Find Academic Supervisors

Rita Torres de SousaView profile

Researcher

• Knowledge graphs
• Machine learning
• Biomedical applications
• +3 more

Rita Torres de Sousa is a Researcher at the Data and Web Science Group, part of the School of Business Informatics and Mathematics at the University of Mannheim. Her research focuses on knowledge graphs, machine learning, and biomedical applications. She leads the KI-DiabetesDetektion project, which integrates multi-source data into knowledge graphs to enhance early diabetes detection through machine learning. Her work bridges computational methods with biomedical challenges, emphasizing explainable AI and semantic similarity in biological contexts. Rita’s contributions include developing benchmark datasets and novel approaches for biomedical knowledge representation. Publications highlight her interdisciplinary research, spanning protein interaction analysis, negative statement embeddings, and supervised learning in complex biomedical systems. She collaborates internationally, evidenced by co-authored papers at venues like the International Semantic Web Conference and Principles of Knowledge Representation and Reasoning (KR).

David B. BlumenthalView profile

Professor

• Biomedical Engineering
• Network Science
• Artificial Intelligence in Medicine
• +6 more

David B. Blumenthal is an Assistant Professor and head of the Biomedical Network Science Lab at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His work focuses on applying network science and AI to study molecular disease mechanisms and developing privacy-aware biomedical AI solutions. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano and has held postdoctoral positions at TU Munich and the Free University of Bozen-Bolzano. His research spans federated learning, graph algorithms, and systems medicine, with a strong emphasis on translational applications in healthcare. Education: BSc/MSc Mathematics, Free University of Berlin & Technical University of Berlin BA/MA Philosophy, Free University of Berlin PhD in Computer Science, Free University of Bozen-Bolzano Visiting PhD student at GREYC Research Lab, France Research Interests: Blumenthal’s work integrates computational methods with biomedical data to uncover disease mechanisms. Key areas include federated learning for medical data privacy, network-based disease module discovery, and AI-driven drug repurposing. He develops tools like ROBUST-Web, NeDRex, and DataSAIL to address challenges in data leakage and heterogeneous data integration. Awards: Best Poster Award (2022), Best Paper Awards (2021, 2017), Best PhD Student Award (2021) Recipient of German Academic Scholarship Foundation support Grants: Leads projects funded by BMBF, DFG, and industry partners, including FLabNet (federated learning in paediatric oncology) and NetMap (dimensionality reduction in single-cell data). His work often bridges FAU’s engineering and medical faculties. Labs/Teams: Heads the Biomedical Network Science Lab, collaborating with institutions like TU Munich, Bitspark GmbH, and Erlangen University Hospital on translational AI projects.

Anders BlomdellView profile

Professor

• Control Systems
• Real-Time Systems
• Embedded Systems
• +7 more

Anders Blomdell is a Professor in the Department of Control Engineering at the Faculty of Engineering, Lund University. His work bridges theoretical control systems with practical applications in real-time, embedded, and cloud-based environments. He is actively involved in research projects focusing on autonomous systems, industrial automation, and networked control. His research interests include Control Systems , Real-Time and Embedded Systems , Networked Control , Industrial Automation , Robotics , and Cloud-Controlled Systems . These areas reflect his focus on scalable, adaptive, and secure control solutions for complex, interconnected systems. The recent publications highlight a strong trend toward event-based control , large-scale optimization , autonomous adaptation , and secure cloud integration . His work often combines control theory with computer science, particularly in distributed and real-time settings, showing a consistent effort to address challenges in latency, scalability, and robustness. Scientific Awards: Advising and Grants: While specific students are not listed, Anders Blomdell leads and participates in major research initiatives such as WASP, ELLIIT, and the Nordic University Hub on Industrial Internet of Things (HI2OT). These projects involve significant funding and collaboration with industry and academia, indicating active mentorship and leadership in training next-generation researchers. Labs and Teams: He is associated with RobotLab LTH and contributes to the development of tools like TrueTime and LabComm , which are widely used in real-time and networked control research. His work supports both academic and industrial innovation in automation and intelligent systems.

Florian ReitzView profile

Researcher

• Computer Science
• Database Systems
• Information Retrieval
• +5 more

Florian Reitz is a Scientific Staff member at Schloss Dagstuhl - Leibniz Center for Informatics, specializing in the dblp computer science bibliography. His current role involves enhancing bibliographic data infrastructure and managing projects like SmartER. He holds a PhD from the University of Trier (2018), focusing on historical metadata analysis. Previously, he was a research assistant in the Databases and Information Systems group under Prof. Bernd Walter. His work centers on metadata quality, author disambiguation, and leveraging historical corrections for test collections. Research interests include information retrieval, database systems, and digital libraries. He contributes to projects like Scalable Author Disambiguation and has co-authored numerous publications on topics such as LLM applications, knowledge graphs, and FAIR data principles. Contact: florian.reitz@dagstuhl.de.

Nil AydayView profile

Researcher

• Statistical Learning Theory
• Machine Learning for Graphs
• Semi-Supervised Learning
• +3 more

Nil Ayday is a Researcher affiliated with the Department of Informatics - I7 at the Technical University of Munich (TUM), located in Garching b. München. They are part of the Theoretical Foundations of Artificial Intelligence group led by Prof. Debarghya Ghoshdastidar. Their research focuses on advanced machine learning theories including Statistical Learning Theory, Machine Learning for Graphs, and Semi-Supervised Learning. Nil’s work contributes to foundational aspects of artificial intelligence, particularly in developing robust methodologies for graph-based learning and semi-supervised frameworks. Their current position involves advancing theoretical insights within the Informatics 7 division at TUM.

• Artificial Intelligence
• Robotics
• Cognitive Robotics
• +4 more

Michael Beetz is a Professor of Computer Science at the University of Bremen's Faculty for Mathematics & Informatics and head of the Institute for Artificial Intelligence (IAI). He leads research in AI-driven robotics, focusing on human-scale manipulation tasks and open-source software frameworks like openEASE. His work emphasizes robot autonomy, knowledge representation, and cognitive perception. Education: Diploma in Computer Science (University of Kaiserslautern) MSc, MPhil, PhD (Yale University, 1993–1996) Venia Legendi (University of Bonn, 2000) Research Interests: Plan-based robotics, knowledge processing, integrated learning, and cognitive perception. His group develops AI methods for robot control, emphasizing open-source contributions to ROS. Recent work includes semantic digital twins, actionable knowledge graphs, and cloud-integrated robotic systems. Publications: Over 200 papers from 2024–2011, focusing on robotic perception, manipulation planning, and human-robot interaction. Recent trends include cloud robotics, cognitive emulation for perception, and knowledge-driven robot programs. Awards: 2019 Honorary Degree from University of Örebro Best Paper Awards (IROS 2021, ICRA 2013) Grants & Teams: Coordinates the German collaborative research center EASE (Everyday Activity Science & Engineering). Active in EU networks like PLANET. Collaborates internationally on projects like SHERPA (human-robot rescue teams). Lab & Projects: IAI develops CRAM cognitive architecture, RoboEarth semantic web, and MARLIN retail automation systems. Focuses on real-world applications in manufacturing, logistics, and human environments.

Vincenzo MessinaView profile

Researcher

• Decentralized Satellite Networks
• Distributed Satellite Systems
• Satellite Constellations
• +7 more

Vincenzo Messina is a Research Associate and Doctoral Candidate at the Chair of Spacecraft Systems at the Technical University of Munich. He holds a Bachelor's in Aerospace Engineering from the Polytechnic University of Turin and a Master's in Space Engineering from the Polytechnic University of Milan, with research experience at the University of Southampton focusing on on-board propulsion systems. His doctoral research focuses on developing decentralized algorithms for Distributed Satellite Systems (DSS), enabling autonomous decision-making and consensus among satellites. Key research areas include task allocation optimization, federated satellite systems, orbital maneuvering, and digital twin implementations for CubeSats. He has also contributed to studies on electrospray thrusters and lunar navigation constellations. Since 2022, his publications emphasize latency optimization in satellite networks, collision avoidance algorithms, and event-based camera architectures for space situational awareness. His work bridges theoretical frameworks with practical mission design, often collaborating with institutions like the International Astronautical Congress and AIAA. Messina has taught multiple courses including Spacecraft Design, Space Mission Design, and Systems Engineering fundamentals at TUM. His teaching roles include instructor, grader, and teaching assistant across various semesters.

• AI for Networking
• Edge Computing
• Federated Learning
• +4 more

Prof. Xiaoming Fu is a Professor at the Institute of Computer Science, University of Göttingen, and Head of the Computer Networks Group. He also holds a secondary membership in the Center for Statistics. His research focuses on AI-driven networking, edge computing, federated learning, and video analytics. He teaches courses such as 'AI-Empowered Networking and Mobile Communications' and oversees multiple seminars and internships on topics like smart cities and network optimization. His work spans technical innovations in multi-agent systems, low-latency services, and resource-efficient machine learning. Recent articles highlight contributions to neural-optimized video streaming, privacy-preserving MARL, and cross-modal social media analysis. He collaborates on interdisciplinary projects involving mobility data, socioeconomic analysis, and quantum networks. Prof. Fu’s research integrates reinforcement learning, network architecture design, and distributed systems to address challenges in 5G/6G, IoT, and smart cities. His lab develops solutions for edge-cloud orchestration, video quality adaptation, and resilient resource allocation, with applications in both academia and industry.

Philipp KanskeView profile

Professor

• Clinical Psychology
• Behavioral Neuroscience
• Social Cognition
• +5 more

Prof. Philipp Kanske is Chair of Clinical Psychology and Behavioral Neuroscience at TU Dresden, leading the Institute of Clinical Psychology and Psychotherapy since 2020. He holds a PhD (summa cum laude) from Leipzig University and completed postdoctoral research at the Central Institute of Mental Health and Max Planck Institute for Human Cognitive and Brain Sciences. His work focuses on social cognition, emotion regulation, and neuroimaging in psychiatric disorders like bipolar disorder. He has held prestigious roles including Young Academy member at the Leopoldina and BMBF Health Research Forum. Education: M.Sc. Psychology (University of Oregon, 2004), Diploma in Psychology (TU Dresden, 2005), Dr. rer. nat. (Leipzig, 2008), and psychotherapist license (2011). Research emphasizes neural mechanisms of empathy, theory of mind, and stress responses using fMRI/EEG. Editor roles include Cognition and Emotion and Scientific Reports . Key awards include the Otto Hahn Medal (2008) and Heinz Maier-Leibnitz Award (2017). His 78+ peer-reviewed papers explore social brain networks, with a SCOPUS h-index of 25. Collaborations span clinical, neuroimaging, and interdisciplinary projects addressing mental health vulnerability and neuroplasticity in interventions.

• Control Theory
• Nonlinear Systems
• Complex Networks
• +7 more

Jinsheng Sun is a prolific researcher with active contributions to Control Theory , Complex Networks , and Image Processing , often collaborating with scholars from institutions like the University of Melbourne and Harbin Institute of Technology . His work spans theoretical advancements and applied methodologies. Key Affiliations : Co-authored papers with researchers from diverse domains, indicating interdisciplinary collaborations. Research Themes : Focus on control systems for nonlinear dynamics, synchronization in complex networks, and forensic analysis of digital media. Research Interests revolve around: Designing adaptive control mechanisms for nonlinear systems and vibration systems , as seen in his 2025 work on transient performance design. Advancing complex network analysis through novel algorithms for identifying vital spreaders and secure synchronization protocols. Developing digital forensics techniques for JPEG compression and watermarking, with applications in information security . Recent Article Trends highlight his focus on control theory (e.g., neural network-based tracking control, H∞ fault detection) and network science (e.g., pinning synchronization, spreader identification). His 2024 work also includes robotics (object-driven navigation) and point cloud segmentation via interactive frameworks. Advising and Grants are not explicitly mentioned, but his extensive co-authorship network suggests mentorship roles. Funding details remain absent in the provided data. Labs and Teams : Collaborated with teams working on TCP/AQM systems , image encryption , and metabolic network reconstruction (e.g., 2018 work on Eriocheir sinensis).

• Machine Learning
• Natural Language Processing
• Climate Modeling
• +6 more

Professor Andreas Hotho leads the Data Science Chair at the University of Würzburg's Faculty of Mathematics and Computer Science, while serving as founding spokesman of the Center for Artificial Intelligence and Data Science (CAIDAS). His academic journey includes senior researcher roles at the University of Kassel and research positions at the AIFB Institute (University of Karlsruhe) and L3S Research Center (Hannover). Research Focus: Machine Learning, Large Language Models, Climate Modeling, Semantic Web, and Knowledge Graphs Methodologies: ConvMOS architecture, DenseLoss approach, LanZ-DML, and BibSonomy system Applications: Environmental monitoring, Digital Humanities, Smart Beehive Analysis, and Social Media Analytics With over 15 recent publications, his work spans climate model output statistics, zero-shot metric learning, imbalanced regression techniques, and German language model development. He serves as editor-in-chief for the Transactions on Graph Data and Knowledge and maintains active roles in academic governance through PC memberships at ECML-PKDD, WWW, and other major conferences. Scientific awards include: NAACL 2024 Best Paper Honorable Mention ICDM 2023 Best Paper Award NeurIPS 2020 Best ML Innovation Award ISWC 2018 SWSA Ten-Year Award WWW 2015 Best Paper Award FAIML 2020 Best Student Paper His research group includes doctoral researchers like Jan Pfister, Julia Wunderle, and Albin Zehe. Key projects include LitBERT for literary analysis, BigData@Geo series for climate modeling, and BeeConnected for ecosystem monitoring.

Daniel SchlörView profile

Researcher

• Machine Learning
• Anomaly Detection
• Natural Language Processing
• +5 more

Dr. Daniel Schlör is a researcher at the Chair of Data Science (Informatics X) at the University of Würzburg, with additional affiliations to the CLiGS (Computational Literary Genre Stylistics) research group in Digital Humanities. His work focuses on machine learning for cybersecurity, fraud detection, and explainable AI, with recent projects exploring synthetic data generation, knowledge graph integration, and deep learning for imbalanced datasets. Research interests include Explainable AI (XAI) for anomaly detection Deep learning architectures for domain-specific relationships Multi-agent simulations for fraud scenario modeling Computational stylistics in digital humanities Article trends show expertise in Developing novel neural units (e.g., ModeConv) for structural anomaly differentiation Advancing XAI methods with generative inpainting techniques Creating open ERP datasets for occupational fraud research Applying graph neural networks to water distribution leakage detection Labs & collaborations include the Data Science Chair’s AI Institute at Hubland Nord campus and CLiGS research group for computational literary analysis.

Michael PabstView profile

Researcher

• Robotics
• Artificial Intelligence
• Mixed Reality
• +13 more

Michael Pabst is a Research Associate at the Visualization Institute of the University of Stuttgart (VISUS) , affiliated with the Schmalstieg Working Group . He holds an M.Sc. in Robotics, Cognition, Intelligence from the Technical University of Munich (2019-2024) and a B.Sc. in Vehicle Engineering from the University of Stuttgart (2015-2019) . Education: M.Sc. Robotics, Cognition, Intelligence (2021-2024), Technical University of Munich B.Sc. Vehicle Engineering (2015-2019), University of Stuttgart Research Interests Michael focuses on Human-Robot Interaction using AR/VR technologies, 3D Scene Understanding , and 3D Scene Graphs . His work spans robotics perception, control systems, and spatial computing applications. He has also contributed to SLAM , environment perception , and teleoperation in autonomous systems. Publications Michael’s recent articles highlight his expertise in robotics and AI. His 2022 paper in Robotics and Autonomous Systems explores Active Visual SLAM for omnidirectional robots. Earlier works (2021 and 2020) address reinforcement learning for automated inspection and aerial human motion capture using drones. Professional Experience He has worked at industry leaders BMW Group and Porsche AG , interned at Fraunhofer IPA , and contributed to research at the Max Planck Institute for Intelligent Systems and the TUM Chair of Astronautics . His roles blend academic research with industrial applications. Labs & Teams Michael collaborates with the Schmalstieg Working Group at VISUS and has been part of teams at TUM , Fraunhofer IPA , Max Planck Institute , and Porsche AG .

Sophie SadlerView profile

Researcher

• Data Visualization
• Network Science
• Computational Intelligence
• +3 more

Sophie Sadler is a Research Associate at the Visualization Institute of the University of Stuttgart , affiliated with the Sedlmair Working Group . Her work focuses on advanced data visualization techniques and network science, particularly in analyzing complex systems through computational intelligence and evolutionary algorithms. Email: sophie.sadler@vis.uni-stuttgart.de Address: Allmandring 19, Stuttgart, Germany Location: Room 00.005 Her recent research involves extrema graphs for fitness landscape analysis, explainable community detection in networks, and feature selection for post-hoc community explanation. These works intersect network science , machine learning , and human-computer interaction , emphasizing interpretability in complex systems. Publications appear in venues like GECCO and Complex Networks , with applications in multi-objective optimization and dynamic systems. Labs & Teams: Sedlmair Working Group at the Visualization Institute of the University of Stuttgart.

Sebastian BaderView profile

Researcher

• Neuro-Symbolic Artificial Intelligence
• Human Activity Recognition
• Assistive Systems for Dementia
• +7 more

Sebastian Bader is a research scientist at the Institute for Visual & Analytic Computing at the University of Rostock, where he contributes to the Chair of Mobile Multimedia Information Systems. His work bridges artificial intelligence, human-computer interaction, and healthcare applications, particularly in assistive technologies. Doctoral Degree: Dr. rer. nat. in Neural Symbolic Integration, TU Dresden, 2009 (summa cum laude) His research interests focus on neuro-symbolic AI, human activity recognition, and intelligent assistive systems for dementia care and neurorehabilitation. He investigates how AI can support clinical interventions, improve patient monitoring, and enhance explainability in medical contexts. His work emphasizes ethical considerations, real-world usability, and integration with sensor and robotic systems. The recent publications highlight a strong trend in applying AI to healthcare, particularly in modeling patient behavior, evaluating robotic assistance, and developing explainable and ethical AI systems. Topics span from underwater robotics to Alzheimer’s diagnostics, showcasing interdisciplinary expertise. He supervises Bachelor's and Master's theses on topics such as visualizing uncertainty in ML, semi-automatic test generation using LLMs, and building probabilistic digital shadows of the Port of Rostock. Sebastian Bader actively collaborates with medical researchers and industry partners, and his projects include "SAMi" (sensor-based activity management for dementia) and "E-BRAiN" (robot-assisted neurorehabilitation). He contributes to both technical AI development and real-world deployment in healthcare environments.