Find Academic Supervisors

Kolja KühnlenzView profile

Professor

• Computer Vision
• Robotics
• Human-Robot Interaction
• +3 more

Kolja Kühnlenz is a Professor of Electrical Engineering and Computer Science at Coburg University of Applied Sciences and Arts since 2014. Previously, he held roles as Senior Research Scientist, Senior Lecturer, and Director of the Dynamic Vision Research Laboratory at the Technical University of Munich (TUM) until 2012. He leads research in cognitive technology and has been a Carl von Linde Junior Fellow. His academic journey includes a Doctor of Engineering from TUM (2007) and a Bachelor's degree in Electrical Engineering from TU Berlin (2002). Research interests focus on computer vision , robotics , human-robot interaction (HRI) , and intelligent automation systems . Notable contributions include work on anthropomorphism perception, adaptive robot controllers, and proactive HRI systems. He has led projects within the Munich Cluster of Excellence CoTeSys and the Bernstein Center for Computational Neuroscience Munich. His publications emphasize motor interference effects , cognitive load impacts , and trust in autonomous systems . Key awards include the CoTeSys Junior Research Group Leadership Award and a Student Research Award from Humboldt Universität Berlin. His work spans applications in Emotion-aware robotics, Biologically inspired vision systems, Autonomous vehicle communication, Human-robot collaboration ethics. Current research trends highlight advancements in bio-inspired motion control and explainable AI for HRI .

Harshit Jitendra MotwaniView profile

Research Fellow

• Computational Algebraic Geometry
• Formal Verification
• Tensors
• +5 more

Harshit Jitendra Motwani is a Postdoctoral Researcher at the Max Planck Institute for Software Systems (MPI-SWS) , Germany. Previously, he was a Postdoctoral Fellow at the Hong Kong University of Science and Technology (HKUST) (2023-2024), following doctoral research at Ghent University (2021-2023) and earlier engagements at HKUST, Ghent University, and the University of Bristol . Education : PhD in Mathematics: Algebra and Geometry from Ghent University (2021-2023), Thesis on Algebro-Geometric Algorithms for Program Synthesis; Integrated BSc and MSc in Mathematics and Computing from IIT Kharagpur (2015-2020). Research Focus : Integrates mathematics into computational domains, particularly Computational Algebraic Geometry , Formal Verification , and Tensors . His work spans program synthesis , tensor networks , conditional independence models , and control theory applications . Publications : Recent contributions include algorithmic advancements in formal methods (FM, AAAI, LAGOS), algebraic statistics (IMRN), and quantum computing (SIGMA). His research combines symbolic computation with practical software engineering challenges. Awards & Honors : IEEE Computer Society Larson Best Paper Award (2023) ACM SIGPLAN Distinguished Paper Award (2023) Young Researcher of the Heidelberg Laureate Forum (2023) INSPIRE Scholarship (2015-2020) Teaching : Guest Lecturer at HKUST, Teaching Assistant at RPTU Kaiserslautern-Landau and IIT Kharagpur. Mentored multiple bachelor's projects at Ghent University and IIT Kharagpur.

Heidemarie KrügerView profile

Professor

• Photonics
• Quantum Detection
• Memristor Technology
• +5 more

Prof. Dr. Heidemarie Krüger serves as the Head of the Research Department Photonics and Quantum Detection at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany. Her work bridges advanced materials science with applied physics and electronics, focusing on photonic and quantum detection systems. Academic Rank: Professor Email: heidemarie.krueger@leibniz-ipht.de Research Interests span photonics, quantum detection, memristor technology, and nanomaterials. She investigates: Electrochemically grown porous platinum for electrocatalysis and optical applications Resistive switching dynamics in multiferroic thin films Magnetooptical properties of ferromagnetic materials Stability enhancements in organic solar cells via amphiphilic additives Memristor-based security solutions like true random number generators Impedance chip development for biochemical monitoring Recent Publications (2024–2025) highlight her interdisciplinary expertise, with trends in: Magnetotransport phenomena in superconductors Optimization of memristive devices for neuromorphic and cryptographic applications Electrochemical and optical engineering of nanostructured materials Stability mechanisms in renewable energy technologies

Kurt SandkuhlView profile

Professor

• Enterprise Architecture
• Enterprise Modeling
• Capability Management
• +5 more

Prof. Dr.-Ing. Kurt Sandkuhl serves as Professor and Chair of Business Information Systems at the Institute of Computer Science within the Faculty of Computer Science and Electrical Engineering at the University of Rostock. He concurrently holds the position of Dean of the Faculty and maintains active roles as Academic Advisor for the Business Information Systems Master's program, ERASMUS+ Coordinator, and member of the academic management of the Center for Entrepreneurship. His research centers on enterprise architecture, enterprise modeling, capability management, and digital business models, with significant contributions to knowledge-based systems, smart process management, and mobile/wearable information systems. Recent work demonstrates pioneering integration of artificial intelligence—particularly large language models—into enterprise modeling practices, addressing challenges in cybersecurity, sustainable business transformation, and public sector digitalization. Analysis of his recent publications reveals dominant trends in AI-augmented enterprise modeling, cybersecurity architecture for public administration, and circular economy transitions in manufacturing. His work consistently bridges theoretical modeling frameworks with practical implementations in SMEs, public transport, and energy management contexts, emphasizing usability and real-world impact. Prof. Sandkuhl advises the WIN M.Sc. program and coordinates international academic exchanges through ERASMUS+. His institutional leadership extends to the IT Initiative Mecklenburg-Vorpommern board and co-opted membership in the Interdisciplinary Faculty's Department of Ageing, while maintaining active affiliations with the German Computer Science Society and founding membership in the European Association for Software and Systems Technology. He directs the Business Informatics Enterprise Modeling Lab, which employs multi-touch tables and smart boards for participatory modeling research. The lab focuses on capability-driven enterprise architecture, digital business ecosystem design, and context-aware systems for quantified products, with notable projects in maritime dataspaces and demand-responsive public transport integration.

Raimund GottkehaskampView profile

Professor

• Electrical Machines
• Actuators
• Numerical Field Calculations
• +1 more

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.

• Industrial Cyber-Physical Systems
• Digital Twins
• Human-Machine Interaction
• +6 more

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
• +3 more

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
• +5 more

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 TrautmannView profile

Professor

• Data Science
• Artificial Intelligence
• Multiobjective Optimization
• +4 more

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 JanssenView profile

Researcher

• Materials Informatics
• Computational Materials Science
• Machine Learning
• +3 more

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 CöllnView profile

Professor

• Energy-Efficient Systems
• Embedded Systems
• Cyber-Physical Systems
• +5 more

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 SchönwälderView profile

Professor

• Computer Networks
• Distributed Systems
• Embedded Systems
• +1 more

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 GuhaView profile

Professor

• Networking
• Distributed Systems
• Computer Security
• +2 more

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
• +3 more

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
• +3 more

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.