جستجوی استاد راهنمای دانشگاهی

Gudrun Kiesmuellerمشاهده پروفایل

استاد

• Inventory Management
• After Sales Service
• Design and Analysis of Manufacturing Systems
• +۴ مورد دیگر

Prof. Dr. Dr. h.c. Gudrun Kiesmüller serves as Full Professor holding the Chair for Operations Management at TUM School of Management, Technical University of Munich, based at Campus Heilbronn since July 2019. She concurrently holds the position of Hedda Andersson Guest Professor at Lund University's Department of Industrial Management & Logistics since January 2021. Previously, she held professorial positions at Otto-von-Guericke-University Magdeburg (2013-2019), Christian-Albrechts-University zu Kiel (2010-2013), and Technical University Eindhoven (2002-2009). Her research program focuses on Operations Management with particular emphasis on the implications of digitization in Industry 4.0, especially in after-sales services. She develops analytical methods to optimize processes across manufacturing and supply chains. Her work spans three interconnected domains: stochastic manufacturing systems design (examining buffer sizing and spare parts planning), safety stock optimization under uncertain demand and supply conditions, and maintenance-reliability integration. She investigates how digitization transforms traditional operations, with growing emphasis on AI applications for supply chain optimization and inventory planning. Prof. Kiesmüller's extensive publication record reveals consistent contributions to operations research methodology with practical business applications. Her work demonstrates increasing integration of data-driven approaches, particularly in the most recent publications which explore AI applications for supply chain optimization. The research shows progression from theoretical inventory models toward more complex, integrated systems that consider multiple uncertainties simultaneously, reflecting the growing complexity of modern supply chains. Her professional recognition includes: 2022 Service Award from the International Society for Inventory Research Multiple Outstanding Reviewer Awards from OR Spectrum (2017, 2020) EURO Best Paper Award (2014) for influential review on lateral transshipments Multiple teaching awards recognizing excellence in both bachelor and master level instruction At TUM, Prof. Kiesmüller teaches a comprehensive curriculum in Operations Management, emphasizing both theoretical foundations and practical applications. Her courses equip students with skills to analyze supply chain planning problems, apply quantitative models, and solve complex operational challenges. She maintains an active research group investigating how digitization transforms operations management practices, particularly in after-sales service contexts where Industry 4.0 technologies enable new optimization possibilities.

Fabian Panseمشاهده پروفایل

پژوهشگر

• Database Systems
• Data Quality
• Probabilistic Data Management
• +۳ مورد دیگر

Dr. Fabian Panse is a Researcher at the Database and Information Systems (DBIS) group within the Department of Informatics at the University of Hamburg. His work focuses on database systems, data quality, and probabilistic data management, with significant contributions to polyglot persistence, duplicate detection, and data simulation frameworks like SmartOpenHamburg and HADeS. Research Assistant since 2009 PhD in Computer Science Research interests center on polyglot persistence , probabilistic databases , duplicate detection , and data pollution techniques . His publications span conferences like VLDB, ICDE, and workshops on database fundamentals. He has supervised over 20 theses including Master's and Bachelor's projects on topics ranging from data synthesis to smart city applications . Key collaborations include Prof. Norbert Ritter and Dr. Wolfram Wingerath.

Rolf Findeisenمشاهده پروفایل

استاد

• Model Predictive Control (MPC)
• Cyber-Physical Systems
• Battery Systems Optimization
• +۵ مورد دیگر

Prof. Rolf Findeisen is a Professor in the Department of Control and Cyber-Physical Systems (CCPS) at Technische Universität Darmstadt. His work focuses on advancing control theory and its applications in cyber-physical systems, autonomous systems, and energy storage systems. Key areas include model predictive control (MPC), battery management systems, machine learning integration into control frameworks, and optimization of crystallization processes. He leads research on safety-critical systems, data-driven control methods, and interdisciplinary applications in robotics and biotechnology. His research spans theoretical advancements in MPC stability, stochastic control, and Gaussian process modeling, alongside practical implementations in autonomous vehicles, lithium-ion battery systems, and bioprocess optimization. Notable contributions include frameworks like HILO-MPC for integrating machine learning with control systems, and methodologies for safe exploration in autonomous navigation. Prof. Findeisen's publications emphasize energy-efficient trajectory planning, fault detection in battery systems, and real-time optimization of manufacturing processes. His work bridges academic theory with industrial applications, addressing challenges in scalability, safety, and computational efficiency. His lab collaborates on national projects like IN-Fly-Tec and INFLIGHT, focusing on innovative flight control systems and sensor technologies. Current research trends include hybrid intelligent optimization, cybergenetic control of microbial systems, and safe reinforcement learning for control systems.

• Control Systems
• Model Predictive Control
• Data-Driven Methods
• +۳ مورد دیگر

Shuhao Yan is a Researcher and Post Doc Researcher at the University of Stuttgart, affiliated with the Department of Mathematics/IMNG within Faculty 08. His research focuses on advanced control systems, particularly in data-driven methods for uncertain systems, stochastic model predictive control (MPC), and grid-forming control in power electronics. He leads the SimTech Project Network 4-3 (II) titled 'Data-Integrated Control Systems Design with Guarantees,' emphasizing robustness and real-world applicability. His research interests revolve around integrating data with traditional control theory to address challenges in uncertain dynamic systems. Key areas include robust MPC, stochastic optimization, and the application of machine learning in control systems. He has contributed to advancements in grid stability analysis, voltage source converter control, and adaptive control strategies under intermittent data conditions. Recent publications highlight trends in learning-based control (e.g., homothetic tube MPC), stochastic MPC with probabilistic constraints, and power grid stability analysis for renewable energy integration. His work bridges theoretical foundations with practical implementations in energy systems and automation. Shuhao Yan holds a PhD and currently conducts research within the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling. His projects emphasize interdisciplinary collaboration between control theory, applied mathematics, and electrical engineering.

• Applied Mathematics
• Computational Methods
• Control Theory
• +۵ مورد دیگر

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

• Database Systems
• Information Retrieval
• Probabilistic Databases
• +۴ مورد دیگر

Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.

Stefan Trautmannمشاهده پروفایل

استاد

• Behavioral Finance
• Experimental Economics
• Decision Making under Uncertainty
• +۳ مورد دیگر

Prof. Stefan Trautmann is a Professor of Behavioral Finance and Contract Theory at Heidelberg University's Alfred Weber Institute. He holds leadership roles including Head of Department (2023-) and Board Member of the Alfred Weber Institute. His research focuses on behavioral economics, experimental finance, and decision-making under uncertainty. He co-leads the experimental economics group, organizing workshops and maintaining a data repository (AWI HeiDATA). Research interests include financial decisions under risk, social preferences, and the economics of religion. He has published in top journals like Journal of Risk and Uncertainty and European Economic Review , and received the 2024 Heinrich-Wiemer-Prize for Economics. He serves on editorial boards of journals such as Journal of Economic Behavior and Organization and Management Science . Teaching includes corporate governance and behavioral finance courses. He advises institutions like the German Federal Institute for Risk Assessment and chairs behavioral finance sessions at the Swiss Society for Financial Market Research. His work bridges theoretical insights with practical applications in policy and finance.

• In-flight ice accretion
• Numerical simulation and optimization of electro-thermal ice protection systems (ETIPS)
• Icing wind tunnel testing and measurement techniques
• +۶ مورد دیگر

Mariachiara Gallia is a Research Fellow at the Institute of Fluid Mechanics , Technische Universität Braunschweig , Germany. She specializes in in-flight ice accretion and numerical simulation of electro-thermal ice protection systems (ETIPS) for aerospace applications. Research Focus: Ice accretion modeling, robust optimization, uncertainty quantification, CFD, machine learning for fluid dynamics, ice roughness characterization. Projects: Participated in the European ICE-GENESIS project; currently co-supervises Ph.D. students in the MSCA DN-JD TRACES project. Education: Holds a Ph.D. in Aerospace Engineering from Politecnico di Milano, Italy. Her work spans numerical frameworks for ice protection systems, icing wind tunnel testing , and roughness effects on heat transfer . Key publications address robust optimization under uncertain cloud conditions, machine learning in fluid dynamics, and modular ETIPS designs for wind turbines.

Annalena Danielsمشاهده پروفایل

پژوهشگر

• Control Engineering
• Vertical Farming
• Industrial Robotics
• +۳ مورد دیگر

M.Sc. Annalena Daniels is a Researcher at the Chair of Control Engineering , Technical University of Munich (TUM), since May 2021. Her work focuses on control systems applied to agricultural and industrial robotics. Education: 2018–2021: Master of Science in Mechanical Engineering, University of Stuttgart (Specializations: Systems Theory and Control Engineering, Modeling and Simulation) 2014–2018: Bachelor of Engineering in Mechanical Engineering, East Bavarian Technical University of Regensburg (Specializations: Modeling and Simulation, Energy Engineering) Research interests include: Vertical farming systems using closed-loop control for crop optimization. Robotic recycling with haptic-enhanced precision in uncertain environments. Advanced fault diagnosis and detection in dynamic systems. Recent publications highlight her expertise in adaptive control frameworks, with applications to greenhouse climate regulation, vertical farm irrigation, and robotic recycling. Her work often integrates machine learning and dynamic modeling for agricultural and industrial automation. Contact: a.daniels@tum.de Labs & Projects: She contributes to the Chair of Control Engineering at TUM, including the HR Recycler project for hybrid human-robot electronic recycling systems.

Patrick Hürtgenمشاهده پروفایل

استاد پژوهش

• International Macroeconomics
• Monetary Economics
• Fiscal Policy
• +۳ مورد دیگر

Patrick Hürtgen is a Research Professor at the Research Centre of the Deutsche Bundesbank. His work focuses on international macroeconomics, monetary policy design, fiscal sustainability, and the role of expectations in economic decision-making. He has contributed to understanding the effects of monetary policy at the zero lower bound, fiscal policy during crises, and the transmission of monetary shocks across economies with fixed vs. floating exchange rates. Key research interests include imperfect information in expectations formation, which influences how households and firms respond to central bank communication. His studies have highlighted the importance of disagreement in inflation expectations for policy efficacy and the differing impacts of monetary policy under varying economic regimes. Recent work examines Bundesbank policy spillovers to European economies, emphasizing the role of exchange rate regimes in determining policy transmission. Hürtgen's publications span high-impact journals like the Journal of Political Economy and Journal of Monetary Economics , with a focus on empirical macroeconomics and policy analysis. His research bridges theoretical models with real-world data, often utilizing historical policy archives to construct novel measures of monetary shocks. He has collaborated on ECB policy communication strategies and co-authored foundational studies on the effectiveness of forward guidance. His work underscores the necessity of incorporating realistic expectations dynamics into macroeconomic frameworks to better guide policy decisions in uncertain environments.

• Epidemiology
• International Public Health
• Global Health
• +۲ مورد دیگر

Mariana Lopes Simoes is a Scientific Associate at the Faculty of Health Sciences, Bielefeld University, affiliated with Working Group 3 Epidemiology & International Public Health. She is also connected to the Center for Uncertainty Studies (CeUS), indicating engagement in interdisciplinary research involving uncertainty across domains. Her work focuses on epidemiology and international public health, suggesting expertise in population health, global disease patterns, and health systems under uncertain conditions. The affiliation with CeUS highlights a methodological interest in managing and analyzing uncertainty in health-related data and policies. Scientific Affiliations: Faculty of Health Sciences, Bielefeld University Working Group 3: Epidemiology & International Public Health Center for Uncertainty Studies (CeUS) There is no public information available on her publications, awards, or teaching activities in the provided text. No email is accessible without institutional login.

• Database Systems
• Data Management
• XML Technologies
• +۶ مورد دیگر

Serge Abiteboul is a prominent researcher affiliated with INRIA (France) specializing in database systems, data management, and distributed computing. His work spans foundational research in XML technologies, active XML systems, probabilistic databases, and ethical data science. He has contributed to influential projects like the Active XML framework and pioneered research on distributed data management systems. Holds the SIGMOD Edgar F. Codd Innovations Award (1998) Co-authored over 300 publications across journals like ACM TODS , VLDB Journal , and conferences such as SIGMOD, PODS, and ICDE Key research areas include: XML query languages and architectures Probabilistic and uncertain data management Workflow systems and collaborative computing Ethical considerations in data systems Notable contributions include the WebdamLog system for distributed data management and foundational work on Active XML . His recent focus on responsible data science addresses transparency, fairness, and regulatory compliance challenges.

• Temporal Data Representation
• Semantic Web
• Uncertainty Management
• +۳ مورد دیگر

Nassira Achich is a researcher at the Cédric Laboratory, Conservatoire National des Arts et Métiers (CNAM), specializing in temporal data representation and imperfection handling within Semantic Web frameworks. Her work develops OWL 2 ontological solutions for uncertain, imprecise, and conflicting temporal data with applications in Alzheimer's care systems and collective memory projects. She holds a PhD jointly awarded by HESAM Université and Université de Sfax (Tunisia) in 2021 for her thesis on temporal data imperfection in Alzheimer's patient software. Her educational background bridges French and Tunisian academic institutions with a focus on practical healthcare applications. Dr. Achich's research centers on integrating imperfection theories (possibility, probability, evidence theory) into semantic technologies for robust temporal reasoning. She addresses core challenges in representing uncertain time intervals, resolving data conflicts, and modeling imprecision within ontological frameworks, contributing significantly to knowledge representation under uncertainty. Analysis of her 12 publications (2019-2024) reveals consistent methodological progression from foundational typologies of temporal imperfection (2019) to advanced ontology implementations (2024). Her work spans theoretical comparisons of uncertainty frameworks and practical implementations in healthcare contexts, published in venues including Journal of Intelligent and Fuzzy Systems, New Generation Computing, and ISWC. No scientific awards or honors are documented in the source material. Information regarding student supervision, grant funding, or administrative roles is absent from the provided publications list. Her academic contributions appear focused on independent research within the Cédric Laboratory's collaborative environment. As a member of Cédric Laboratory's research teams, she contributes to axes including 'Complex data, machine learning and representations' and 'Data mining and statistics,' working within interdisciplinary groups advancing semantic technologies for real-world data challenges.

Hao Yanمشاهده پروفایل

استاد

• Control Systems
• Model Predictive Control (MPC)
• Stochastic Control
• +۳ مورد دیگر

Prof. Hao Yan is a Professor at the 2nd Physics Institute of the University of Stuttgart, affiliated with Faculty 08. He has been honored with the Humboldt Research Award and is recognized as an International Partner by the Baden-Württemberg Stiftung. His research focuses on advanced control systems, including model predictive control (MPC), stochastic control, optimization, and DNA nanotechnology. Yan's work integrates machine learning and robust methodologies to address challenges in dynamic systems and power electronics. His research interests span control theory applications in electrical engineering, with a particular emphasis on grid-forming converters, frequency stability, and energy systems. He also explores the intersection of nanotechnology and control systems. Recent publications (2018–2025) highlight trends in stochastic MPC, distributionally robust optimization, and learning-based control strategies. Notable contributions include work on tube MPC, intermittent observation handling, and discounted probabilistic constraints in uncertain environments. His research bridges theoretical advancements with practical applications in smart grids and adaptive control systems. Prof. Yan has received prestigious awards including the Humboldt Research Award and international partnerships supporting his research in top-tier institutions. His work is disseminated through leading journals and conferences in control systems and electrical engineering.

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.