Mathias Weske is a Professor at the Hasso Plattner Institute in Potsdam, Germany. His research focuses on Business Process Management (BPM), Process Mining, Blockchain-based process execution, and Robotic Process Automation (RPA). He has contributed extensively to advancing methodologies for resource allocation in processes, decision support systems, and integrating blockchain technology into collaborative processes. Key research areas include business process analysis, data-driven case management, and healthcare process optimization. His work spans theoretical frameworks (e.g., BPMN extensions, choreography models) and practical tools (e.g., OpenBPT platform). Recent efforts emphasize RPA complexity metrics, accessibility in process modeling for visually impaired users, and democratizing RPA mining techniques. Publications frequently explore interdisciplinary applications like healthcare process mining and blockchain transaction ordering. His contributions bridge theory and practice, addressing challenges in automation, collaboration, and process intelligence across industries.
Lior Limonad is a Researcher at IBM Research, Haifa, Israel, specializing in the intersection of artificial intelligence (AI), business process management (BPM), and ontology engineering. His work focuses on leveraging large language models (LLMs) for explainable business processes, causal reasoning in AI systems, and enhancing decision-support mechanisms through advanced analytics. Research Interests: AI-driven process optimization Explainability in machine learning Cybersecurity for IoT systems Ontology development for enterprise systems Human-AI collaboration in education and motor skill acquisition Publications highlight trends in applying LLMs to business processes, causal inference frameworks, and hybrid systems integration. Collaborations include institutions like the University of New Brunswick and industry partners like Siemens.
Sadegh Soudjani is currently a Senior Research Group Leader at the Max Planck Institute for Software Systems (MPI-SWS) since December 2023. Prior to this, he served as the director of the AMBER group and a Reader (full professor) in Cyber-Physical Systems at Newcastle University, United Kingdom. His research focuses on developing integrated environments for programming, verifying, and designing high-performance, scalable cyber-physical systems in uncertain physical environments. Education: BSc in Pure Mathematics BSc in Electrical Engineering MSc in Control Engineering PhD in Systems & Control Research Interests: Formal synthesis, abstraction, and verification of complex dynamical systems under probabilistic temporal specifications. Applications include energy networks, autonomous systems, smart grids, transportation systems, and systems biology. Projects: SymAware (€4 million EIC-funded), CodeCPS (EPSRC-funded), MoVeS, Safe-CPS, AMBI. His work bridges control theory and formal methods, with a focus on stochastic hybrid systems. Scientific Awards: EPSRC New Investigator Award (2021, £484k) DISC Best PhD Thesis Award (2015) QEST Best Paper Award (2018) Newcastle Teaching Award (2020) Best Repeatability Prize at IFAC ADHS'21 Advising & Grants: Supervised 4 PhD students jointly with Max Planck Institute and Ludwig Maximillian University. Secured €4 million for SymAware and €728k for 7 PhD studentships. Led collaborative projects with institutions like KTH, Uppsala, TU Eindhoven, Siemens, and Netherlands Aerospace Centre. Labs & Teams: Currently leads the AMBER group at MPI-SWS, previously directed AMBER group at Newcastle University. Organized international workshops including V2CPS, SNR, VARS, and ARCH (stochastic category).
Patrizia Scandurra is a Professor affiliated with the University of Bergamo, Italy. Her research focuses on formal methods, software architecture, self-adaptive systems, and model-driven engineering. She has contributed extensively to the development of rigorous system design frameworks like ASMETA and has led work on resilience engineering in cyber-physical systems. Scandurra has co-authored numerous papers in top venues such as ECSA, ABZ, and IEEE Transactions, and has served as editor for conference proceedings including ECSA 2024 and ABZ 2024. Her work emphasizes practical applications of formal methods in safety-critical systems, IoT, and medical devices, with a recent focus on explainable AI and trustworthiness in autonomous systems. Areas: Formal Methods, Self-Adaptation, Cyber-Physical Systems Tools: ASMETA, HYPpOTesT Toolkit Key Projects: MVM-Adapt, RAMSES, IPSOS emergency response system Her research spans theoretical advancements and practical implementations, often bridging gaps between model-driven approaches and real-world system deployment. Current trends include addressing uncertainty in self-adaptive systems, trust analysis for medical devices, andexplainability in robotics.
Hirokazu Kato is a Professor at Kyoto University's Graduate School of Informatics, Department of Applied Mathematics and Physics. He has held academic positions at multiple institutions including Osaka University (PhD 1996), Hiroshima City University (1999-2003), and Nara Institute of Science and Technology (2003-2007). His research spans augmented and virtual reality, human-robot interaction, computer graphics, and healthcare technology. Kato has collaborated extensively with institutions like Chuo University and Nagoya University. Key research areas include AR/VR applications in social interaction, medical training, and industrial automation. He has developed systems for pain relief via robotic touch, AR-enhanced job interview training, and mixed-reality educational platforms. Over 217 publications since 1989 reflect his contributions to fields like near-eye display optimization, gesture recognition, and multimodal interaction design. Notable projects include: Augmented Reality face filters for social anxiety mitigation Huggable robots with intra-hug gesture modeling AR-based physical therapy systems using patient-specific motion simulation General software frameworks for AR industrial tasks His work bridges technical innovation with human-centric applications, emphasizing real-world usability across education, healthcare, and manufacturing domains.
Roberta L. Klatzky is a Professor affiliated with Carnegie Mellon University, contributing significantly to research at the intersection of haptics, robotics, and human perception. Her work focuses on developing and evaluating tactile and haptic technologies, with applications in wearable devices, assistive robotics, and human-computer interaction. Her research interests include the design and perception of tactile feedback systems, sensorimotor control in robotics, and the integration of haptics into medical and assistive technologies. She collaborates extensively with engineers and neuroscientists to advance the field of haptic interfaces and their real-world applications. Recent publications highlight her contributions to wearable haptic systems, such as the PixeLite array and magnetic soft actuators, as well as studies on human perception of texture and dynamic displays. Her work often bridges theoretical psychology and applied engineering, emphasizing practical usability in clinical and robotic contexts. Klatzky’s research also explores cognitive assistance through wearable technologies, leveraging machine learning and data-driven approaches to optimize human-device interactions. Her interdisciplinary approach addresses challenges in tactile rendering, material simulation, and the design of intuitive user interfaces.
Di Zhang is affiliated with Guangdong Medical College's School of Information Engineering and holds a PhD in Synthetic Aperture Radar Image Interpretation from the University of Hamburg (2022). Their research focuses on interdisciplinary fields such as deep learning, remote sensing, optimization algorithms, and their applications in medical imaging, environmental science, and education technology. They have published extensively in top-tier journals like IEEE Access, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Remote Sensing. Key affiliations: University of Hamburg (PhD), Guangdong Medical College, and others listed in disambiguation entries. Research interests include AI-driven medical diagnostics, SAR image analysis, IoT data management, and educational assessment systems. Recent work emphasizes deep learning frameworks for image processing, algorithm optimization, and multimodal data fusion. Publications span diverse topics such as migraine diagnosis via radiomics, social support in online learning, and robust visual SLAM systems. Their work bridges theoretical advancements with practical applications in healthcare, robotics, and environmental monitoring.
April Yi Wang is a tenure-track Assistant Professor at the Department of Computer Science, ETH Zürich, leading the PEACH Lab. She holds core faculty roles at the Institute for Intelligent Interactive Systems and the ETH AI Center. Her research focuses on human-centered approaches in programming, education technology, and data science collaboration. She earned her PhD from the University of Michigan (2023) and MSc from Simon Fraser University (2018), advised by Steve Oney and Christopher Brooks. Education: PhD in Information Science, University of Michigan (2018–2023) MSc in Computer Science, Simon Fraser University (2016–2018) B.Eng in Computer Science, Zhejiang University (2013–2016) Research Interests: Human-Computer Interaction (HCI) Programming Support Systems Collaborative Data Science AI-Enhanced Education Literate Programming Accessibility in Technology Recent Work Trends: Her 2025 publications emphasize AI-driven educational tools (e.g., Math2Visual for math pedagogy, datAR for data literacy), emotion-aware moderation systems, and studies on workplace multitasking. Her work bridges HCI with computational education, focusing on intuitive programming interfaces and inclusive design. Awards: Gary M. Olson Award (2023), ACM CHI Honorable Mentions (2023/2020/2018), Rising Stars in EECS (2022). Grants: Innovedum funding for Coducate project (2025). Lab Focus: Designing expressive systems for programming and data literacy through visual/tangible interfaces, AI co-decomposition tools, and interdisciplinary metaphors. Teaching: Courses on Human-Computer Interaction, Educational Technology, and Mixed Reality at ETH Zürich.
Thorsten Berger is a Professor and Head of the Chair of Software Engineering at Ruhr University Bochum. He previously held positions as an Associate Professor at the University of Gothenburg and Chalmers University of Technology. His research focuses on variability management, software engineering, and model-based systems, with notable contributions to cybersecurity, AI, and robotics. Berger has been awarded prestigious grants including the Wallenberg Academy Fellowship and VR Starting Grant, and his work has been recognized with multiple best paper and influential paper awards. Research Interests : Berger’s work emphasizes software evolution, variability management in systems engineering, and privacy-by-design frameworks for AI systems. His projects include data-driven root-cause analysis, behavior tree verification, and secure mission specification for autonomous systems. He has led initiatives such as the REVaMP2 platform and contributed to collaborative robotics research under the H2020-funded CO4ROBOTS project. Awards : Most Influential Paper Award (SLE 2024, VaMoS 2023) Wallenberg Academy Fellowship (2020) ERC Starting Grant Finalist (2020) He has organized major conferences like SPLC and Dagstuhl seminars, and serves on editorial boards for journals like Science of Computer Programming. His funded projects include collaborations with industry partners like Volkswagen and Phoenix Contact, addressing challenges in configurable systems and safety-critical applications. Thorsten Berger is a member of the Wallenberg AI, Autonomous Systems, and Software Program (WASP) Faculty, the German Hochschulverband, and ACM.
Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.
Raphael Wittkowski is a Professor of Theory of Active Soft Matter at RWTH Aachen University and the DWI – Leibniz Institute for Interactive Materials. He leads a research group focused on theoretical and computational physics of active systems, with strong affiliations in the Department of Physics at RWTH Aachen. Educational Background: Bachelor of Science in Mathematics, Heinrich Heine University Düsseldorf (2009) Bachelor of Science in Physics, Heinrich Heine University Düsseldorf (2009) Master of Science in Physics, Heinrich Heine University Düsseldorf (2010) PhD in Physics, Heinrich Heine University Düsseldorf (2012) Research Interests: His work spans active soft matter, acoustofluidics, microfluidics, microrobotics, programmable and intelligent materials, 3D bioprinting, and theoretical modeling. He investigates systems driven by sound or light, develops simulation software (AcoDyn), and explores applications in medicine and engineering. His research integrates statistical physics, field theory, and artificial intelligence concepts in material design. Publication Trends: Recent publications emphasize field theories for active matter, computational modeling of active Brownian particles, intelligent materials, and biomedical applications of microrobots. His work frequently appears in high-impact journals such as Science Advances , Nature Communications , and Physical Review Letters , reflecting a strong focus on theoretical innovation and interdisciplinary applications. Scientific Contributions: Development of AcoDyn, a Rust-based software for acoustofluidic and microfluidic simulations Leadership in major research projects including Sonocraft and CRC 1459 B01 Heisenberg Grant recipient for research in active soft matter and statistical physics Advising and Grants: He advises PhD students such as Adrian Paskert and leads a dynamic research group. He has secured competitive funding, including a Heisenberg Grant, and is involved in collaborative, interdisciplinary projects that bridge physics, engineering, and medicine. Research Groups and Projects: He leads the Theory of Active Soft Matter group at RWTH Aachen. Key projects include Sonocraft (ultrasound-based 3D bioprinting), CRC 1459 B01 (intelligent light-driven microsystems), and foundational work on active matter theory and simulation frameworks.
Ajay Kumar is a prominent researcher and academic with extensive contributions across multiple domains including Operations Research, Artificial Intelligence, Blockchain, and Biometric Recognition. His work spans a wide range of topics, from digital transformation in supply chains to machine learning applications in healthcare and software engineering. He has collaborated with numerous scholars and published extensively in high-impact journals and conferences. Research Interests: Operations Research, AI in Healthcare, Blockchain Applications, Biometric Recognition, Software Reliability, Digital Transformation Publications: Kumar has authored numerous publications in journals like Annals of Operations Research , IEEE Transactions on Engineering Management , and SN Computer Science , as well as conferences such as CVPR and IC3I. Collaborations: Kumar has worked with leading experts like Kim Hua Tan, Shivam Gupta, Seema Bawa, and others, contributing to interdisciplinary research in supply chain, data science, and IoT. Key Contributions: His research includes innovative frameworks such as blockchain-enabled supply chains, hybrid AI models for diabetes prediction, and scalable tools for genomic data analysis. Emerging Trends: Kumar’s recent work focuses on explainable AI, fake news detection, and integrating IoT with blockchain for secure and efficient systems across health, logistics, and UAV communication.
Prof. Dr.-Ing. Birgit Vogel-Heuser is a Full Professor and Director of the Chair of Automation and Information Systems at the Technical University of Munich (TUM), within the TUM School of Engineering and Design. She holds leadership roles as Vice Dean for Research and Innovation and is involved in strategic initiatives like the Munich Institute of Robotics and Machine Intelligence (MIRMI). Her research focuses on evolvable automation architectures, human-machine interaction, and data-driven manufacturing processes. Education: Diploma in Electrical Engineering (RWTH Aachen, 1987), PhD in Mechanical Engineering (RWTH Aachen, 1990). Industry Experience: Over 10 years in industrial automation, including roles at Phoenix Contact GmbH. Leadership Roles: Head of TUM Strategy and Study Commission for Mechanical/Process Engineering Faculties (since 2025) IEEE Fellow (2023) and Distinguished Lecturer (IEEE RAS) Member of Bavarian Academy of Sciences and Humanities (acatech) Research Interests: Systems/software engineering, embedded systems, Industry 4.0/5.0, digital twins, and cyber-physical systems. Key projects include the KI.FABRIK Bayern initiative and the DFG-funded Priority Program SPP 2422 on data-driven process modeling. Awards: Federal Cross of Merit (2024), IEEE Fellow, Borchers Plaque (1991), and others. Grants/Projects: Lead roles in over 30 funded projects, including CRC 768 and SPP 1593 on software evolution. Publications: Over 200 journal/conference papers, including impactful work on industrial agents and automation architectures. Labs/Teams: Leads the MIRMI 'Work' sector and collaborates with industry partners like Grammer AG and HAWE Hydraulik SE. Active in standardization (e.g., IEEE 2660.1-2020).
Dr. Julio Rogelio Guadarrama Olvera is a researcher and leader of the Humanoid Robotics Group at the Chair of Cognitive Systems, Technical University of Munich (Prof. Gordon Cheng). He holds a Dr.-Ing. (Engineering Doctorate) from TUM, awarded with honors in 2021 for his thesis on humanoid robot whole-body control and biped locomotion. His current role includes postdoctoral research focusing on tactile feedback control, object manipulation, and bipedal locomotion. Education: Bachelor's in Mechatronic Engineering (2011), National Polytechnic Institute, Mexico City Master's in Engineering (2013), Center for Research and Advanced Studies, National Polytechnic Institute, Mexico City PhD in Robotics (2021), Technical University of Munich Research Interests: His work centers on advanced robotics systems, particularly tactile-based control for humanoid robots, bipedal locomotion stability, and human-robot interaction. Key areas include: Development of artificial robot skin for sensory feedback Real-time control algorithms for dynamic environments Ethical and safety frameworks for collaborative robots Applications in healthcare robotics and neuroengineering Teaching: Active in teaching advanced robotics courses such as ' Modelling and Control of Legged Robots ' and ' Practical Course RoboCup@Home ' at TUM. Labs/Teams: Leads the Humanoid Robotics Group within ICS, collaborating with international teams on projects like tactile feedback systems and robot skin technology. Involved in EU and industry-funded robotics initiatives.
Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.
