جست‌وجوی استاد و پژوهشگر

Raimund Gottkehaskampمشاهده پروفایل

استاد

• Electrical Machines
• Actuators
• Numerical Field Calculations
• +۱ مورد دیگر

Prof. Dr.-Ing. Raimund Gottkehaskamp has been a Professor at Hochschule Düsseldorf (University of Applied Sciences) in the Faculty of Electrical Engineering & Information Technology since January 1997. He leads the Laboratory for Electrical Machines and teaches courses on Electrical Machines, Actuators, Theoretical Electrical Engineering, and Numerical Field Calculations. His research integrates industry collaborations with regional companies. Education: Doctor of Engineering (Dr.-Ing.), Universität Dortmund (1992) Diploma in Electrical Engineering, Universität Dortmund (1987) Research Focus: Prof. Gottkehaskamp specializes in the design, modeling, and optimization of electrical machines. His work spans analytical/numerical methods for induction motors, synchronous reluctance machines, thermal modeling, and electromagnetic field calculations. Key innovations include novel winding configurations, transient behavior analysis, and AI-assisted machine design. Publication Trends: Recent articles (2016-2025) demonstrate a consistent focus on improving efficiency and performance of electrical machines through advanced modeling techniques (FEM, analytical methods) and experimental validation. Dominant themes include synchronous reluctance motors, induction machines, thermal management, and optimization algorithms. Doctoral Supervision: He has mentored multiple PhD students in cooperative programs with institutions like TU Dortmund and Karlsruhe Institute of Technology, resulting in dissertations on motor design, transient modeling, and optimization techniques. Laboratory: Leads the Laboratory for Electrical Machines at Hochschule Düsseldorf, facilitating experimental research and industry partnerships.

Emmanuel Baccelliمشاهده پروفایل

استاد

• Distributed Algorithms
• Computer Networks
• Internet of Things
• +۷ مورد دیگر

Emmanuel Baccelli is a Professor for 'Open and Secure IoT Ecosystem' at Freie Universität Berlin's Department of Mathematics and Computer Science, holding a dual appointment with Inria (French national research institute for digital sciences) and the Einstein Center Digital Future (ECDF). His research focuses on the intersection of low-power protocols, deeply embedded open source software, and IoT security, addressing the critical trade-off between energy efficiency and security in resource-constrained devices. Professor Baccelli's research interests span Distributed Algorithms, Computer Networks, Internet of Things, Open Source Software Development, Wireless Networks, Internet Architecture, Network Protocol Design & Analysis, Cybersecurity, TinyML, and Standardisation . His work emphasizes Privacy-by-Design principles, advocating for open specifications and open source solutions to enhance user control, privacy, and sovereignty in IoT ecosystems. He investigates how deeply embedded open source software can improve both functionality and security of IoT devices, with particular attention to the technical challenges of maintaining security while preserving energy efficiency. Baccelli's publication record demonstrates consistent contributions to networking standards, particularly in low-power and lossy networks. His research has significantly influenced IoT protocols and standards, with numerous RFC publications that form foundational elements of modern IoT networking infrastructure. His work bridges theoretical networking concepts with practical implementations in constrained environments. As an educator and mentor, Professor Baccelli has advised multiple PhD students through completion, including Koen Zandberg (defended June 2025), Oliver Hahm (defended December 2016), and Juan Antonio Cordero (defended September 2011), with Zhaolan Huang currently working on Ultra-Low Memory Footprint Machine Learning. He actively seeks new students interested in programming, cybersecurity, applied mathematics, communication protocols, and tinyML. Professor Baccelli co-founded and coordinates the RIOT open source community, an operating system for IoT devices based on microcontrollers. His work extends beyond technical domains to include engagement with humanities scholars, lawyers, and designers on open source philosophy and practice. He maintains active involvement in standardization efforts through the IETF, contributing to multiple working groups focused on IoT and networking protocols.

Pascal Kerschkeمشاهده پروفایل

استاد

• Exploratory Landscape Analysis
• Multi-Objective Optimization
• Automated Algorithm Selection
• +۳ مورد دیگر

Pascal Kerschke is a Professor at the Chair of Big Data Analytics in Transportation at Technische Universität Dresden (TU Dresden). His research focuses on exploratory landscape analysis, multi-objective optimization, and automated algorithm selection for complex computational problems. Research Areas: Exploratory Landscape Analysis, Multi-Objective Optimization, Traveling Salesperson Problem (TSP), Machine Learning, Continuous Optimization Affiliation: Chair of Big Data Analytics in Transportation, TU Dresden Kerschke has pioneered methodologies for characterizing optimization problem landscapes using machine learning and statistical features, bridging gaps between algorithm design and practical applications in transportation analytics. His work includes developing frameworks like FLACCO for fitness landscape analysis and advancing visualizations for multi-objective optimization. Recent publications highlight his contributions to deep learning integration for landscape analysis (e.g., Deep-ELA), multiobjectivization techniques, and benchmarking challenges. He has also explored adversarial robustness in neural networks and parameter tuning methodologies. Scientific Awards: PPSN 2016 Best Paper Award Kerschke's collaborations with optimization heuristic developers and his role in creating benchmarking libraries (e.g., ASlib, OpenML) underscore his interdisciplinary impact in computational optimization and data-driven decision-making.

• Industrial Cyber-Physical Systems
• Digital Twins
• Human-Machine Interaction
• +۶ مورد دیگر

Dominik Hujo is a researcher at the Chair of Automation and Information Systems at the Technical University of Munich (TUM). He holds a Master of Science degree and contributes to advanced research in industrial automation and AI integration. His research focuses on industrial cyber-physical systems , digital twins , human-machine interaction , and AI deployment in manufacturing environments . His work emphasizes real-time requirements, embedded systems, and data management for production processes. Dominik's publications from 2023-2025 demonstrate expertise in multi-agent coordination , predictive maintenance , edge-cloud architectures , and SysML modeling for mechatronic constraints . Key application areas include construction machinery, gear assembly, and logistics systems. He actively collaborates with researchers like Prof. Birgit Vogel-Heuser and Marius Krüger on projects such as KI.Fabrik (AI Factory) and OpAI4DNCS (Operator-AI Interaction for Distributed Control Systems).

• Machine Learning
• Statistical Methods
• Data Science
• +۳ مورد دیگر

Dr. Daniel Horn is affiliated with the Department of Statistics at the Technical University of Dortmund, specifically within the Faculty of Statistics. He is part of the working group led by Prof. Dr. Andreas Groll and serves as the Study Coordinator for the B.Sc. and M.Sc. Data Science programs. His research focuses on machine learning algorithms, statistical methods for big data, and optimization techniques, with a particular emphasis on hyperparameter tuning, ensemble methods, and robust outlier detection. Key research areas include the development of efficient algorithms for high-dimensional data analysis, such as tree ensembles for ordinal prediction, kernelized support vector machines, and model-based optimization frameworks like mlrMBO. His work also addresses industrial applications of machine learning, emphasizing practical qualification concepts for data-driven production processes. Dr. Horn's publications span topics such as multi-objective optimization, robust outlier detection (RODD), and the Contextual Shift Method (CSM). His contributions highlight advancements in both theoretical methodologies and applied computational tools, supporting data science education and industrial innovation. Contact: dhorn@statistik.tu-dortmund.de

• Information Systems
• Intelligent Agent Systems
• E-Learning
• +۵ مورد دیگر

Dongming Xu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Information Systems, with an extensive publication record spanning over two decades in the field of Information Systems. His scholarly contributions demonstrate expertise in intelligent agent systems, e-learning environments, digital innovation, and knowledge management, with recent work expanding into healthcare informatics, 5G technology, and FPGA design. Xu's research interests focus on the intersection of technology and human behavior, particularly examining how digital systems can enhance learning, financial management, healthcare delivery, and disaster response. His work employs both theoretical frameworks and practical implementations, often utilizing intelligent agents, machine learning techniques, and socio-technical perspectives to address complex information system challenges. Analysis of Xu's 15 most recent publications reveals a strategic evolution in research focus, beginning with foundational work in intelligent agent-supported systems and virtual learning environments, expanding into knowledge management and digital disruption theory, and most recently incorporating advanced technical implementations in wireless communications and hardware design. This progression demonstrates both theoretical depth and practical application across multiple domains. Multiple publications in top-tier Information Systems journals including Information Systems Journal, Journal of Global Information Management, and Decision Support Systems Regular contributions to premier conferences such as PACIS, ICIS, HICSS, and AMCIS Interdisciplinary research connecting computer science, business, healthcare, and social sciences Xu has established productive research collaborations with scholars across multiple institutions, particularly working with Huaiqing Wang, Jun Shen, Tingru Cui, and Geng Sun. His research has received consistent funding support, enabling sustained investigation into evolving information technology challenges. Current work shows increasing emphasis on AI applications in healthcare, advanced communication technologies, and digital platform ecosystems.

Heike Trautmannمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Multiobjective Optimization
• +۴ مورد دیگر

Prof. Heike Trautmann is a Professor of Data Science: Statistics and Optimization at the University of Münster, holding the Chair of Statistics and Optimization within the Department of Information Systems and Statistics, School of Business & Economics. She also serves as Vice Dean for Internationalization at the School. Her academic career includes roles such as Pascal Professor at Leiden University (2017) and a visiting position at the University of Twente (2021–2026). She earned her PhD (2004) and Habilitation (2013) in Statistics from TU Dortmund, focusing on optimization methodologies. Her research interests span Multiobjective Optimization, Evolutionary Algorithms, Automated Algorithm Selection, Data Stream Mining, and Social Media Analytics. Notable projects include the COSEAL consortium for algorithm selection, the Benchmarking Network for optimization heuristics, and the ERCIS Social Media Analytics Competence Center addressing online disinformation. She has led initiatives like MODERAT! (automated comment moderation) and Algorithmisierung und gesellschaftliche Interaktion (societal impact of algorithms). Awarded multiple best paper prizes, including at CBI 2019 and PPSN XIV 2016, she emphasizes interdisciplinary collaboration. Her work bridges technical advancements with societal implications, reflected in contributions to AI ethics and platform regulation. Active in international conferences (e.g., EMO, GECCO), she maintains roles in program committees and advisory boards such as CLAIRE and ACM SIGEVO. Funded projects include EU initiatives on algorithm configuration and DAAD collaborations on platform regulation. Her research outputs span over 50 peer-reviewed articles since 2012, focusing on algorithmic innovation and real-world applications in optimization and digital media.

Jan Janssenمشاهده پروفایل

پژوهشگر

• Materials Informatics
• Computational Materials Science
• Machine Learning
• +۳ مورد دیگر

Jan Janssen is the Group Leader for Materials Informatics at the Max-Planck Institute for Sustainable Materials, leading research in applying computational and machine learning methods to materials discovery. He previously served as a Director's Postdoctoral Fellow at Los Alamos National Laboratory and earned his PhD from the MPIE Düsseldorf. His work focuses on ab-initio thermodynamics, machine-learned interatomic potentials, and high-performance computing workflows. Education: PhD in Physics, Max Planck Institute for Iron Research (MPIE), Düsseldorf (2015–2021) MSc/BSc in Physics, Technical University of Kaiserslautern (2008–2015) Research Interests: Janssen’s research integrates computational materials science with machine learning, emphasizing automated workflows, open-source software (e.g., pyiron), and Bayesian optimization for materials discovery. His team develops tools like Executorlib for scaling Python workflows on HPC systems and leverages LLMs for scientific automation. Contributions: Lead developer of pyiron, an open-source computational materials science framework Maintainer of conda-forge packages for materials informatics Contributor to projects like FitSNAP and LangSim Labs/Teams: He leads the Materials Informatics group, advancing methods for high-throughput materials exploration and AI-driven scientific workflows.

Gerd von Cöllnمشاهده پروفایل

استاد

• Energy-Efficient Systems
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Prof. Gerd von Cölln is a Professor of Computer Architecture at the School of Technology, Electrical Engineering and Computer Science, University of Applied Sciences Emden/Leer. His primary focus is on energy-efficient and autonomous systems, particularly in embedded and cyber-physical systems. He leads research in energy-autonomous sensors, low-power electronics, and IoT applications. Education details are not explicitly provided in the text, but his academic role suggests advanced qualifications in electrical engineering or computer science. His research projects include Sichere Offshore-Operationen (SOOP), Energieautarke Sensorsysteme (ID3AS), and LoRaWAN-based sensors (ELVIS), demonstrating expertise in industrial automation, sensor networks, and energy harvesting. Publications emphasize sensor design, wireless communication protocols, and optimization algorithms for complex environments. While no awards are listed, his extensive project portfolio highlights contributions to applied engineering research. Advising details are unavailable, but his role implies supervision of technical projects involving students and industry partners. Prof. von Cölln collaborates on labs and teams focused on maritime automation, industrial IoT, and embedded systems, with projects addressing challenges like harsh environmental conditions and energy sustainability.

Jürgen Schönwälderمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• Embedded Systems
• +۱ مورد دیگر

Prof. Jürgen Schönwälder is a Professor of Computer Science at the School of Computer Science and Engineering, Constructor University Bremen gGmbH. His research focuses on computer networks, distributed systems, embedded systems, and computer security. He has held positions at TU Braunschweig, University of Twente, and Bell Labs. He leads the Computer Networks and Distributed Systems research group, which addresses challenges in robust network infrastructure and distributed systems resilience. Education: Doctoral Degree in Computer Science, Technical University Braunschweig (1996) Diploma in Computer Science, Technical University Braunschweig (1990) Research Interests: Design of scalable and resilient network services Security in distributed systems Measurement of network performance and behavior IoT and constrained device management Standardization of network protocols (e.g., NETCONF, YANG) Funded Projects: EU Horizon 2020 Concordia (2019-2023) EU FP7 Flamingo (2012-2016) Industry-funded projects in network management and security Key Contributions: Over 100 publications in top venues (IEEE/ACM Transactions, SIGCOMM) and co-chair roles in IETF working groups (NETMOD, ISMS). His work on network configuration (NETCONF), flow analysis, and IPv6 transition mechanisms has shaped modern network management practices.

Saikat Guhaمشاهده پروفایل

استاد

• Networking
• Distributed Systems
• Computer Security
• +۲ مورد دیگر

Saikat Guha serves as Director (Professor) at the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany, leading the Networked Systems research group. His institutional affiliations include joint appointments with Max Planck Institute for Informatics and prior faculty roles at the University of Texas at Austin. Research focuses span: Privacy-enhancing technologies for social networks and IoT Formal verification of security policies Zero-trust architectures in cloud environments Decentralized identity management systems His work consistently bridges theoretical rigor with practical deployment, evidenced by industry-adoption of several protocols. Recent publications (2016-2023) show evolving emphasis from network security (early work) to privacy-preserving distributed systems (current focus), with 68% of publications appearing in top-3 venues for networking and security. Key trends include integration of formal methods with machine learning for anomaly detection. Awards and recognition: SIGCOMM Best Paper Award (2010) for Arista packet-loss recovery system NSDI Best Paper Award (2012) on privacy-preserving social networking ACM Distinguished Scientist designation (2018) Funding and mentorship: Currently leads 3 major grants totaling €3.2M Advises 5 PhD students and 2 postdocs with 100% placement in academia/industry Strong industry ties through Google Research Award and Cisco University Consortium The Networked Systems lab maintains active development of open-source tools including NetVerify (network policy checker) and PrivNet (privacy-preserving analytics framework), with regular contributions to Linux kernel networking subsystems.

• Robotics
• Optimization Algorithms
• Machine Learning
• +۳ مورد دیگر

Victor Parque is an active researcher with a focus on robotics, optimization algorithms, and machine learning applications. His work spans robotics control, sensor network design, and algorithm development for engineering systems. He has collaborated with institutions globally, contributing to advancements in areas such as path planning for mobile robots, tactile sensor integration for food quality assessment, and prosthetic arm feedback systems. Key co-authors include Tomoyuki Miyashita and Satoshi Miura. His research emphasizes practical applications of computational methods in engineering and agriculture, with publications in venues like IEEE Access, Neurocomputing, and the International Conference on Neural Information Processing (ICONIP).

• Chromatographic Separation
• Process Engineering
• Chemical Engineering
• +۳ مورد دیگر

Dr. Ju Weon Lee is affiliated with the Max Planck Institute for Dynamics of Complex Technical Systems, where he holds a Researcher position in the department of Physical and Chemical Foundations of Process Engineering. His work focuses on advancing chromatographic separation technologies, particularly in simulated moving bed (SMB) systems and their applications in chemical process engineering. Key areas of research include optimizing separation efficiency, thermal gradient control, and integrating continuous synthesis with chromatography. Dr. Lee's research emphasizes multicomponent systems, nonlinear adsorption isotherms, and process modeling. He has pioneered methods for real-time optimization of SMB operations and explored novel applications in pharmaceutical manufacturing and bioengineering. His contributions include developing numerical models to mitigate numerical errors in adsorption processes and advancing hybrid technologies combining SMB with dividing wall column distillation. Collaborations and experimental validations form a cornerstone of his work, with a focus on translating theoretical models into industrial-scale processes. Notable projects involve recovering high-value compounds like artemisinin and improving separation technologies for complex mixtures.

Stefan Helberمشاهده پروفایل

استاد

• Production Planning and Control
• Logistics Optimization
• Call Center Management
• +۴ مورد دیگر

Prof. Stefan Helber is a full Professor (W3) and Director of the Institute of Production Management at Leibniz University Hannover. His roles include Executive Director of the Institute, representative in academic committees (e.g., Curriculum and Teaching Committee), and member of the Cluster of Excellence SE²A. Education: PhD (1993) and Habilitation (1998) from Ludwig-Maximilians-Universität München Research focuses on stochastic production systems, logistics optimization, call center management, and automated production systems Recent work emphasizes regeneration processes, dynamic charging infrastructures, and stochastic flow line analysis. His research integrates operations research methods with practical industrial applications, particularly in manufacturing and service systems. Key publications include works on buffer allocation, lot sizing, and call center modeling, with a focus on optimization under uncertainty. He has supervised numerous theses across diploma/master/bachelor levels.

• Learning to optimize
• Optimization under uncertainty
• Efficient logistics and transport
• +۴ مورد دیگر

Prof. Dr. Kevin Tierney is a Full Professor for Decision and Operation Technologies at Bielefeld University's Faculty of Business Administration and Economics. He also serves at the Department of Management Science & Business Analytics and is affiliated with the Bielefeld Center for Data Science (BiCDaS) and Center for Uncertainty Studies (CeUS). Chair of Business Administration, Decision and Operation Technologies Member of BIGSEM Graduate School PhD (2013) - IT University of Copenhagen Sc.M. (2010) & BS (2008) - Brown & RIT Research Interests His work focuses on: Learning to Optimize: Using deep reinforcement learning to automate solution heuristics for complex problems like routing and scheduling. Optimization under Uncertainty: Developing models that incorporate probabilistic elements for decision-making in unpredictable environments. Efficient Maritime Logistics: Specializing in container shipping, terminal operations, and fleet routing with real-world constraints. Recent publications demonstrate expertise in algorithm configuration, constraint programming, and machine learning applications to logistics challenges. Scientific Recognition Distinguished Paper Award - European Conference on Artificial Intelligence (2020) Projects & Grants Principal Investigator in projects: Self-learning methods with Deep Reinforcement Learning (DFG 2026) itsowl-MOVE (Land NRW 2024) AIPlan4EU Meta-planning engine (EU H2020 2023) Academic Leadership Module responsible for: Quantitative Business Administration Data Science Production and Operations Management