Find Academic Supervisors

Rudolf GrübelView profile

Professor

• Probability Theory
• Stochastic Processes
• Combinatorial Analysis
• +3 more

Prof. Rudolf Grübel is a full Professor at the Institute for Insurance and Financial Mathematics, Leibniz University Hannover (since 1994). He previously held positions at the University of Paderborn (1993-1994), Technical University of Delft (1989-1993), and Imperial College London (1985-1989). His research focuses on Probability Theory, stochastic processes, and combinatorial structures, with applications to algorithm analysis and actuarial science. Expertise includes renewal theory, random trees, and statistical methods. Contributions to branching processes, Markov chains, and permutons. His work emphasizes probabilistic models in discrete structures (e.g., search trees, random graphs), with recent trends toward copula-based statistical tests and mixture representations of distributions. Key topics include tail behaviors, limit theorems, and boundary theory approaches. Publications span over 40 years, with notable contributions to the Annals of Probability, Journal of Applied Probability, and Electronic Journal of Probability. No awards are listed, but his extensive academic output reflects his influence in stochastic analysis.

• Optimal Transport
• Gradient Flows
• Ricci Curvature
• +7 more

Prof. Dr. Matthias Erbar is a faculty member in the Faculty of Mathematics at Bielefeld University, where he leads the research group AG Erbar. His work is centered on mathematical analysis, with a focus on optimal transport, gradient flows, and discrete geometric analysis. He is affiliated with the Department of Mathematics and maintains an active research profile with numerous publications in top-tier journals. His research interests lie at the intersection of analysis, probability, and geometry. He investigates optimal transport on discrete and continuous spaces, gradient flow structures in PDEs and stochastic processes, Ricci curvature on metric measure spaces and graphs, and functional inequalities such as Poincaré and logarithmic Sobolev inequalities. His work often employs entropy-based methods and explores the geometric implications of curvature conditions in non-smooth settings. The recent publications show a consistent trend toward understanding geometric and analytic properties of discrete systems, including graphs, Markov chains, and configuration spaces. There is a strong emphasis on formulating continuous concepts like Ricci curvature and gradient flows in discrete settings, enabling applications in probability, statistical mechanics, and numerical analysis. The work frequently involves collaboration with leading researchers in the field. Scientific Awards: No awards explicitly mentioned in the provided text. Prof. Erbar advises doctoral students and leads an active research group (AG Erbar), suggesting engagement in graduate supervision and collaborative research. While specific grants are not listed, his publication output and collaborations imply sustained research funding. He has co-authored papers with researchers across Europe, indicating international collaboration and academic leadership. He is the principal investigator of AG Erbar , a research group within the Faculty of Mathematics at Bielefeld University. The group focuses on analysis and probability, particularly in the context of optimal transport and discrete geometry. The group likely includes PhD students and postdoctoral researchers, contributing to the broader research environment in mathematical analysis at Bielefeld.

Dominik MehlemView profile

Researcher

• Point Cloud Compression
• Data Compression
• 3D Graphics
• +4 more

Dominik Mehlem is a Researcher at the Institute of Communications Engineering (RWTH Aachen University), specializing in point cloud compression and video coding . He contributes to the ISO/IEC MPEG standardization process for 3D data compression. Research Focus : Development of compression algorithms for point cloud data, analysis of Versatile Video Coding (VVC) vs. HEVC in video-based point cloud compression (V-PCC), and inter-frame prediction techniques like 3D motion compensation in geometry-based point cloud compression (G-PCC). Email : mehlem@ient.rwth-aachen.de Office Hours : Mon-Thu, 10:00–12:00 and 14:00–16:00 (by appointment) Key Publications : Recent works in Visual Communications and Image Processing (VCIP) and International Picture Coding Symposium (PCS) , focusing on entropy coding, graph-based methods, and VVC optimization for point cloud compression.

• 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.

Johanna SenkView profile

Researcher

• Spiking Neural Networks
• Neuromorphic Computing
• High-Performance Computing
• +3 more

Dr. Johanna Senk is a Team Leader of the Future Simulation Architectures team at the Institute for Advanced Simulation (IAS-6), part of Forschungszentrum Jülich GmbH. Her research focuses on spiking neural networks, neuromorphic computing, and high-performance computing, with an emphasis on simulation technology and reproducible science. She leads efforts to develop scalable architectures for large-scale neuronal network simulations, particularly leveraging GPU-based systems and neuromorphic hardware. Her work bridges theoretical neuroscience and computational engineering, addressing challenges such as network connectivity design, metadata standardization for scientific workflows, and optimizing simulation efficiency. Key contributions include the development of the NNMT toolbox for mean-field analysis and the beNNch framework for performance benchmarking. Senk’s team collaborates extensively on multi-area cortical models and has published extensively on topics like GPU-accelerated neuronal network construction, synaptic dynamics modeling, and the reconciliation of spiking activity with local field potentials. Her research aims to advance both computational methods and biological understanding of neuronal systems.

• 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.

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.

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +3 more

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Pascal BelouinView profile

Researcher

• Digital Humanities
• Knowledge Modeling
• Network Analysis
• +6 more

Pascal Belouin serves as a Digital Humanities Scholar at the Max Planck Institute for the History of Science (MPIWG) in Berlin, part of the Max Planck Society. His role focuses on providing specialized technical support to historians implementing digital methodologies in their research. Belouin holds a Bachelor's degree in Software Engineering from the University of La Rochelle, a Master of Research in Human Computer Interaction from Lancaster University, and a Master of Arts in Social Sciences from the Open University (completed in 2010). His research interests span multiple domains within Digital Humanities, with particular expertise in knowledge modeling, distant reading techniques, network analysis, and data visualization. He is actively exploring innovative approaches to research data preservation through decentralized storage solutions, reflecting his background in software engineering and computer science. Belouin's recent publications demonstrate a consistent focus on research infrastructure development, particularly in the areas of data interoperability, semantic web technologies, and sustainable preservation of research data. His work shows a progression from API development for textual resources toward more comprehensive solutions for long-term data preservation using decentralized approaches. He is actively involved in multiple significant projects at the MPIWG including the Central Knowledge Graph (CKG), CORAL: Commoning Oral Histories of Knowledge, Heavens Map, VoH Image Database, Commoning Biomedicine, Measuring the Earth, and RISE and SHINE: Research Infrastructure for the Study of Eurasia. Before joining the MPIWG, Belouin had significant professional experience in the private sector and held a leadership role with the Medical Schools Council Assessment Alliance, where he spearheaded the design and implementation of the MSCAA Item Bank—a comprehensive web-based platform enhancing collaboration across the UK's medical assessment community.

Robert CastiesView profile

Researcher

• Digital Humanities
• Research Data Management
• Open Science
• +3 more

Robert Casties is a researcher at the Max Planck Institute for the History of Science (MPIWG) in Berlin, where he has worked since 2002. He specializes in digital humanities, focusing on developing and applying digital solutions for historical science research through principles of open data, shared standards, and sustainable technologies. His expertise supports the Institute's digital projects and collaborations globally. Education: MSc (Diplom) in Physics from the University of Hamburg; PhD (Dr. phil. nat) in History and Philosophy of Science from the University of Bern. Casties' research bridges technical innovation with scholarly inquiry in the history of science. He actively contributes to the DH-Tech special interest group of the Association of Digital Humanities Organisations and serves on the Technical Review Committee of IIIF . His work emphasizes collaborative digital infrastructure and sustainable research practices. Notable projects include the Central Knowledge Graph (CKG) , Islamic Scientific Manuscripts Initiative (ISMI) , and Reading Early Medicine . His publications highlight advancements in semantic digital humanities and technical frameworks for digital humanities research.

• Computational Complexity
• Cryptography
• Zero-Knowledge Proofs
• +5 more

Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.