Find Academic Supervisors

• Computer Vision
• Machine Learning
• Autonomous Driving
• +4 more

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

• Robotics
• Photogrammetry
• Simultaneous Localization and Mapping (SLAM)
• +12 more

Cyrill Stachniss is a Full Professor at the University of Bonn , where he heads the Lab for Photogrammetry and Robotics and is affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence . He was previously a Visiting Professor in Engineering at the University of Oxford until 2025. His academic journey includes positions at the University of Freiburg, University of Zaragoza, and the Swiss Federal Institute of Technology. University: University of Bonn School: Faculty of Engineering Department: Department of Photogrammetry Academic Rank: Professor His research spans robotics, photogrammetry, SLAM, autonomous navigation, perception systems, agricultural robotics, and unmanned aerial vehicles . He emphasizes probabilistic techniques for mobile robots and has made significant contributions to visual and LiDAR-based localization, scene understanding, and 3D reconstruction. The recent publications highlight a strong trend toward neural implicit representations, agricultural phenotyping, radar-based perception, and active learning . His team develops robust systems for real-world deployment in dynamic and unstructured environments, particularly in precision farming and autonomous vehicles. Scientific Awards: IEEE RAS Early Career Award (2013) Microsoft Research Faculty Fellow (2010) 7th EURON Georges Giralt Award (2008) Multiple Best Paper Awards at ICRA, IROS, RSS, and RAL Faculty Teaching Award, University of Freiburg He has advised numerous students and leads the DFG Cluster of Excellence PhenoRob and Research Unit FOR 1505 Mapping on Demand . His lab has co-founded three startups, reflecting strong industry and societal impact. He also runs the educational video series 5 Minutes with Cyrill , explaining key robotics concepts.

Margret KeuperView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning Model Robustness
• +7 more

Prof. Margret Keuper is a Professor of Machine Learning at the University of Mannheim's School of Business Informatics and Mathematics, leading the Data and Web Science Group. She is also affiliated with the Max-Planck-Institute for Informatics and ELLIS (fellow since 2024). Her research focuses on robust deep learning, neural architecture search, and computer vision tasks like motion segmentation and adversarial defense. She holds a PhD from the University of Freiburg and previously held positions at the University of Siegen and the University of Mannheim. Her work spans projects funded by DFG and BMBF, including Climate Visions for social media analysis and TrackOpt for motion tracking. She teaches courses on computer vision, generative models, and reinforcement learning. She actively serves on program committees for top conferences like CVPR, ECCV, and NeurIPS, and is an associate editor for IEEE TPAMI and JAIR. Education: PhD in Computer Science from University of Freiburg (advisor: Thomas Brox) Research Projects: Learning to Sense (DFG), Climate Visions (BMBF), TrackOpt (BMBF) Key Roles: Head of Mannheim Master in Data Science Examination Board, Member of MSc Business Informatics Board Her research emphasizes robustness in AI systems, with contributions to adversarial attacks, domain generalization, and efficient solvers for large-scale problems. She advises over 15 PhD students across academic and industry partnerships.

• Medical Image Analysis
• Machine Learning
• Multiple Instance Learning
• +3 more

Ario Sadafi is a researcher at the Technical University of Munich (TUM) , affiliated with the Chair of Computer Science Applications in Medicine under Prof. Nassir Navab. His work spans medical image analysis , machine learning , and computational pathology , with a strong focus on developing AI-driven solutions for microscopic imaging in hematology and oncology. Research Focus: Multiple Instance Learning for weakly supervised medical image classification. Explainable AI for biomedical single-cell imaging. Continual and cross-domain learning for robust diagnostic models. Microscopic image analysis for blood cell disorders and leukemia subtyping. Teaching Contributions: Sadafi has been actively involved in teaching courses such as Computer Aided Medical Procedures , Medical Augmented Reality , and Deep Learning for Medical Applications . He also supervises practical courses and seminars in 3D Computer Vision and Machine Learning in Medical Imaging . Labs & Collaborations: He works closely with the MEDIA (Medical Image Analysis) and NARVIS labs at TUM, contributing to projects in surgical data science , generative models , and robotics & ultrasound . Publications Impact: His research output (2018–2025) emphasizes AI-driven hematology , with applications in red/white blood cell classification, leukemia subtype diagnosis, and interpretable deep learning models for clinical use.

• Artificial Intelligence
• Decision-Making Systems
• Fuzzy Logic
• +5 more

Núria Agell Jané is a Full Professor at ESADE Business School , Universitat Ramon Llull, specializing in Artificial Intelligence and Decision-Making Systems. She leads the JUICE (Judgements and Decisions in the Market Place) research group and the ESADE D3 - Institute for Data-Driven Decisions . Doctorate in Applied Mathematics (Qualitative Reasoning Modelling), UPC-BarcelonaTech Bachelor's in Mathematics, University of Barcelona Her research focuses on Artificial Intelligence , Decision-Making Systems , and Fuzzy Logic , with applications in Business, Marketing, and Sustainability. Recent publications emphasize Hesitant Fuzzy Linguistic Term Sets , Consensus Modeling , and AI in Sustainable Development . She coordinates multiple publicly and privately funded projects applying AI to Business and Marketing challenges. As PhD Programme Director (2005-2013) and current Department Director of Operations, Innovation and Data Sciences , she has shaped academic and research strategies at ESADE. Her work spans collaborations with institutions like LAAS-CNRS (France) and University of Edinburgh Business School , with over 40 journal publications and 50 conference contributions. She has directly supervised 11 PhD students in AI and Decision Sciences.

• Probabilistic Machine Learning
• Physics-Informed Machine Learning
• Industrial Applications of Data Science
• +4 more

Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.

Pierre MonninView profile

Researcher

• Knowledge Graphs
• Semantic Web
• Graph Embedding
• +4 more

Pierre Monnin is a Junior Fellow in AI at Université Côte d'Azur , conducting research within the Wimmics team at the I3S Laboratory . He also teaches within the EFELIA Côte d'Azur program. His work spans multiple institutions through funded projects like SHACKLE (EU Horizon), ECLADATTA and AT2TA (ANR), with collaborations at Télécom Paris , Università di Bari , and INESC-ID in Lisbon. Previous roles include temporary lecturer at TELECOM Nancy (2023-2024) and researcher at Orange (2020-2023). Research Interests focus on the knowledge graph lifecycle (construction, matching, refinement, mining, discovery) from neurosymbolic AI and analogical reasoning perspectives. He explores Domain knowledge injection into ML models Symbolic-semantics for graph embeddings Zero-shot bootstrapping techniques Context-aware semantic annotation Link prediction with constraint enrichment Life sciences applications Recent scientific awards include: Best Paper Award at ESWC 2024 (Student & Resource Papers) Best Thesis Award from French Association EGC (2022) 1st Prize (Accuracy Track) at Semantic Web Challenge (2021) His teaching portfolio covers AI fundamentals for foreign languages, marketing, and adult education programs, with specialized courses in Semantic Web technologies NoSQL databases XML tools Compiler implementation He supervises multiple PhD students and interns on topics involving neurosymbolic refinement , knowledge reconciliation , and analogical reasoning . Key software contributions include: KGPrune - Web application for thematic Wikidata subgraph extraction PyGraft - Synthetic knowledge graph generation tool DAGOBAH UI - Semantic table interpretation interface He also maintains datasets like PGxLOD and YAGO4-LP for pharmacogenomics and link prediction.

• Artificial Intelligence
• Decision Making under Uncertainty
• Machine Learning
• +6 more

Núria Agell Jané is a Full Professor in the Department of Operations, Innovation and Data Science at ESADE Business School. She also serves as a researcher at the ESADE D3 - Institute for Data-Driven Decisions. With a PhD in Applied Mathematics from UPC (Universitat Politècnica de Catalunya), her academic career spans several decades with significant contributions to artificial intelligence and decision science. Her research focuses on developing Artificial Intelligence techniques for decision making under uncertainty. Specifically, she works on systems with automatic learning, qualitative and fuzzy reasoning, multi-criteria decision making, group decision systems, recommendation systems, and consensus modeling. Her methodological approach combines theoretical foundations with practical applications in business contexts, particularly in marketing and strategic decision making. Her recent publications demonstrate a strong trend toward applying hesitant fuzzy linguistic approaches to complex decision problems across diverse domains including media analysis, food waste reduction, healthcare fraud detection, and sustainable energy planning. These works consistently integrate AI methodologies with real-world business challenges, showing her commitment to bridging theoretical research with practical applications. Professor Agell has directed or co-directed 11 doctoral theses and maintains active research collaborations with institutions including The University of Edinburgh Business School. She coordinates the Research Group Judgements and Decisions in the Market Place (JUICE), which focuses on applying AI to business decision contexts. Her research has been supported by multiple projects related to AI applications in marketing and business strategy.

• Computer Vision
• Machine Learning
• Video Analysis
• +5 more

Prof. Laura Leal-Taixé is an Associate Professor at the Technical University of Munich (TUM) leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Chair, promoted from a 2017 Tenure Track Assistant Professorship. Her work focuses on advancing computer vision and machine learning, particularly in video analysis, multi-object tracking, and autonomous systems. She received a Sofja Kovalevskaja Award (2017) for her project socialMaps, which integrates dynamic social data into traffic modeling. Education: B.Sc./M.Sc. in Telecommunications Engineering, Technical University of Catalonia (UPC), Barcelona Ph.D. in Information Processing, Leibniz University Hannover (2014) Postdoc at ETH Zurich (2014–2016), and Senior Researcher at TUM’s Computer Vision Group (2016–2019) Research Interests: Multi-object tracking and segmentation in videos Motion analysis and semantic segmentation for autonomous driving Deep learning for video understanding Social dynamics modeling in urban environments Awards & Grants: €1.65M Sofja Kovalevskaja Award (Humboldt Foundation, 2017) DAAD Australia-German Joint Research Scheme (2017) Multiple travel grants from CVPR and Women in Computer Vision Labs & Collaborations: Dynamic Vision and Learning Group at TUM Collaborations with ETH Zurich, Northeastern University, and NVIDIA

Oliver EberleView profile

Researcher

• Explainable AI
• Natural Language Processing
• Digital Humanities
• +3 more

Oliver Eberle is a Researcher at the Machine Learning Group within the Technical University of Berlin (TU Berlin), affiliated with BIFOLD – Berlin Institute for the Foundations of Learning and Data. He holds a Joint M.Sc. in Computational Neuroscience (2017) and a Ph.D. in Machine Learning (2022) from TU Berlin and Humboldt-Universität zu Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing (NLP), and their applications in Digital Humanities and Cognitive Science. Notable projects include XAI methods for transformer architectures, historical corpus analysis, and collaborations with the Max Planck Institute for the History of Science on the Sphaera Corpus. Key contributions include developing frameworks like BiLRP for similarity models, MambaLRP for sequence models, and xMIL for medical imaging. His work bridges technical advancements in AI with interdisciplinary applications in humanities and science.

Katarzyna BozekView profile

Professor

• Deep Learning
• Computational Biology
• Medical Image Analysis
• +8 more

Katarzyna Bozek is a Professor at the University of Cologne and leads the Bozek Lab at the Center for Molecular Medicine Cologne. Her research focuses on developing deep learning methods for quantitative analysis of biomedical image and time series data, with applications in pathology, nephrology, neurology, and cancer research . Collaborations include institutions like the University Hospital Cologne. Research areas: Computational Biology & Medicine, Machine Learning, Biomedical Image Analysis Key projects: Explainable AI for histopathology data, DISK model for behavioral science The lab applies novel computer vision techniques to C. elegans behavior analysis and super-resolution microscopy in kidney research. Recent work includes transformers pretrained on text for Whole Slide Image classification and federated learning models for precision oncology. Advising : Supervised doctoral candidates including Juan Pisula, Maurice Deserno, and Hussein Naji . The lab actively collaborates on interdisciplinary projects, with funding from multiple partners.

Maribel AcostaView profile

Professor

• Databases
• Semantic Web
• Artificial Intelligence
• +3 more

Maribel Acosta is a Professor of Databases and Information Systems at Ruhr-Universität Bochum's Faculty of Computer Science. She holds affiliations with the Institut für Neuroinformatik (INI), focusing on interdisciplinary research combining natural and artificial cognitive systems. Her work spans databases, semantic web technologies, artificial intelligence, and machine learning applied to knowledge graphs. Acosta earned her Ph.D. (Summa Cum Laude, 2017) and Master's in Computer Science from Karlsruhe Institute of Technology (KIT). She has held roles including Junior Professor at RUB (2020–present), Deputy Professor at KIT (2019–2020), and Assistant Lecturer at Heidelberg University (2020). Research Interests: Her primary research focuses on efficient querying of knowledge graphs, integration of machine learning in data management, and semantic web technologies. Key areas include knowledge graph population, federated query processing, and stability in neural networks. Recent work explores applications in automotive systems and social science data integration. Awards: Notable recognitions include a 2022 WWW Best Paper nomination, Aminer's Top-100 Influential Scholars (2020–2021), and multiple teaching excellence awards from KIT (2017–2020). Teaching & Labs: She teaches courses on database systems, knowledge graphs, and artificial intelligence at RUB. Her lab contributes to projects like SMART-KG and has pioneered federated SPARQL query frameworks. She actively supervises doctoral research in knowledge graph applications and machine learning.

Darui JinView profile

Research Fellow

• Computational Pathology
• Medical Image Analysis
• Artificial Intelligence in Diagnostics
• +4 more

Darui Jin is a postdoctoral researcher at the Gerstung Lab, specializing in computational pathology and multimodal data analysis. He received his Bachelor's and PhD degrees in Engineering from Beihang University in 2017 and 2024, respectively. Research Interests: Development of AI-based diagnostic tools for central nervous system tumors Medical image analysis with applications in histopathology Multiple instance learning for molecular characteristic prediction Image and video restoration techniques Infrared image segmentation methodologies

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +5 more

Junxi Chen is an academic affiliated with Dalian University of Technology's School of Software. His research focuses on advanced machine learning techniques, medical image analysis, and computer vision applications in healthcare. He has contributed significantly to adversarial machine learning, robust deep learning models, and medical image segmentation. His work bridges theoretical advancements with practical applications in medical diagnostics and data security. Key research areas include adversarial attacks/defense mechanisms, medical imaging segmentation (e.g., skin lesions, tumors), and innovative neural network architectures like UNet variants. He also explores secure data-hiding techniques in encrypted images and hybrid memory systems optimization. Publications span top venues such as CVPR, IEEE Transactions, and Medical Image Analysis journals. Collaborations with institutions like Dalian University of Technology and international co-authors highlight his global research impact.

• Algorithms
• Computational Geometry
• Shortest Path Planning
• +4 more

Stefan Funke is a Professor at the University of Stuttgart's Institute for Formal Methods in Computer Science, part of the Faculty of Computer Science, Electrical Engineering and Information Technology. His work focuses on algorithm design and computational geometry with applications in road networks, trajectory analysis, and geographic information systems. He leads the Algorithmics Group, developing efficient algorithms for shortest path planning, network optimization, and privacy-preserving queries. Research interests include geometric algorithms, graph theory, and practical implementations of theoretical results in transportation and spatial data analysis. Notable contributions involve contraction hierarchies for fast shortest path queries, trajectory mining techniques, and methods for preserving privacy in network-based computations. Recent work emphasizes scalable solutions for large-scale road networks, including simplification methods that preserve topological features and energy-efficient routing strategies for electric vehicles. His research often bridges theoretical foundations with real-world applications in navigation systems and smart mobility solutions. Awards and grants are not explicitly listed in the provided data, but his extensive publication record indicates sustained recognition in algorithmic research. He advises on multiple interdisciplinary projects involving spatial data processing and algorithm engineering.