Find Academic Supervisors

• Process Mining
• Business Process Management
• Stochastic Processes
• +4 more

Dr. Sander Leemans is a Professor at RWTH Aachen University leading the Business Process Management Foundations and Engineering research group. His work focuses on advancing process mining theory and practice with emphasis on stochastic modeling and conformance verification. Leemans' research centers on process mining, business process management, and stochastic process modeling. He investigates conformance checking techniques for probabilistic models, process discovery algorithms, and the integration of exogenous data into process analysis. His work bridges theoretical foundations with practical applications in healthcare, robotic process automation, and inter-organizational systems. Recent publications reveal a concentrated research trajectory in stochastic conformance checking, where Leemans develops methods for matching observed traces to stochastic process models using alignment techniques, entropy metrics, and partial-order reasoning. He also pioneers object-centric process mining frameworks and explores silent transitions in labeled Petri nets, significantly enhancing the precision and applicability of process mining in real-world scenarios. The Business Process Management Foundations and Engineering group under Leemans' leadership drives innovation in process mining through rigorous theoretical development and open-source tooling, maintaining RWTH Aachen's position at the forefront of business process intelligence research.

Michael FinkView profile

Researcher

• Control Theory
• Automation and Robotics
• Agricultural Engineering
• +4 more

Michael Fink is a researcher at the Chair of Automatic Control Engineering , Technical University of Munich . He holds an M.Sc. in Electrical Engineering and Information Technology (2020) and a B.Eng. in the same field from Technical University Munich and University of Applied Sciences Landshut (2018), respectively. Research Interests : Model Predictive Control (MPC) with focus on stochastic and robust variants Optimal control strategies for autonomous driving and vertical farming Constraint violation probability minimization in dynamic systems Publications span topics in: Time-optimal MPC for linear systems Stochastic and robust MPC frameworks Learning-based control for greenhouse climate systems Vertical farming optimization Contact: michael.fink@tum.de

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +4 more

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

Xinyu JiaView profile

Research Fellow

• Uncertainty Quantification
• Structural Reliability
• Risk Assessment
• +3 more

Xinyu Jia is currently a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich since June 2024, and concurrently serves as Associate Professor in the Department of Mechanical Engineering at Hebei University of Technology, China since October 2022. Her research focuses on advancing uncertainty quantification, structural reliability, and risk assessment methodologies for engineering systems. Her academic background includes: PhD in Mechanical Engineering, University of Thessaly, Greece (2018-2021) Bachelor of Engineering and Master of Science in Mechanical Engineering, Hunan University, China (2011-2018) Dr. Jia specializes in Bayesian learning frameworks for physics-based models, with particular expertise in uncertainty propagation in structural dynamics and industrial robotics applications. Her work develops hierarchical Bayesian approaches that integrate multi-level data to enhance predictive accuracy for complex engineering systems, addressing critical challenges in structural health monitoring and risk-informed decision making. Analysis of her 2022-2023 publications reveals a concentrated research trajectory in applying Bayesian inference to structural dynamics, with emphasis on hierarchical modeling techniques, variational inference schemes, and nonlinear model updating. These contributions predominantly appear in top-tier mechanical engineering journals, demonstrating methodological innovations that bridge theoretical statistics with practical engineering reliability problems. Her scientific recognition includes: Humboldt Research Fellowship (2023) Marie Curie Early Stage Researcher Fellowship (2018) No specific student advisement records are documented, though her Associate Professor role implies teaching responsibilities. Her fellowship awards represent significant research funding supporting her work in uncertainty quantification. As an active member of TUM's Engineering Risk Analysis Group, she contributes to high-impact projects including digital twins for ships, S3UQDyn, Navigating Risk, and infrastructure resilience initiatives like BIG-ROHU and INFRA.RELEARN, focusing on probabilistic risk modeling across civil and mechanical engineering domains.

Yan ZhangView profile

Lecturer

• Computer Science
• Artificial Intelligence
• Computer Vision
• +5 more

Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.

Rolf WankaView profile

Professor

• Algorithms
• Combinatorial Optimization
• Swarm Intelligence
• +3 more

Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.

Oliver DeussenView profile

Professor

• Visualization
• Computer Graphics
• Digitalization Processes
• +4 more

Oliver Deussen is a Professor of Visual Computing at the University of Konstanz, recognized by the German Informatics Society (GSI) as a Fellow for his contributions to computer science. His research focuses on visualization, robotics, and environmental modeling, particularly in plant and landscape representation. He has pioneered methods in image manipulation and robotic painting, emphasizing digitalization's societal impacts. His work spans computational biology (e.g., schooling fish behavior) and AI-driven creative technologies. Research Interests: Visualization techniques, swarm behavior analysis, robotic creativity, and interdisciplinary applications of computer science. He explores how computational methods can model natural systems and enhance human-machine interaction. Awards: Fellow of the German Informatics Society (GSI) His research often bridges theory and practice, with contributions to SLAM frameworks, style transfer algorithms, and uncertainty visualization tools. Collaborations in robotics and biology reflect his commitment to applied computational research.

Dongheui LeeView profile

Assistant Professor

• Human motion understanding
• Physical human-robot interaction
• Machine learning in robotics
• +5 more

Dongheui Lee is an Assistant Professor at the Institute of Automatic Control Engineering (LSR) within the Faculty of Electrical Engineering and Information Technology at Technische Universität München (TUM). She leads the Dynamic Human Robot Interaction for Automation System Lab. Her research focuses on human motion understanding, physical human-robot interaction, and machine learning in robotics. Education: B.S. and M.S. in Mechanical Engineering from Kyunghee University (2001-2003), PhD in Mechano-Informatics from the University of Tokyo (2007). Prior roles include research scientist at KIST Korea (2001-2004) and project assistant professor at the University of Tokyo (2007-2009). Research Interests: Human-robot collaboration, probabilistic robotics, motion recognition, and incremental lifelong learning mechanisms. She has contributed to advancements in motion primitives, compliant physical interaction, and real-time object tracking. Selected Awards: Finalist for KUKA Service Robotics Best Paper Award (2009), Hirose Scholarship (2006-2007), and multiple grants from KRF, KOSEF, and international robotics competitions. Key Publications: Focus on prioritized inverse kinematics, motion imitation, and adaptive control systems. Her work bridges robotics theory and practical applications in humanoid robots and human-robot interaction.

Marc ToussaintView profile

Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Marc Toussaint is Full Professor leading the Learning & Intelligent Systems Lab at TU Berlin's EECS Faculty. His research integrates machine learning, optimization, and AI reasoning to solve fundamental robotics problems like physical reasoning and human-robot interaction. He holds a physics diploma from University of Cologne and PhD from Ruhr-Universität Bochum. Key research themes include: Task-motion planning integration Reinforcement learning for robotics Physical simulation and control Probabilistic inference methods Recent publications focus on efficient kinodynamic planning, belief space planning under uncertainty, and neural policy learning. He develops open-source robotic tools like the 'robotic python package' used in academic courses worldwide. Toussaint collaborates with Amazon Robotics and MIT CSAIL, and has held positions at Max Planck Institute and University of Stuttgart.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +3 more

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

Davide TateoView profile

Visiting Professor

• Reinforcement Learning
• Robotics
• Safe and Reliable Robot Learning
• +3 more

Davide Tateo is a postdoctoral researcher and visiting professor at TU Darmstadt, leading the Safe and Reliable Robot Learning Research Group within the Intelligent Autonomous Systems group of the Computer Science Department. His research focuses on developing safe and efficient reinforcement learning algorithms for real-world robotics applications. His work spans Reinforcement Learning (Safe RL, Deep RL) and Robotics (fast motion planning, locomotion). He is involved in multiple funded projects including KIARA (advanced manipulation in risky scenarios), DeepWalking (human gait learning), and INTENTION (active perception for legged robots). Recent publications highlight his expertise in Safe RL (inductive biases, collision probability fields), Locomotion (multi-embodiment, morphology-aware policies), and Optimization (contact planning, trajectory distillation). He collaborates with the PEARL lab at TU Darmstadt and has contributed to key workshops like CoRL 2024 and RSS 2024. Contact details: Email: davide.tateo@tu-darmstadt.de Room E303, Building S2|02, Hochschulstr. 10, Darmstadt Phone: +49-6151-16-20811

Ayush TewariView profile

Assistant Professor

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +3 more

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.

Syed Ali RazaView profile

Researcher

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +3 more

Syed Ali Raza is a researcher affiliated with the University of Technology, Sydney (PhD 2018), with a former affiliation at the Institute of Business Administration, Karachi. His work focuses on reinforcement learning, robotics, and human-robot interaction. Key contributions include studies on social robots' question-answering services, human feedback integration in AI systems, and optimization of robotic movements in multi-agent scenarios. He has collaborated extensively with researchers like Mary-Anne Williams and Sajjad Haider. His research spans theoretical advancements in machine learning algorithms and practical applications in robotics competitions (e.g., RoboCup). Research interests emphasize computational reinforcement learning, reward shaping techniques, and ethical design of autonomous systems. Notable projects include privacy-first approaches for social robots and hybrid methods for humanoid robot locomotion. His work bridges theoretical AI advancements with real-world robotic implementations.

Rayna DimitrovaView profile

Assistant Professor

• Verification
• Reactive Systems Synthesis
• Control and Robotics
• +3 more

Rayna Dimitrova is a Tenure-track faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Previously, she held positions as Lecturer (Assistant Professor) at the University of Sheffield and University of Leicester, and postdoctoral roles at the University of Texas at Austin and the Max Planck Institute for Software Systems. She earned her PhD from Saarland University. Her research focuses on formal methods, including verification and synthesis of reactive systems, applications to control and robotics, quantitative analysis of probabilistic systems, and information-flow security. Key interests include strategic synthesis under partial observability, probabilistic uncertainty, and continuous dynamics, with applications to autonomous systems. She has served in numerous prestigious roles, including PC co-chair for VMCAI 2024, HYPER 2023, and SMT 2018. Her awards include the RS3 Best Paper Award (VMCAI 2012) and nominations for ETAPS (TACAS 2015) and EMSOFT (2014) Best Paper Awards. Her research group includes PhD students Rafael Dewes, Philippe Heim, and Saleh Soudijani. She has taught courses on reactive synthesis, program analysis, and decision procedures at multiple institutions, including the University of Sheffield and TU Kaiserslautern.

• Haptics
• Human-Computer Interaction
• Acoustic Engineering
• +3 more

Yasutoshi Makino is a researcher specializing in ultrasound-based haptics, tactile feedback systems, and human-computer interaction. He has collaborated extensively with Hiroyuki Shinoda and other colleagues, focusing on mid-air haptic displays, noncontact object manipulation, and sensory reproduction. Research Interests Makino's work explores the intersection of acoustics, neuroscience, and engineering to create immersive tactile experiences without physical contact. His innovations include ultrasound-driven actuation mechanisms, thermal sensation rendering, and real-time human motion prediction for robotic systems. Recent Publications The 15 most recent articles highlight advancements in airborne ultrasound tactile displays, texture synthesis via GANs, and applications in guide dog training analysis, virtual reality, and interactive robotics. Key themes include dynamic pressure control , multi-stimulus integration , and low-latency systems . Collaborations Co-authored with Masahiro Fujiwara (43 papers) Collaborated with Hiroyuki Shinoda (145 papers) Worked with Shun Suzuki, Takaaki Kamigaki, and Ryoya Onishi on thermal and mechanical haptic feedback systems.