Find Academic Supervisors

• Artificial Intelligence in Manufacturing
• Production Engineering
• Circular Economy in Manufacturing
• +4 more

Dr. Amon Göppert serves as Professor and Chair of the Intelligence in Quality Sensing group at RWTH Aachen University's Laboratory for Machine Tools and Production Engineering (WZL). His work integrates artificial intelligence into manufacturing processes to enhance quality control, production efficiency, and sustainable practices. Göppert's research spans intelligent manufacturing systems with core expertise in AI-driven production engineering, circular economy applications, and advanced assembly systems. He investigates how machine learning optimizes production ramp-up, disassembly processes, and flexible manufacturing while developing sensor-based quality control solutions for industrial applications. His recent publications reveal strong trends toward AI implementation in production planning, with emphasis on worker assistance systems for disassembly, digital twin applications for real-time control, and mobile robotics in line-less assembly environments. Key focus areas include sustainable manufacturing, metrology innovation, and adaptive scheduling systems. Göppert leads multiple high-impact research initiatives: AI-driven Product Development: Machine learning for smart measurement strategies in metrology MetaVision Consortium: Industrial metaverse applications for AI-supported vision systems Generative AI for Non-Destructive Testing optimization Cluster of Excellence Internet of Production participation As Chief Engineer at WZL, he oversees technical implementation of research projects and collaborates with industry partners to translate innovations into practical manufacturing solutions, particularly in adaptive assembly systems and quality sensing technologies.

Matthias BeckerView profile

Professor

• Human-Computer Interaction
• Simulation and Modeling
• Distributed Systems
• +6 more

Matthias Becker is a Professor at the Institute for Practical Computer Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been a core member of the Human-Computer Interaction group since 2019. He serves as Internship Coordinator for Computer Science and Computer Engineering and holds key roles in the Computer Science Examination Board and Selection Committee, actively shaping academic governance and student development. His academic journey began with PhD studies at the University of Bremen (1996-2000) supported by a DFG grant, followed by a postdoctoral permanent position at Leibniz University Hannover (2000-2019), an Associated Assistant Professor role at École des Mines de Nantes (2000), and a Habilitation in Computer Science in 2013. This foundation enabled his transition to a full professorship in 2019. Becker's research spans Human-Computer Interaction, Simulation and Modeling, and Bio-inspired Computing, with applications in agriculture, renewable energy, and manufacturing. His work integrates distributed systems, optimization algorithms, and wireless sensor networks to solve complex real-world problems, such as greenhouse monitoring, wind farm logistics, and tire noise reduction. Recent publications reveal a strategic focus on practical validation of simulation models and cross-domain applications of nature-inspired algorithms. His 15 most recent publications (2018-2024) demonstrate consistent innovation in applying simulation techniques to offshore wind farm installation, agricultural pest management, and sports science. These works emphasize real-world validation, collaborative problem-solving, and the development of domain-specific optimization frameworks that bridge theoretical algorithms and industrial implementation. As Internship Coordinator, Becker facilitates critical industry-academia connections for students, while his examination board responsibilities ensure rigorous academic standards. His leadership in the Human-Computer Interaction group drives research on interactive systems for agriculture, energy, and health, with particular emphasis on user-centered design in complex operational environments like wind farm logistics and greenhouse automation.

• High Performance Computing
• Network Simulation
• Parallel Discrete Event Simulation
• +5 more

Kevin A. Brown is an active researcher in the field of High Performance Computing with a strong publication record spanning over a decade. His work primarily focuses on HPC network simulation, performance analysis, and optimization of parallel computing systems. He has collaborated extensively with researchers including Christopher D. Carothers, Robert B. Ross, and Satoshi Matsuoka across multiple institutions. Dr. Brown's research interests center around network simulation techniques, particularly Parallel Discrete Event Simulation (PDES) for modeling HPC networks. His recent work explores multi-fidelity network simulation frameworks, surrogate modeling for performance prediction, and machine learning applications for network traffic forecasting. He has made significant contributions to understanding network congestion, quality-of-service mechanisms, and the interference between different types of traffic in HPC environments. His publication record shows consistent output with 19 publications documented between 2014 and 2025, with increased productivity in recent years. The 2023-2025 period shows particularly strong activity with 12 publications, indicating ongoing research momentum. His work appears primarily in top HPC conferences including SIGSIM-PADS, CLUSTER, and ICPP. Notable recent contributions include the development of MFNetSim for multi-traffic modeling of Dragonfly systems, research on zombie packet techniques for hybrid PDES simulation, and work on steady-state fluid models for HPC networks. His research demonstrates a clear trajectory from fundamental network performance analysis toward more sophisticated simulation frameworks incorporating machine learning techniques.

• Artificial Intelligence
• Machine Learning and Deep Learning
• Generative AI
• +7 more

Sasanka Potluri serves as Professor of General Computer Science and Medical Informatics at Karlshochschule (Karlsruhe University of Education) since September 2025. He is actively engaged in teaching and research within the Department of Computer Science and Medical Informatics, focusing on the intersection of artificial intelligence and healthcare applications. His academic leadership spans multiple research projects aimed at transforming healthcare delivery through technological innovation. His educational background includes: Dr.-Ing. in Electrical Engineering and Information Technology from Otto-von-Guericke University Magdeburg (Germany) Dipl.-Ing. in Information Technology from Alpen-Adria University Klagenfurt (Austria) B. Tech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Kakinada (India) Professor Potluri's research spans the cutting edge of artificial intelligence applications in healthcare, with particular expertise in machine learning, deep learning, and generative AI. His work bridges technical innovation with practical healthcare solutions, focusing on clinical decision support systems, biomedical statistics, and digital signal processing. He has developed novel approaches for healthcare logistics optimization, synthetic health data generation, and addressing digital health equity issues. His research methodology combines theoretical rigor with practical implementation, often working at the intersection of computer science, medical informatics, and systems engineering. His publication record reveals a consistent trajectory from industrial control systems security toward healthcare applications of AI. Early work focused on intrusion detection in industrial control systems using deep learning techniques, while recent publications demonstrate a strategic shift toward healthcare logistics, patient transportation optimization, and blood product management. This evolution reflects both his technical expertise in AI and his commitment to addressing critical challenges in healthcare delivery systems. His research increasingly incorporates generative AI approaches to solve complex healthcare resource allocation problems. Professional service includes: Member and Reviewer at GMDS (German Society for Medical Informatics, Biometry and Epidemiology) since 2024 Reviewer for European Federation for Medical Informatics since 2024 Reviewer for IEEE Transactions on Network and Service Management since 2020 Reviewer for Elsevier Journals including Engineering Applications of Artificial Intelligence since 2017 Professor Potluri actively supervises B.Sc, M.Sc, and PhD students in medical informatics, AI applications, generative AI, clinical decision support systems, and healthcare logistics. His current research projects focus on hospital resource and process optimization, synthetic health data generation, digital health equity studies, and generative AI in healthcare. He previously held research positions as Junior Research Group Leader at University Hospital Jena, Project Leader at Otto von Guericke University Magdeburg, and Research Assistant for EU Projects, building a strong foundation for his current interdisciplinary work.

Gunter BolchView profile

Professor

• Performance Modelling
• Process Control
• Operating Systems
• +6 more

Dr. Gunter Bolch was a Professor and former head of the Performance Modelling and Process Control research group at the Department of Computer Science 4 (Distributed Systems and Operating Systems) of Friedrich-Alexander-Universität Erlangen-Nürnberg. He held positions such as Akademischer Direktor and was a Visiting Professor at institutions like the Catholic University of Rio de Janeiro. His work focused on performance modeling using queueing networks, stochastic Petri nets, and Markov chains, with applications in telecommunications and operating systems. Education: Studied Telecommunication at Technical Universities of Karlsruhe and Berlin (Ph.D. 1973). Retired in 2006 but remained active in research and academia until his passing in 2008. Research interests spanned performance evaluation, process control, and analytical methods for computer systems. He authored/co-authored 7 books and over 130 publications, including influential works on queueing networks and MOSEL modeling language. Key contributions include the PEPSY and MOSEL tools for performance analysis. Collaborated internationally on conferences like ASMTA and ESS. His work integrated theoretical models with practical applications, impacting both academia and industry.

Stefan KuhlinsView profile

Professor

• Business Informatics
• Information Technology
• E-commerce
• +6 more

Prof. Dr. Stefan Kuhlins is a Professor of Business Informatics at Heilbronn University's Faculty of Economics. He specializes in Business Engineering Logistics and previously worked at the universities of Mannheim and Regensburg. His research focuses on E-commerce, XML technologies, and enterprise software integration. He also operates as a freelance business information technology specialist. Current: Professor at Heilbronn University (Faculty of Economics) Previous: University of Mannheim, University of Regensburg Specialization: Business Engineering Logistics (formerly Technical Logistics Management) Freelance: E-commerce and software projects Education : Graduate in Business Information Technology Research Interests : His work spans business informatics, information technology, and programming with particular emphasis on database systems, XML standards, and Robotic Process Automation (RPA). He has developed frameworks for enterprise application integration and explored wireless usability metrics through Java software agents. His publications reflect a strong focus on E-commerce systems, XML schema design, and software development methodologies. Publications Trends : His scholarly output from 1993-2018 covers XML technologies, Java applications in enterprise systems, simulation modeling for logistics, and web service integration. The articles demonstrate consistent engagement with software engineering challenges in E-commerce and enterprise contexts. Technical Leadership : Since 2014, he has served as Head of the Internship Office for Business Engineering Logistics (BEL) and Technical Logistics Management (TLM) programs at Heilbronn University. This administrative role complements his technical expertise in systems design. Programming Expertise : With deep knowledge of C++ standard libraries and Java technologies, he has contributed to areas like garbage collection, CORBA optimization, and multithreaded frameworks. His technical work spans from core programming challenges to enterprise-scale integration solutions.

Christian AlmederView profile

Professor

• Supply Chain Management
• Operations Research
• Production Planning and Scheduling
• +3 more

Prof. Dr. Christian Almeder serves as Professor and Head of the Chair of Supply Chain Management within the Faculty of Business Administration and Economics at Viadrina European University (Frankfurt (Oder), Germany). His research focuses on operations research applications in production planning, logistics, and supply chain optimization, with particular expertise in lot sizing, scheduling, and perishable goods management. He maintains active research output with publications spanning from 1997 to 2023. Almeder's research centers on mathematical modeling of complex production and logistics systems. His primary contributions involve developing heuristic and metaheuristic solutions for capacitated lot sizing problems, multi-level scheduling, and integrated production-distribution planning. Key specialties include handling perishability constraints, lead time uncertainties, and batch processing requirements using genetic programming, simulation-based optimization, and clearing function approaches. His work bridges theoretical operations research with industrial applications in supply chain management. Analysis of his 15 most recent publications (2013-2023) reveals a consistent focus on lot sizing and scheduling, with increasing emphasis on integrated supply chain problems and perishable goods logistics. Methodologically, he combines metaheuristics (genetic programming, simulated annealing) with simulation techniques to address real-world complexities like stochastic processing times and limited buffers. His work demonstrates strong application in production planning parameter tuning, vehicle routing integration, and robust operational planning under uncertainty.

Mirko StoffersView profile

Researcher

• Cyber-Physical Systems
• Discrete Event Simulation
• Parallel Programming
• +4 more

Mirko Stoffers is a Postdoctoral Researcher and head of the Cyber-Physical Systems (CPS) group at COMSYS, RWTH Aachen University. He holds a Master’s (2011) and Doctorate (2019) from RWTH Aachen, with a PhD focusing on automated optimization of discrete event simulations. His research spans Cyber-Physical Systems, smart grids, and parallel programming, contributing to projects like REDeFiNE, QuGrids, and SAFEr Grid. He has led teams in developing resilient communication protocols and energy management systems. Education: PhD in Computer Science (2019), RWTH Aachen Master’s in Computer Science (2011), RWTH Aachen Research Scholar at Georgia Tech (2011-2012) under Prof. George Riley Research Interests: Cyber-Physical Systems integration and resilience Parallel and distributed simulation techniques Smart grid communication and energy systems Data-driven optimization in networked systems Publications highlight innovations in simulation automation (e.g., memoization techniques) and network softwarization. He has received two Best Paper Awards for contributions to parallel simulation and wireless network modeling. Labs/Teams: Leads the CPS group within COMSYS, collaborating on interdisciplinary projects at the intersection of computing and physical systems.

• Operations Research
• Optimization
• Network Flows
• +3 more

Prof. Horst W. Hamacher is a Professor in the Department of Optimization at the University of Kaiserslautern's Faculty of Mathematics. His research focuses on optimization, operations research, and their applications in logistics, healthcare, and disaster management. Key areas include network flow optimization, evacuation planning models, radiation therapy treatment design, and hub location problems. He leads the Optimization Group (AG Optimierung), developing algorithms for complex systems. His work integrates theoretical advancements with practical solutions, such as minimizing beam-on time in radiation therapy and optimizing urban evacuation routes. He has authored/co-authored over 100 journal papers and book chapters, including seminal contributions to facility location theory and dynamic network flows. Collaborations span academic and industrial partners, addressing challenges in public transportation, medical engineering, and sustainable urban planning. Notable projects include the 'OptionS' initiative for sustainable water management optimization and the 'FlowLoc' framework for evacuation dynamics. He actively contributes to interdisciplinary education, bridging mathematics with real-world applications like robotic assembly and infrastructure design.

Arne KoorsView profile

Researcher

• Discrete-Event Simulation
• Algorithm Optimization
• Financial Market Modeling
• +3 more

Arne Koors serves as a scientific Assistant at the Department of Computer Science , University of Hamburg , under the Faculty of Mathematics, Informatics and Natural Sciences (MIN) . His research focuses on discrete-event simulation optimization , financial market modeling , and ERP system integration . He has contributed to 9 Bachelor's and 5 Diplom theses as primary supervisor. Education & Teaching: He teaches courses including Mathematics for Computer Science , Introduction to Computer Science , and Simulation Projects . His teaching spans topics like cryptography, network systems, and simulation seminars. Research Contributions: His work emphasizes performance optimization of simulators, priority queue algorithms, and simulation-ERP system interaction. Notable achievements include a Best Paper Award at ESM 2014 for Analysis by State: An Alternative View on Discrete Event Time Series . Practical Experience: He led project management for 75 manufacturing companies in ERP implementation and developed a globally used sales planning software deployed in 250 firms. His invited talks include presentations on ERP systems in education and sales planning strategies. Key Projects: Development of the DESMO-J simulation framework, integration of financial risk metrics into discrete-event systems, and asynchronous RNG methods for performance enhancement.

• Econometrics
• Empirical Finance
• Asset Pricing
• +9 more

Joachim Grammig is a Professor in the Department of Statistics and Econometrics at the School of Business and Economics, University of Tübingen, Germany. His research lies at the intersection of econometrics and empirical finance, with a focus on asset pricing, financial market microstructure, high-frequency data, and international financial markets. His research interests span Econometrics , Empirical Finance , Asset Pricing , Financial Market Microstructure , High Frequency Data , and International Financial Markets . He develops and applies advanced statistical models to understand price formation, risk, and information flow in financial markets. His recent publications show a strong trend toward integrating structural economic models with modern econometric techniques such as indirect inference, simulation-based estimation, and machine learning. His work frequently addresses long-standing puzzles in asset pricing, including the equity premium, rare disasters, and information asymmetry. He also contributes to methodological advances in duration modeling and specification testing. Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia (2024) Estimating the SARS-CoV-2 Infection Fatality by Data Combination (2022) Empirical Asset Pricing with Multi-Period Disasters (2021) Creative Destruction and Asset Prices (2016) Tell-Tale Tails: A New Approach to Estimating Unique Market Information Shares (2013) He advises PhD students and junior researchers, including Jantje Sönksen and Franziska Julia Peter. His research is supported by access to high-frequency financial data and computational tools, and he emphasizes reproducibility by sharing programs and data. He has collaborated extensively with scholars across Europe and the U.S. He leads a research team in econometrics and empirical finance at Tübingen, fostering a collaborative environment for graduate students and visiting scholars.