for Dynamics, Control, Machine Learning and Numerics (DCN-AvH) at Friedrich-Alexander University Erlangen-Nuremberg (FAU), operating under the Alexander von Humboldt Professorship. He actively contributes to the CRC TRR154 project focused on gas network optimization. His academic foundation includes: Industrial Mathematics studies at FAU PhD in Mathematics from FAU (2021) with dissertation on nodal control and probabilistic constrained optimization for gas networks His research specializes in PDE constrained optimization, optimization under uncertainty, and probabilistic robustness, with direct applications to gas network systems. He has pioneered work on the turnpike phenomenon in optimal control under uncertain conditions, establishing critical theoretical frameworks for long-horizon control problems. Analysis of his publication record reveals a consistent focus on mathematical optimization of energy infrastructure, particularly addressing compressor station placement, boundary control of wave equations, and probabilistic constraints in flow networks. His interdisciplinary approach bridges pure mathematics with engineering applications in gas transport systems. Supported by the German Research Foundation (DFG) through the CRC TRR154 grant, his work integrates advanced numerical methods with real-world energy market modeling. He collaborates extensively within the FAU MoD (Mathematics and Data Science) initiative and maintains active international research partnerships.

Martin Middendorfمشاهده پروفایل

استاد

• Swarm Intelligence
• Bioinformatics
• Genome Rearrangement Analysis
• +۷ مورد دیگر

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 .

Steffen Goebbelsمشاهده پروفایل

استاد

• 3D City Modeling
• Computer Graphics
• Numerical Analysis
• +۵ مورد دیگر

Prof. Dr. Steffen Goebbels is a Professor of Mathematics and Computer Science at Niederrhein University of Applied Sciences, Faculty of Electrical Engineering and Computer Science in Krefeld, Germany. He maintains an office in room F 202 and is actively involved in teaching and research. His academic work spans multiple disciplines with a strong focus on applied mathematics and computer science. His research interests center around 3D city modeling, mathematical optimization, and computer graphics. He has made significant contributions to the field of CityGML data processing and has developed algorithms for calculating 3D building models from land registry data and laser scan data. His work with the iPattern Institute has led to practical applications in cities like Krefeld, Leverkusen, and Dortmund. He has also contributed to neural network approximation theory and various optimization problems. Prof. Goebbels has published extensively in recent years, with publications spanning computer graphics, mathematical optimization, and machine learning. His research shows a consistent pattern of applying mathematical techniques to solve practical problems in 3D modeling and computer vision. He has also co-authored several influential textbooks on mathematics for computer science students. He has received recognition for his work through publications in reputable journals and conference proceedings, though specific awards are not mentioned in the available information. His research has practical applications in urban planning, architectural visualization, and manufacturing processes. Prof. Goebbels is actively involved in teaching mathematics courses (Mathematics 1-3), Numerical Analysis, Logic Programming, Functional Programming, and Scientific Computing. He has developed teaching materials including online courses and textbooks that are widely used in his institution.

Lei Buمشاهده پروفایل

استاد

• Software Verification
• Software Testing
• Software Analysis
• +۸ مورد دیگر

Lei Bu is a Professor and Vice Dean of the Software Institute at Nanjing University, China. He has been with Nanjing University since 2010, progressing from Assistant Professor to his current position. His academic career includes a visiting position at Microsoft Research Asia through their StarTrack Program from 2014-2015. Professor Bu received his B.Sc. and Ph.D. degrees in Computer Science from Nanjing University, with additional research experience at Carnegie Mellon University and University of Texas at Dallas during his doctoral studies. His academic journey shows steady progression through the ranks at his alma mater. Lei Bu's research primarily focuses on software verification, testing, and analysis, with particular expertise in model checking, bounded model checking, formal methods, and cyber-physical systems. His work spans theoretical foundations to practical applications in safety-critical systems. Recent publications demonstrate growing interest in integrating machine learning techniques with traditional formal methods, as seen in works like SpecGen that leverage large language models for program specification generation. His research outputs show a consistent focus on verification techniques for complex systems, particularly addressing challenges in hybrid systems, cyber-physical systems, and IoT applications. Professor Bu has developed several verification tools including BACH (Bounded Reachability Checker for Linear Hybrid Automata) and BRICK (Bounded Reachability Checker of Numerically-Intensive C Code), which have been used in international verification competitions. 2023: Zhongchuang Software Talent Award 2022: CCF-IEEE CS Young Computer Scientist Award 2020: The Outstanding Teachers of Computing in Higher Education Award Program 2019: NASAC Young Software Innovation Award 2016: Young Talent Development Program 2014: StarTrack Program Visiting Young Faculty 2007: Full Scholarship under the State Scholarship Fund Professor Bu serves as a Principal Investigator on multiple significant research projects funded by the Natural Science Foundation of China, including a Key Program grant (2023-2027) on dynamic adjustment-control-fault tolerance theory. He has also advised numerous students and taught courses including Formal Languages and Automata, Theoretical Foundation of Software Engineering, and Preliminary Introduction to the Theory of Computation. His laboratory work focuses on developing practical verification tools for industrial applications in cyber-physical and IoT systems.

Simon Hilpertمشاهده پروفایل

پژوهشگر

• Energy System Modeling
• Mathematical Optimization of Energy Systems
• Renewable Energy Integration
• +۳ مورد دیگر

Simon Hilpert is a Researcher at Europa-Universität Flensburg, affiliated with the Interdisciplinary Institute for Environmental, Social and Human Sciences and the Department of Energy and Environmental Management. He has served as a research assistant since 2013 and teaches energy system modeling in the Master's program EUM (Energy and Environmental Management). His educational background includes: Bachelor's in Industrial Engineering (specializing in Energy and Environmental Management) from Flensburg University of Applied Sciences (2007-2011), including a semester abroad at Universidade Nova de Lisboa Master's in Systems Engineering from Flensburg University of Applied Sciences (2011-2013), with thesis on combined heat and power plant modeling using mixed-integer linear optimization Hilpert's research focuses on energy system modeling and optimization using mathematical programming and open-source tools (Python, Pyomo, MathProg). He is a principal developer of the Open Energy Modelling Framework (oemof), particularly the solph module for energy system optimization. His doctoral work analyzes modeling approaches for future electricity-heat coupled energy systems, critical for renewable energy integration. His methodology emphasizes reproducibility and open science in energy transition research. His publications reveal a cohesive research trajectory centered on advancing open-source energy modeling. Key contributions include oemof.solph for optimization, district heating system integration, framework development for open science, and qualitative evaluation methodologies. These works collectively address computational challenges in modeling complex energy transitions, with emphasis on district heating integration and reproducible research practices. Hilpert actively contributes to the oemof community and collaborates within the Interdisciplinary Institute for Environmental, Social and Human Sciences. His work supports sustainable energy system design through robust open-source tools, directly impacting academic and practical energy transition efforts in the German-Danish border region and beyond.

• Hyperdimensional Computing
• Edge AI
• Neural Network Acceleration
• +۳ مورد دیگر

Tajana Rosing is a Professor at the University of California, San Diego, specializing in hyperdimensional computing, neural network acceleration, and edge AI systems. Her work bridges hardware-software co-design for IoT, biomedical applications, and security-enhanced computing. University: University of California, San Diego Research Focus: Hyperdimensional Computing, Edge AI, IoT Systems, Neural Acceleration, Secure Machine Learning Her recent publications focus on energy-efficient neural network acceleration (e.g., Processing-in-Memory architectures), few-shot learning for edge devices, and hyperdimensional methods for sensor data and biomedical applications. She explores robustness against adversarial attacks and federated learning frameworks. Key trends include hyperdimensional computing for multimodal sensor fusion, transformer optimization via hardware-software co-design, and privacy-preserving edge AI. Collaborations often involve co-authors like Xiaofan Yu, Minxuan Zhou, and Onat Güngör. Her work impacts domains such as healthcare (glucose prediction, ECG analysis), cybersecurity (intrusion detection), and bioinformatics (mass spectrometry, viral sequence analysis).

Sönke Hartmannمشاهده پروفایل

استاد

• Operations Research
• Logistics
• Optimization Algorithms
• +۳ مورد دیگر

Sönke Hartmann is Professor for Operations Research and Logistics at the Hamburg School of Business Administration (HSBA) since 2007. His academic work focuses on optimization algorithms, resource-constrained project scheduling, and container terminal optimization. He teaches courses in Operations Research, Statistics, Mathematics, Computer Science Foundations, AI & Data Science, and Data Analysis. Professor Hartmann completed his Computer Science studies at the University of Kiel and earned his PhD in Economics with a specialization in Operations Research. His doctoral research laid the foundation for his subsequent work in mathematical optimization and scheduling problems. His research spans multiple areas within operations research and logistics. Dr. Hartmann has made significant contributions to resource-constrained project scheduling, developing algorithms that address complex resource allocation challenges. His work on container terminal optimization has practical applications in port logistics, focusing on improving efficiency through simulation and mathematical models. He has also explored applications of mathematical programming to solve logic puzzles and smartphone applications, demonstrating the versatility of optimization techniques across different domains. Professor Hartmann's publication record shows a clear evolution from theoretical scheduling algorithms to practical applications in container terminals. His recent work (2025) provides a comprehensive review of fifty years of research on resource-constrained project scheduling, while his earlier publications established foundational work in genetic algorithms for project scheduling. The journal distribution shows strong representation in top-tier operations research journals like European Journal of Operational Research. As an active member of the academic community, Professor Hartmann serves as a reviewer for more than 30 scientific journals, contributing his expertise to maintain scholarly standards in operations research and logistics publications. His research has practical implications for container terminal operations, with applications in scheduling equipment and manpower, optimizing container movements, and improving overall terminal efficiency through simulation models. This industry-focused research demonstrates the real-world impact of his academic work.

Falk Hanteمشاهده پروفایل

استاد

• Optimization of complex systems
• Optimal control of partial differential equations
• Mixed-integer optimal control
• +۵ مورد دیگر

Prof. Falk Hante is a Professor of Applied Mathematics at Humboldt University of Berlin's Faculty of Mathematics and Natural Sciences, specializing in optimization of complex systems. He holds positions as Deputy Managing Director of the Institute of Mathematics and spokesperson roles in TRR154 (Mathematical Modeling of Gas Networks). His research focuses on optimal control of dynamic systems, particularly involving partial differential equations, with applications in gas networks and hybrid systems. Education: PhD in Mathematics (2010), FAU Erlangen-Nuremberg Habilitation (2018), FAU Erlangen-Nuremberg Research Interests: He investigates applied analysis, numerical mathematics, and mathematical optimization, with emphases on: Optimization of switching systems Network dynamics control Model predictive control Gas transport network optimization Recent Articles Trends: Recent work emphasizes gas network optimization, stabilization of PDE systems, and general equilibrium problems. Methodologies include mixed-integer programming and relaxation techniques for PDE-constrained systems. Awards: STAEDTLER Promotional Prize (2011) MATCH Postdoctoral Fellowship (2011–2012) Advising/Grants: Supervises research projects including Math+ (Decision-Making for Energy Networks) and DFG-funded TRR154 subprojects. Former advisees include Yan Brodskyi and Fabian Rüffler. Labs/Teams: Leads the Optimization of Complex Systems working group, collaborating with researchers like Michael Hintermüller on algorithmic optimization seminars and gas network modeling.

Michael Elberfeldمشاهده پروفایل

استاد

• Computer Science
• Theoretical Foundations
• Parameterized Complexity
• +۶ مورد دیگر

Prof. Dr. Michael Elberfeld is a faculty member at the Technical University of Central Hesse , affiliated with the Department of Mathematics, Natural Sciences and Computer Science . His research focuses on theoretical computer science, particularly in computational and parameterized complexity, logic in computer science, and algorithmic meta-theorems. Education : Doctorate in Theoretical Computer Science from the University of Lübeck (2012); Diploma (MSc) in Computer Science from the University of Lübeck (2007). His work explores the intersection of graph theory , logic , and space-bounded computation , with groundbreaking contributions to problems on bounded tree-width structures and order-invariant logics . He has developed logspace algorithms for graph canonization and studied succinctness tradeoffs in logical formalisms. Key publications include analyses of parameterized space complexity , algorithmic meta-theorems , and applications to bioinformatics like haplotype inference and network orientation. His research is supported by the European Commission through grants 648276 and P 28699 . He collaborates extensively with researchers such as Martin Grohe , Pascal Schweitzer , and Till Tantau , bridging theoretical logic with practical applications in staff rostering and biological network analysis .

Lukas Goschمشاهده پروفایل

پژوهشگر

• Robust Machine Learning
• Graph Neural Networks
• Combinatorial Optimization
• +۵ مورد دیگر

Lukas Gosch is a PhD student at the Technical University of Munich , affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . He is part of the DAML research group under the supervision of Prof. Stephan Günnemann and the relAI graduate school . Research Focus : Robustness in machine learning, graph neural networks (GNNs), combinatorial optimization, adversarial verification, and efficient ML. Education : M.Sc. in Computational Science (2018-2021, University of Vienna), B.Sc. in Physics (2013-2017, Vienna University of Technology). His work investigates how to certify and improve the robustness of neural networks, particularly against label/data poisoning and backdoor attacks. He leverages techniques like neural tangent kernels and mixed-integer programming to derive theoretical guarantees on model behavior. Recent papers analyze robustness plateaus and semantic-aware adversarial examples. Recent Scientific Recognition : Best Paper Award @ NeurIPS 2024 AdvML Frontiers Workshop Selected Oral Talk @ NeurIPS TSRML 2022 Best Master's Thesis Award @ Austrian Society for Operations Research 2021 Performance Scholarship @ University of Vienna 2020

• Graph Theory
• Combinatorics
• Optimization
• +۳ مورد دیگر

Laura Gellert is a researcher at the Institute of Optimization and Operations Research at the University of Ulm. Her work focuses on advanced graph theory problems, including colorings, decompositions, and structural properties of complex graphs. Research Interests: Graph coloring, cycle decompositions, quadrangulations, t-perfect graphs, degree sequences, and treewidth-related problems. Awards: No specific awards mentioned. Teaching Activities: She has taught courses such as Graph Theory, Optimization I/II, and seminars on discrete mathematics and optimization at the University of Ulm from 2013 to 2017.

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.