Find Academic Supervisors

Massimo FornasierView profile

Professor

• Mathematical Modeling
• Data Analysis
• Image/Signal Processing
• +5 more

Prof. Massimo Fornasier holds the Chair of Applied Numerical Analysis at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His research focuses on mathematical modeling, numerical analysis, and data-driven methods, particularly in areas like compression, sparse recovery, and optimization. He has made significant contributions to consensus-based optimization, control of multiagent systems, and applications in image/signal processing. Education: PhD in Computational Mathematics, University of Padua (2003) Postdoctoral fellowships at University of Vienna, Sapienza University of Rome, and Princeton University Awards: ERC Starting Grant (2012) START Prize (2011) Prix de Boelpaepe (2009) His work bridges theoretical analysis and computational methods, with applications ranging from compressive sensing to machine learning. Recent research emphasizes consensus-based optimization frameworks and their global convergence properties. Editorial roles include journals like Networks and Heterogeneous Media and Calcolo . He leads research groups in areas such as Data Science and Numerical Analysis at TUM.

Erman AcarView profile

Assistant Professor

• Neuro-Symbolic AI
• Causal Inference
• Explainable AI
• +4 more

Erman Acar is an Assistant Professor for Explainable AI in Finance at the University of Amsterdam, affiliated with the Socially Intelligent Artificial Systems (SIAS) group at the Informatics Institute (IvI) and the Cognition, Language and Computation (CLC) lab at the Institute of Logic, Language and Computation (ILLC). His research focuses on Neuro-Symbolic AI architectures that integrate machine learning with symbolic approaches (logical or causal) to enhance reasoning and explainability in single/multiagent scenarios, applied to financial services. Education : PhD in Computer Science (University of Mannheim, 2017), MSc in Computational Logic (TU-Wien/TU-Dresden, 2012). Previous Roles : Postdoctoral researcher at VU Amsterdam (2018-2021), Leiden University (2021-2022). Research Trends : His recent work emphasizes causal discovery via meta-reinforcement learning, aligning with his Neuro-Symbolic AI and XAI themes. A PhD position (2022) highlights his focus on deploying these approaches for fairness and transparency in fintech. Teaching : Co-teaches the course Interpretability & Explainability in AI (University of Amsterdam, 2023) and leads the AI4Fintech initiative. Labs & Teams : Collaborates with the SIAS group (IvI), CLC lab (ILLC), and the upcoming AI4Fintech hub in Amsterdam.

Jae Hee LeeView profile

Research Fellow

• Multimodal Language Models
• Explainable AI
• Neuro-Symbolic AI
• +4 more

Dr. Jae Hee Lee is a postdoctoral researcher in the Knowledge Technology Group at the University of Hamburg, holding a PhD in Computer Science from the University of Bremen. His research focuses on multimodal language models, explainable AI, and neuro-symbolic integration to enhance model robustness and generalization. Previously, he specialized in spatio-temporal reasoning and multiagent systems, supported by grants like the Feodor Lynen Fellowship (2016–2017). He is an associate editor of AI Communications and co-organizes the International Workshop on Spatio-Temporal Reasoning and Learning. His recent work includes projects like LUMO (DFG-funded, 2025–2029), exploring lifelong multimodal learning through compositional knowledge. Lee advises multiple students, including Björn Plüster, developer of the LeoLM German LLM. Key research interests span explainable vision-language models, causal reinforcement learning, and concept-based explanations. He frequently serves on program committees for AI/NLP conferences (e.g., IJCAI, COLING) and organizes reading groups on LLM/XAI topics.

• Artificial Intelligence
• Smart Grids
• Machine Learning
• +5 more

Carlos Ramos is a researcher affiliated with the University of Porto's Faculty of Engineering and the Polytechnic Institute of Porto's GECAD Research Group. He holds a PhD in Electrical Engineering and Computers from the University of Porto (1993). His work focuses on AI applications in energy systems, ambient intelligence, and agent-based systems. Ramos has collaborated extensively with researchers like Zita A. Vale and Sabah Mohammed on projects involving smart grids, manufacturing optimization, and cyber-physical systems. Research Interests : His primary areas include artificial intelligence, smart grids, machine learning, agent-based systems, and ambient intelligence. Recent work emphasizes explainable AI models in building energy management and genetic algorithms for industrial scheduling. Publications : Ramos has over 138 publications spanning journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Intelligent Systems . Key themes include AI-driven energy optimization, IoT applications in smart cities, and multi-agent systems for market simulation. His work often bridges theoretical AI with practical industrial and healthcare applications. Labs/Teams : Active in the GECAD Research Group, focusing on intelligent engineering solutions. Collaborates with international partners on projects like the Mascem electricity market simulator and the ISEM agent-based marketplace.

Christof WittwerView profile

Academic

• Smart Grids
• Renewable Energy Systems
• Energy Management
• +3 more

Prof. Christof Wittwer is Director of the Business Area 'System Integration' at Fraunhofer Institute for Solar Energy Systems (ISE) in Freiburg. He holds an honorary professorship at the University of Freiburg's Faculty of Environmental and Natural Resources, contributing to master’s programs in Renewable Energy Engineering and Management. His career includes roles as a researcher, postdoc, and department head at Fraunhofer ISE, focusing on distributed energy systems, grid integration, and control engineering. Education: Diploma in Electrical Engineering (University of Kaiserslautern) Key Roles: Director of System Integration, Honorary Professor, and former Head of 'Smart Grids' Department Research: Smart Grids, Energy Management, and System Simulation His research emphasizes system integration of renewable energy, grid resilience, and decentralized energy systems. Notable projects include the Fraunhofer Cluster of Excellence for Integrated Energy Systems (CINES) and contributions to the C/sells initiative for future energy systems in Southern Germany. Publications span smart grid controls, stochastic optimization, and renewable energy policy, with a focus on practical implementation and system-level solutions.

Frieder StolzenburgView profile

Professor

• Artificial Intelligence
• Knowledge Representation
• Machine Learning
• +7 more

Prof. Dr. Frieder Stolzenburg is a Professor for Knowledge-Based Systems at the Harz University of Applied Sciences since 2002. Current roles include Vice Rector for Research and Equal Opportunities (2022-2027) and Deputy Head of the Doctoral Center for Engineering and Information Technology . Major research areas span Artificial Intelligence, Machine Learning, Multi-Robot Systems, Cognitive Reasoning , and Music Cognition . PhD in Disjunctive Logic Programming (1998) Habilitation on Multiagent Systems and RoboCup (2006) Academic Leadership: Vice Dean (2018-2022), Program Coordinator Research focuses on hybrid AI systems combining formal logic with cognitive models, including periodicity detection in music perception and UAV-based agricultural monitoring . Recent work explores large language models for explainable AI and deep learning applications in plant disease classification . Selected scientific awards: Best Poster/Demo at KI-2012 3rd Place RoboCup German Open 2008 Koblenz University Prize 1993 Prolog Programming Competition 3rd Place 1994 Key memberships include CLAIRE , German Informatics Society (GI) , and German University Association (DHV) . Teaching emphasizes Agent Systems , Theoretical Computer Science , and Intelligent Knowledge Engineering .

Zhenzhang YeView profile

Researcher

• Optimization
• Photometry-Based Reconstruction
• Visual SLAM
• +4 more

Zhenzhang Ye is a researcher affiliated with the Computer Vision Group at the Technical University of Munich (TUM) , part of the TUM School of Computation, Information and Technology. His work focuses on Photometry-Based Reconstruction , Optimization , and Geometry Processing , with additional interests in Visual SLAM , Deep Learning , and Biomedicine . Research Interests : Optimization techniques, Photometry-Based Reconstruction, and geometric processing for computer vision tasks. Publications : Active contributor to conferences like CVPR, AISTATS, AAAI, and ICCV, with recent works on 3D human motion prediction, hypergradient estimation, and photometric stereo. Contact: yez@in.tum.de

Artur AndrzejakView profile

Professor

• Artificial Intelligence
• Programming Systems
• Distributed Systems
• +6 more

Prof. Dr. Artur Andrzejak is a full professor (W3) at Heidelberg University since 2010, serving as the Head of the research group Artificial Intelligence for Programming (AIP), and currently as Executive Director of the Institute of Computer Science. He has held significant academic roles including Deputy Dean of the Faculty of Mathematics and Computer Science (2019-2024), Dean of Studies for Computer Science (2015-2017), and Liaison Professor of the German National Academic Foundation (2014-2023). Habilitation in Computer Science (2009), Freie Universität Berlin Ph.D. in Computer Science (2000), ETH Zurich Diploma in Mathematics (1995), Freie Universität Berlin His research focuses on artificial intelligence applications in software engineering, particularly code generation and error correction techniques. He has also made significant contributions to software aging analysis, distributed systems, and cloud computing, with publications spanning federated learning, secure computation, and software rejuvenation. Recent publications demonstrate a strong focus on code generation and error correction methods using large language models, while earlier work includes foundational studies in distributed systems, grid computing, and software rejuvenation techniques. His research combines theoretical computer science with practical applications in enterprise software systems. As an academic leader, he has organized Dagstuhl Seminars on Parallel Data Analysis (2013) and Self-Healing Systems (2009), and served on editorial boards for the journals Parallel Processing Letters and Multiagent and Grid Systems. He has held various leadership roles in academic institutions and research networks.

Hagen LangerView profile

Researcher

• Natural Language Processing
• Knowledge Representation
• Multiagent Systems
• +7 more

Dr. habil. Hagen Langer is a Senior Researcher at the University of Bremen since 2004 and Managing Director of the Collaborative Research Center EASE (SFB 1320) since July 2017. He previously served as Technical Coordinator of the EU-funded RoboHow project until 2016. Education: Doctoral Degree - Georg-August-Universität Göttingen Habilitation - University of Osnabrück His research focuses on natural language processing , knowledge representation , multiagent systems , cognitive robotics , and machine learning . These interests are reflected in his work on semantic interoperability, autonomous logistics, and knowledge-intensive robotic tasks. Key publication trends include: Robotics research with emphasis on cognitive capabilities (2006-2016) Multiagent system architectures for logistics and decision support Knowledge representation frameworks for autonomous processes Natural language processing techniques for semantic understanding Simulation modeling for smart materials and logistics Scientific Awards: Best Student's Paper Award 2009 (Proc. of KDIR 2009) As Managing Director of CRC EASE , he leads research on Everyday Activity Science and Engineering, focusing on knowledge-enabled autonomous systems integration.

• Artificial Intelligence
• Digital Transformation
• Industry 4.0
• +8 more

Hartwig Baumgärtel is a Professor of Engineering at Technical University of Ulm (THU), serving as Head of the Institute for Business Organization and Logistics, Dean of Studies in Faculty P, and Dean of Studies for Industrial Engineering/Logistics. He also chairs the Joint Examination Board for WL and is actively involved in the Cooperative Doctoral College 'Cognitive Computing in socio-technical Systems'. His research spans several key areas: Digital Transformation and Industry 4.0 applications in production and logistics, Semantic Web technologies and ontologies, Multiagent Systems, and Digital Twins. His work focuses specifically on skill- and service-based industrial production, intralogistics, semantic service discovery, lifecycle management of flexible production systems, Industrial Internet of Things, cyber-physical systems, and service-oriented architectures. He has developed expertise in OPC UA-based communication protocols for warehouse and production systems. Baumgärtel's recent publications (2023-2024) reveal a strong emphasis on Warehouse 5.0 concepts, skill-based production systems using OPC UA, and the integration of Digital Twins with agent systems. His work shows a clear trajectory toward creating more flexible, autonomous, and intelligent production and logistics systems through the convergence of semantic technologies, digital twins, and agent-based architectures. He leads significant research projects including EU-funded initiatives Productive4.0 (2017-2020) and SC^3 (2021-2023), plus THU's OPC UA-based infrastructure development for learning factories (2018-2025). Baumgärtel teaches courses on Digital Transformation, Cyber-physical Systems, Internet of Things, Data Mining, Enterprise Information Systems, Supply Chain Management, and Quantitative Planning Methods. He also delivers international instruction through TUM Asia Singapore's Master programs in Advanced and Digital Manufacturing. Professionally, he serves on the VDI/VDE GMA FA 3.35 Agent Systems committee and the IFAC TC 3.1 Computers for Control, Working Group Industrial Agents, demonstrating his leadership in these technical domains.

Daniel CremersView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +10 more

Prof. Daniel Cremers is a Professor of Informatics and Mathematics at the Technical University of Munich (TUM), leading the Chair of Computer Vision & Artificial Intelligence within the TUM School of Computation, Information and Technology. His work spans computer vision, machine learning, and mathematical image analysis, with contributions to autonomous systems, SLAM, and self-driving cars. Affiliations: Chair of Computer Vision & AI, TUM School of Computation, TUM. Education: Bachelor's in Mathematics and Physics (University of Heidelberg), Master's in Theoretical Physics (University of Heidelberg), PhD in Computer Science (University of Mannheim). Research Interests: Focuses on computer vision fundamentals, deep learning, variational methods, and real-world applications like autonomous systems. Key areas include 3D reconstruction, SLAM, and geometric processing. Recent Contributions: Over 12 papers accepted to CVPR 2025, including work on probabilistic bundle adjustment and dynamic scene reconstruction. Notable achievements include the ECCV 2024 Koenderink Award for LSD SLAM and the Leibniz Prize (2016). Awards: Leibniz Prize, ERC grants (Starting, Consolidator, Advanced), multiple best paper awards. Teaching: Offers courses like Computer Vision II: Multiple View Geometry and Introduction to Deep Learning. Supervises practical courses on deep learning methods and autonomous systems. Labs/Teams: Leads the Computer Vision Group, actively organizing conferences (e.g., GCPR/VMV 2024) and contributing to open datasets like DeepScenario. Engages in industry through startups and advisory roles.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

Dan Liu is a prolific academic researcher with affiliations at institutions including University of California at San Diego and Beijing Institute of Technology . His work spans interdisciplinary areas such as computer science , biomedical informatics , and environmental science . Research interests include few-shot learning , multi-agent systems , deep learning for ecology , and data augmentation in imbalanced diagnostics . His publications highlight collaborations in signal processing , bioinformatics , and urban sustainability . Recent articles focus on marine microalgae classification , carbon emissions prediction , and wildlife action recognition , reflecting a trend toward environmental AI and resource optimization across domains.

• Vehicular Edge Computing
• Internet of Things Security
• Wireless Networks
• +6 more

Liang Zhao is a prominent professor at Shenyang Aerospace University's School of Computer Science, with extensive research contributions in vehicular edge computing, IoT security, and wireless communications. Previously earning a PhD from Napier University's School of Computing in 2011, Zhao has established himself as a leading researcher in mobile computing through numerous high-impact publications in IEEE journals and conferences. Dr. Zhao's research focuses on cutting-edge challenges in vehicular networks, UAV communications, and edge computing systems. His work bridges theoretical advances in deep reinforcement learning with practical applications in transportation and IoT systems, particularly addressing task offloading optimization, resource allocation, and security challenges in mobile environments. Recent projects explore innovative solutions for LEO satellite constellations, RIS-assisted communications, and federated learning applications across distributed systems. Analysis of Zhao's recent publication trends reveals a strong emphasis on practical implementations of AI techniques for mobile computing challenges, with significant contributions to vehicular edge computing frameworks, secure IoT protocols, and energy-efficient UAV communications. His research consistently addresses real-world constraints like network limitations, harsh environments, and heterogeneous system requirements. Dr. Zhao maintains an exceptionally productive research trajectory with 246 publications spanning from 2009 to 2025, demonstrating sustained contributions to the field. His collaborative network is extensive, with particularly strong partnerships with Ammar Hawbani (113 co-authored papers), Na Lin (41 papers), and Xingfu Wang (40 papers), suggesting well-established research groups and international collaborations. While specific grant information isn't detailed in the publication record, the scale and scope of Zhao's research output across multiple institutions suggest significant research funding support for projects in mobile computing, vehicular networks, and edge computing systems.

• Linguistic Theories
• Philosophical Semantics
• Multimodal Communication
• +7 more

Dr. Andy Lücking is a postdoctoral researcher at Goethe University Frankfurt, specializing in multimodal communication and cognitive semantics. He serves as Principal Investigator for the GeMDiS project within the ViCom SPP initiative, and previously held research fellowships at Université Paris Cité's Laboratoire de Linguistique Formelle and Frankfurt's Text Technology Lab. Current research focuses on neurocognitive semantics Developed iconic gesture theory with TTR/RTT frameworks Created multimodal corpora (FraGA, DoTT, TGVCorp) Active in computational educational linguistics Contributed to annotation tools (TextAnnotator, DependencyAnnotator) His work combines theoretical modeling with experimental methods, spanning over a decade of contributions to dialogue semantics, gesture-speech integration, and semantic role labeling. Recent projects explore VR-based multimodal research (Va.Si.Li-Lab) and diachronic dependency parsing. He has co-authored 20+ publications including proceedings in SemDial, LREC, and Springer HCI volumes. Collaboration network includes: Jonathan Ginzburg (dialogue theory), Alexander Mehler (computational linguistics), Alexander Henlein (VR research), and Max Planck Institute researchers. Key methodologies involve corpus creation, machine learning, and cognitive modeling of referential phenomena.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +7 more

Frans A. Oliehoek is a Full Professor in Interactive Learning and Decision Making at the Department of Intelligent Systems, Delft University of Technology. He co-leads the Sequential Decision Making group and serves as director and co-founder of the ELLIS Delft Unit. His academic affiliations include senior membership at AAAI, board membership at IFAAMAS, and associate editor roles for JAIR and AIJ. His research focuses on interactive learning and decision making , integrating AI, machine learning, and game theory to develop algorithms for agents interacting in complex, dynamic environments. Key themes include Bayesian reinforcement learning , influence-based abstraction , multi-agent coordination , and uncertainty modeling in real-world applications like traffic control, human-AI collaboration, and e-commerce agents. Recent publications highlight advancements in Bayesian RL with factored POMDPs , multi-agent safety guarantees , and state abstraction . His work spans theoretical foundations (e.g., Nash equilibria, MDP homomorphic networks) and practical frameworks (e.g., SHARPIE for human-AI experiments). Scientific contributions include: ELLIS Fellowship Senior AAAI Membership Best Paper Award at ALA 2021 Outstanding Paper Award at RLC 2024 He has advised students like Miguel Suau and Robert Loftin, with applications in multi-agent reinforcement learning , Bayesian planning , and human-AI interaction . Collaborations span institutions like UC Berkeley, University of Amsterdam, and MIT.