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Yunjie YangView profile

Associate Professor

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
• +5 more

Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

Marta Zofia KwiatkowskaView profile

Professor

• Automated verification
• Probabilistic model checking
• Temporal logics
• +3 more

Professor Marta Zofia Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College at the University of Oxford, where she has held a faculty position since 2007. She previously served as Professor of Computer Science at the University of Birmingham (2001–2007), Reader and Lecturer at the University of Birmingham (1994–2001), and Lecturer at the University of Leicester (1986–1994). Her academic career began as Assistant Professor at the Jagiellonian University in Kraków, Poland (1980–1988). Education: BSc/MSc in Computer Science, Jagiellonian University, Kraków MA, University of Oxford PhD, University of Leicester Research Interests: Professor Kwiatkowska spearheaded the development of probabilistic and quantitative verification methods on the international stage. Her work bridges theory and practice through the PRISM model checker—the leading software tool in probabilistic model checking—used worldwide for research and teaching. Application domains include communication and security protocols , nanotechnology designs , power management , ubiquitous computing and systems biology . She investigates automated verification , temporal logics , semantic models for concurrency , real-time systems , and biological process modelling . Current grant funding exceeds £3.7 million from EPSRC, EU and ERC, including the prestigious ERC Advanced Grant VERIWARE. Scientific Awards & Honours: Fellow of the Royal Society Fellow of the ACM Fellow of the European Association for Theoretical Computer Science (EATCS) Fellow of the British Computer Society (BCS) Fellow of the Polish Society of Arts & Sciences Abroad ERC Advanced Grant VERIWARE (€2.046 M, 2010–2015) Top Cited Article Award, Theoretical Computer Science (2005–2010) Best Paper Award, QEST 2006 Doctoral Supervision & Grants: Professor Kwiatkowska actively supervises doctoral students (D.Phil. at Oxford) and post-doctoral researchers. She welcomes applications in areas aligned with her research interests, detailed here . Current students include Charlie Griffin, Daqian Shao, Matthew Yuan and Minghao Liu; past students and researchers number over twenty, many now in faculty or industry leadership roles. Laboratory & Teams: She leads the Oxford Quantitative Verification group within the Department of Computer Science. Ongoing projects include FUN2MODEL, ELSA, FAIR and the flagship PRISM probabilistic model checker. The group maintains strong collaborations with biological, robotics and engineering teams worldwide.

• Computer Vision
• Autonomous Driving
• Robotics
• +7 more

Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools

• Soft Matter Physics
• Flexible Electronics
• Sustainable Electronics
• +3 more

Martin Kaltenbrunner is a Professor in the Department of Soft Matter Physics at the Faculty of Engineering & Natural Sciences, Johannes Kepler University Linz (JKU). He leads the LIT Soft Materials Lab and is affiliated with the Linz Institute of Technology (LIT), focusing on sustainable material innovations for next-generation electronics. His research spans biodegradable flexible electronics, energy-autonomous systems, and eco-friendly substrates. Key interests include perovskite solar cells for healthcare robotics, mycelium-based electronic skins, and algal polysaccharide conductive nanocomposites. He pioneers sustainable alternatives using organic materials to replace conventional electronics in soft robotics and wearable devices. Recent publications (2024-2025) emphasize circular economy principles, featuring mycelium substrates for PCBs, algae-derived transistors, and passivation techniques for high-efficiency solar cells. The work integrates materials science with environmental sustainability, targeting applications in medical sensors and autonomous robotics. Professor Kaltenbrunner has supervised 18 research works and leads multiple major grants: Personalized Sustainable Smart Patch Omnificence (Persimmon) - EU project (2024-2028) Mycelium-based substrate for sustainable flexible PCBs (MycoSub) - EU project (2024-2025) Intelligent cellulose-based sensors - FFG project (2022-2025) Metasurface Fabrication (META-FAB) - FFG project (2024-2027) Nadelholzreststoffe for mycelium packaging (MycoSoft) - FFG project (2023-2026) He directs the LIT Soft Materials Lab, which develops biodegradable gels, fungal biomaterials, and sawmill byproduct-based insulation. The lab collaborates across JKU's engineering and natural sciences divisions to advance sustainable electronics through interdisciplinary projects like Persimmon and MycoSub, emphasizing real-world deployment of eco-friendly technologies.

• Photonics
• Laser Technology
• Machine Learning
• +5 more

Professor Robert Eason is a leading academic at the University of Southampton, specializing in photonics and laser technology. His research spans interdisciplinary areas combining Machine Learning , Medical Diagnostics , and Microfluidics . Research Interests : Eason focuses on AI-driven laser applications, including deep learning for phototherapy , autonomous laser machining , and low-cost paper-based diagnostic devices . His work bridges photonics with biomedicine and advanced manufacturing. Recent Publications : His 2025 article in Scientific Reports explores AI simulations for psoriasis treatment, while 2024-2022 works address laser-controlled microfluidics, deep learning in microscopy, and reinforcement learning for laser machining. Supervision : He supervises PhD student Georgia Mourkioti in laser-based research projects. External Roles : Eason has served as a speaker at international conferences including the International Symposium on Laser Precision Microfabrication (2018), LAISER (2019), and Deep Learning for Control of Light-Matter Interactions (2022).

Mohamed KhamisView profile

Associate Professor

• Human-Computer Interaction
• Human-centered Security
• Eye Tracking
• +9 more

Mohamed Khamis is an Associate Professor at the University of Glasgow , specializing in Human-Computer Interaction (HCI) with a focus on Human-centered Security and Eye Tracking for privacy protection. His research spans Pervasive Displays , Usable Security , and User Privacy in immersive environments. PhD from Ludwig Maximilian University of Munich (LMU) Supervised by Florian Alt and Andreas Bulling Research Interests: Usable Security and Privacy Designing Gaze-based Systems Thermal Attacks and Shoulder Surfing Mitigation Security in Public Displays and Virtual Reality Recent Publications demonstrate expertise in: Drone Interaction and Proxemics Privacy Scales for Measuring Granular Constructs Gaze-enabled Mobile Authentication Multimodal Security Systems Deepfake Privacy Applications XR Dark Patterns Analysis Scientific Contributions: Recipient of multiple Honorable Mention Awards at CHI and MobileHCI Funding from EPSRC for thermal imaging research Keynote speaker at ECCV 2020 Open Eyes Workshop Co-organizer of ETRA workshops on Eye-Gaze for Security

Marc PollefeysView profile

Professor

• 3D Computer Vision
• Robotics
• Machine Learning
• +5 more

Marc Pollefeys is a Full Professor of Computer Science at ETH Zurich and Director of the Microsoft Mixed Reality and AI Zurich Lab. He has held roles such as Visiting Professor at Stanford University (2007) and Assistant/Associate Professor at UNC-Chapel Hill (2002–2009). His research focuses on 3D computer vision, robotics, machine learning, and augmented reality. Education: PhD in Computer Science from KU Leuven (1999), followed by postdoctoral research there until 2002. He transitioned to academic roles at UNC-Chapel Hill before joining ETH Zurich in 2007. Research interests include 3D reconstruction, visual localization, SLAM, and applications in archaeology, urban modeling, and robotics. Notable projects include real-time 3D scanning, city-scale reconstruction, and autonomous vision-based drones. Key awards include ACM Fellow (2022), IEEE Fellow (2012), and ERC Starting Grant (2008). He advises numerous PhD students and collaborates with institutions like Google and Microsoft. Labs and teams: Leads the Computer Vision and Geometry (CVG) lab at ETH Zurich and directs the Microsoft Mixed Reality and AI Lab. His work bridges academia and industry, focusing on perception for mixed reality and autonomous systems.

Wilfried KubingerView profile

Professor

• Embedded Systems
• Machine Vision
• Autonomous Robotics
• +3 more

Prof. Wilfried Kubinger serves as the Head of the Department of Electronic Engineering at the University of Applied Sciences Technikum Wien, Austria. He holds a PhD in Technical Sciences from the Technical University of Vienna (1999) and has extensive experience in research and industry. His academic roles include leading the 'Automation & Sensor Technology' competence field and managing the 'Automation & Robotics' research area. **Research Focus:** His work centers on embedded systems, machine vision, autonomous robotics, and real-time control systems. Notable projects include obstacle detection for autonomous vehicles, stereo vision algorithms, and agricultural robotics applications. He actively contributes to IEEE and OVE engineering associations. **Professional Journey:** Prior to academia, he worked at Siemens Austria (2000–2003) as a software developer and project manager, and at AIT Austrian Institute of Technology (2003–2010) managing research projects in autonomous systems. He participated in DARPA Grand Challenge/Urban Challenge as a principal scientist for vision-based obstacle detection. **Publications:** His research spans embedded vision systems, FPGA implementations, and autonomous vehicle technologies. Recent work emphasizes agricultural robotics and Industry 4.0 applications. He also leads R&D project acquisition and implementation for Technikum Wien.

• Electrical Engineering
• Computer Science
• Information Technology
• +3 more

Bernhard Rinner is a Professor at the Institute of Networked and Embedded Systems within the Faculty of Technical Sciences at Alpen-Adria-Universität Klagenfurt. He is a member of the university Senate and serves as Prodekan (Deputy Dean). His research spans robotics, embedded systems, sensor networks, and privacy-aware computing , with a focus on resource-efficient designs for drones, autonomous systems, and IoT applications. Email: Bernhard.Rinner@aau.at Phone: +43 463 2700 3671 Office: B02.1.62, Lakesidepark Haus B02, Klagenfurt, Austria Research Interests include: Self-aware autonomous systems for adaptive mission planning and anomaly detection Resource-efficient embedded AI for drones and camera networks Secure IoT applications with privacy-preserving visual data processing Multi-agent coordination in confined environments and 3D navigation Dynamic sensor calibration for low-cost wireless networks Publications reflect trends in drone networks, self-aware computing, and privacy-aware sensing , emphasizing lightweight architectures, distributed coordination, and energy-efficient designs. Many recent works focus on binary neural networks, decentralized re-identification, and adaptive sensor reconfiguration .

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +5 more

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

• Human-Robot Interaction
• Assistive Robotics
• Machine Learning in Robotics
• +5 more

Prof. Dongheui Lee is a Full Professor at TU Wien's Institute of Computer Technology, Faculty of Electrical Engineering and Information Technology, and leads the Human-centered Assistive Robotics Group at the German Aerospace Center (DLR). She holds a PhD from the University of Tokyo (2007) and has held academic roles at Technical University of Munich (TUM), the University of Tokyo, and KIST. Her research focuses on human-robot interaction, assistive robotics, and machine learning applications in robotics. Education: PhD, Information Science and Technology, University of Tokyo, 2007 MS, Kyung Hee University, 2003 Research Interests: Her work spans human motion understanding, assistive robotics, human-robot collaboration, and control systems. Key areas include robotic balance assistance, motion imitation, and safety-aware robotics. She has pioneered methods for light touch support in human-robot interaction and developed frameworks for dynamic task execution. Recent Trends in Publications: Recent work emphasizes variable stiffness control, motion retargeting, and multimodal anomaly detection. Publications highlight advancements in assistive robotics, human motion prediction, and reinforcement learning for locomotion. Articles often integrate robotics with machine learning to improve safety and adaptability in human-robot systems. Awards: Carl von Linde Fellowship (TUM Institute for Advanced Study, 2011) Helmholtz professorship prize (2015) Best Intelligence Paper Award (2024) Projects & Grants: Leads projects like LunarAssembly (robotic assembly on the Moon) and INVERSE (interactive robots through reasoning). Funded initiatives include EU Horizon, BMBF, and industry collaborations. Coordinates teams for projects like PERSEO (service-oriented robotics) and SOLAR (body representation studies). Labs/Teams: Directs the Human-centered Assistive Robotics Group at DLR and collaborates with TU Wien's Autonomous Systems unit. Her teams focus on real-world applications in healthcare, manufacturing, and human-centered robotics.

• Archaeological prospection
• Geophysics
• Ground-penetrating radar
• +6 more

Immo Trinks is Associate Professor (Privatdozent) at the University of Vienna and Head of the Vienna Institute for Archaeological Science . He coordinates a comprehensive teaching programme in archaeological prospection, archaeometry and scientific documentation, and leads large-scale geophysical projects across Europe. Education & qualifications: Dipl.-Geophys. (Diploma in Geophysics) PhD (awarded) Habilitation (Privatdozent, Austrian post-doctoral lecturing qualification) Research interests revolve around non-destructive mapping of buried archaeological landscapes . He specialises in ground-penetrating radar , magnetometry and multi-sensor robotic platforms , developing workflows that fuse high-resolution geophysical data with 3D archaeological interpretation. Coastal wetlands, Roman towns, Viking harbours and prehistoric pile dwellings serve as key study areas. Recent publications demonstrate a clear trend toward automated, large-coverage surveys and multi-method data fusion . Papers in 2022-2025 report motorised GPR arrays on snow-covered fields, semi-autonomous driverless systems, and integrated interpretation frameworks that combine magnetics, GPR and remote sensing to reveal Roman military compounds, Etruscan cities, Swedish war camps and Neolithic monumental landscapes. Scientific awards & honours: No specific awards are listed in the supplied text; however, his continuous funding and invited keynote contributions (e.g., ArchaeoProspection conference series) indicate international recognition. Teaching & student supervision: Regular courses: Introduction to Archaeological Prospection, Archaeometry, Magnetic Prospection, GPR Practical, Underwater Prospection, Human Evolution & Archaeological Science, LaTeX for Archaeologists Excursions: 2-day field schools at Alpine pile-dwelling sites and around Vienna Thesis seminars: continuous Master Thesis Topic Search & Exposé and final thesis colloquia every semester Labs & facilities: Trinks heads the Vienna Institute for Archaeological Science (VIAS), operating several motorized multi-channel GPR systems, high-resolution magnetometer arrays, electromagnetic induction sensors, underwater sonar and multi-spectral cameras. The institute maintains dedicated GIS/visualisation labs and field vehicles for pan-European surveys within the international ArchPro and Stonehenge Hidden Landscapes initiatives.

• Control Engineering
• Automation
• Model Predictive Control
• +5 more

Martin Steinberger is an Associate Professor at the Institute of Control and Automation (IRT) at Graz University of Technology (TU Graz). His work focuses on advanced control systems, networked control, model predictive control (MPC), and automation in manufacturing and autonomous systems. He leads research into real-time optimization, fault diagnosis, and safety-critical applications in industries like pharmaceuticals and automotive. Research interests include: Networked Control Systems Model-Based Control Autonomous Vehicle Trajectory Planning Process Automation Robotics and Industrial Automation His work bridges theoretical control engineering with practical applications in manufacturing lines, chemical processes, and autonomous driving. Recent studies emphasize digital real-time release testing for pharmaceuticals, universal control concepts for manufacturing systems, and safety-aware trajectory optimization for automated vehicles. His publications frequently address challenges in time-varying delays, packet loss mitigation, and robust observer design for nonlinear systems. Steinberger collaborates on EU-funded projects and regularly contributes to conferences like the International Workshop on Variable Structure Systems. His research often involves experimental validation, as seen in work with compact pharmaceutical manufacturing setups and small-scale autonomous vehicle testing platforms.

• Robotics
• Autonomous Systems
• Mobile Robotics
• +6 more

Markus Bader is a PostDoc Researcher at the Technische Universität Wien's Faculty of Informatics, Department of Automation Systems. He holds roles as Curriculum Coordinator for Master's programs in Automation Systems and Mobile Robotics, and serves on multiple academic committees including the Faculty Council and Curriculum Commissions for Informatics and Computer Engineering. He earned his Diplom-Ingenieur (2006) and Doctor Technica (Dr.techn.) from TU Wien. His research focuses on autonomous systems, mobile robotics, and control systems, with notable work on multi-robot coordination, path planning algorithms, and real-time navigation in human environments. Key projects include TransportBuddy (2018), exploring navigation in human spaces, and the Formula Student Driverless race car design (2017). Bader has led or contributed to funded projects such as the Austrian Research Promotion Agency (FFG)-supported Green Facade Digital Twin (2025–2027), Independent Wheel Offset Steering (2016–2017), and MPCv1 (2015–2017), emphasizing model predictive control and sensor integration. Research Highlights : Prioritized multi-robot route planning (MRRP), human motion prediction for autonomous navigation, and sensor fusion for vehicle localization. Grants : FFG-funded projects totaling over €2.5M, including autonomous vehicle coordination and mobile robotics tool development. He advises students on topics like ROS2-based route planning and independent steering systems, with 7+ supervised theses documented. Bader's work bridges theoretical robotics research with practical applications in industrial automation and autonomous vehicle systems.

• Agronomy
• Sustainable Agriculture
• Crop Production
• +5 more

Reinhard Neugschwandtner is an Associate Professor at the Institute of Agronomy , University of Natural Resources and Life Sciences, Vienna . His research focuses on sustainable crop production, soil science, and precision agriculture in Pannonian climates. Key projects: Agri-photovoltaic systems, digitalization in agriculture, drought-tolerant legumes, autonomous robotics in crop technology Leadership roles: Project leader in 8+ research initiatives since 2014 Research Interests : Dr. Neugschwandtner specializes in: Soil health and earthworm ecology under different tillage systems Nitrogen dynamics in legume-cereal intercropping systems Digital tools for canopy parameter estimation and precision farming Life cycle assessment of agricultural practices Climate-smart crop management strategies Scientific Awards : Awardee of five prestigious honors including: Kardinal-Innitzer-Förderungspreis (2017) Klaus Fischer Innovationspreis für Technik und Umwelt (2016) Walter-Kubiena-Preis (2008) BISi Award (2003) Publications & Presentations : Over 171 publications and 102 presentations focusing on: Long-term tillage experiments Winter crop adaptation Intercropping efficiency Digital agriculture tools Soil nutrient dynamics