جست‌وجوی استاد و پژوهشگر

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).

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

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.

Paul Wentgesمشاهده پروفایل

استاد

• Management Control Systems
• Value-oriented Controlling
• Performance Management
• +۴ مورد دیگر

Prof. Paul Wentges is a Professor and Director of the Institut für Controlling at the University of Ulm's Faculty of Mathematics and Economics. He holds a Diplom in Wirtschaftsmathematik from Ulm University (1991), an M.Sc. in Mathematics from Syracuse University (1988), and a Dr. oec. from the University of St. Gallen (1994). His career includes roles as a researcher at the University of St. Gallen, financial consultant at Westdeutsche Landesbank, and academic leadership positions at the University of Ulm and University of Vienna. His research focuses on Management Control Systems, Performance Management, Family Businesses, and Corporate Governance. Notable contributions include work on organizational social capital's impact on control systems and stakeholder theory applications in corporate finance. He has served as Dean of the Faculty of Mathematics and Economics (2011–2013) and holds leadership roles in academic associations like the European Accounting Association. Education: 1984–1991: Diplom in Wirtschaftsmathematik (Ulm University) 1987–1988: M.Sc. Mathematics (Syracuse University) 1991–1994: Dr. oec. (St. Gallen University) 1996–2002: Habilitation (Ulm University) Awards: Amicitia-Preis (1994) SVOR/ASRO Prize (1997) Key Research Themes: Strategic performance measurement Risk management in corporate finance Family business governance Stakeholder-oriented corporate control His publications span management accounting, operations research, and corporate governance, with a focus on bridging theoretical concepts with practical managerial challenges. He actively contributes to academic communities through editorial roles and association memberships.

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.

Christian Haaseمشاهده پروفایل

استاد

• Lattice Polytopes
• Toric Algebra
• Tropical Geometry
• +۳ مورد دیگر

Prof. Dr. Christian Haase is a Professor at the Department of Mathematics within the School of Mathematics and Computer Science at Freie Universität Berlin. He leads the Discrete Algebraic Geometry & Math for Teaching Post workgroup. His research focuses on lattice polytopes, toric algebra, tropical geometry, combinatorial commutative algebra, algorithmic algebra, and geometric/topological combinatorics. He has held a permanent academic position since at least 2008, as evidenced by his extensive publication record from that period onward. His work includes collaborations on foundational topics like Ehrhart theory, integer decomposition properties, and convex polytope decompositions. His research contributions span theoretical advances in algebraic geometry and discrete mathematics, with applications to computational algebra and geometric combinatorics. Notable works include studies on the finiteness threshold width of lattice polytopes and the exploration of Gaussian mixture models' mode counts. He maintains an active role in mentoring PhD students and postdoctoral researchers in his workgroup, though specific advisee names are not listed here. His research has been published in top journals such as Advances in Mathematics, Mathematische Zeitschrift, and the Annals of Combinatorics. Haase's academic activities include organizing research projects and conferences, and he serves as a key contributor to the mathematical community through his editorial work and collaborative research initiatives.

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

• Optimization Algorithms
• Supply Chain Management
• Production Planning
• +۴ مورد دیگر

Achim Koberstein is an Assistant Professor at Johann Wolfgang Goethe University, Frankfurt am Main, where he holds the Detlef Hübner Endowed Chair for Business Administration, specializing in Logistics and Supply Chain Management within the Department of Economics. His research focuses on optimization methodologies applied to industrial and logistical challenges. Research Focus Professor Koberstein's work spans three primary domains: Optimization algorithms and solver technology, production and supply network planning systems, and gas market optimization frameworks. His research integrates mathematical modeling with practical applications in: Advanced algorithm development for large-scale linear programming Strategic planning in automotive manufacturing systems Risk-optimized procurement in liberalized energy markets Sustainable logistics network design Publication Analysis Koberstein's recent publications (2006-2010) demonstrate consistent focus on optimization techniques with industrial applications. His work evolves from foundational algorithm improvements (dual simplex enhancements, Gomory cut strengthening) to domain-specific implementations in automotive production planning, gas procurement, and logistics networks. A strong emphasis emerges on uncertainty modeling, resource optimization, and large-scale system design.

Michael Palkمشاهده پروفایل

پژوهشگر

• Graph Theory
• Data Science
• Public Transport
• +۴ مورد دیگر

Michael Palk is an external PhD student (since 2024) and former research associate (2019-2023) at the Institute of Information Systems, University of Hamburg under Prof. Stefan Voß. He holds an MSc (2019, Distinction) and BSc (2017) in Business Mathematics from the University of Hamburg. His research focuses on applying graph theory, data science, and optimization techniques to public transport and energy systems, alongside social media analytics. Key contributions include advancements in graph neural networks for combinatorial optimization and bilingual sentiment analysis frameworks. He has published 3 peer-reviewed articles in IEEE Access, Information Systems Frontiers, and international workshops. Education 2017-2019: MSc Business Mathematics (Distinction), University of Hamburg 2012-2017: BSc Business Mathematics, University of Hamburg Research Graph neural networks for NP-hard problems Social media analytics for product events Optimization models for power distribution networks Affiliations Institute of Information Systems Faculty of Business Administration

• Power Systems
• Energy Economics
• Grid Integration
• +۳ مورد دیگر

Albert Moser is a Professor and Chair of Transmission Grids and Energy Economics at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology. His research focuses on power system optimization, energy market design, and grid integration of renewable resources, with emphasis on developing modeling frameworks for future energy systems. His technical expertise encompasses congestion management strategies, grid stability analysis for converter-dominated systems, and integrated energy system planning. Recent work investigates water-energy interdependencies for hydrogen production, complexity reduction techniques for stability assessment, and curative remedial action planning for enhanced grid resilience. Moser's publications demonstrate consistent focus on practical solutions for energy transition challenges, including market-based approaches for grid charge allocation, uncertainty-aware infrastructure planning, and advanced modeling of HVDC systems and wind generation clusters. His group develops computational tools for security-constrained optimization and hybrid simulation techniques to address power quality issues.