Rafael Barea Navarro is a Professor in the Department of Electronics Technology at the University of Alcalá. His research focuses on biomedical engineering, autonomous systems, robotics, artificial intelligence, and driver safety. He leads the Biomedical Engineering Research Group (GIB) and the Robótica de Servicios y Tecnologías para la Seguridad Vial (Robesafe) group. His work bridges AI applications in medical diagnostics (e.g., multiple sclerosis via OCT) and advanced autonomous vehicle technologies, including motion prediction, reinforcement learning, and real-time safety systems. Education: PhD in Electronics from the University of Alcalá (2001), with a thesis on EOG-based human-computer interfaces for mobility assistance. His research spans over 50 peer-reviewed articles since 2000, emphasizing practical applications in healthcare and robotics. Key research themes include: 1) AI-driven medical diagnostics; 2) autonomous vehicle control systems; 3) sensor fusion for navigation; and 4) driver attention monitoring. His work integrates robotics, computer vision, and biomedical signal processing. Publications emphasize interdisciplinary approaches, with recent work combining OCT and explainable AI for early disease diagnosis, and hybrid reinforcement learning frameworks for urban autonomous driving.
Dr. Daniel Pizarro Pérez is a Professor in the Department of Electronics at the University of Alcalá, Spain. He is a member of the GEINTRA research group, which focuses on Electronic Engineering applied to Intelligent Spaces and Transport. He holds a doctoral degree from the University of Alcalá, completing his thesis on 'Localización de robots móviles en espacios inteligentes utilizando cámaras externas y marcas naturales' (2008), supervised by Dr. Manuel Ramón Mazo Quintas and Dr. Enrique Santiso Gómez. His research interests span computer vision, medical imaging, smart grid technologies, acoustic signal processing, and control systems. Notable projects include augmented reality applications in laparoscopic surgery, non-intrusive load monitoring using smart meters, and advanced control methodologies for power electronics. His work bridges theoretical advancements with practical applications in healthcare, energy systems, and robotics. Recent publications highlight contributions to neural radiance fields for minimally-invasive surgery, distributed acoustic sensing in submarine environments, and deep learning-based activity recognition via energy consumption. His research frequently integrates interdisciplinary approaches, such as combining computer vision with medical robotics and leveraging machine learning for real-time control systems. Dr. Pizarro’s work has been supported by grants and collaborations within the GEINTRA group, with applications in intelligent spaces, robotic navigation, and sensor fusion. He has also contributed to educational initiatives like the GEMS Erasmus+ project, emphasizing sensory module development for robotics education.
Hulya Yalcin is an Assistant Professor in the Department of Mechanical Engineering at Istanbul Technical University. Her research focuses on artificial intelligence applications in robotics, computer vision, and precision agriculture. She leads projects in musculoskeletal modeling, plant phenology monitoring, and assistive technologies for elderly care. Her work contributes to UN Sustainable Development Goals related to innovation, health, and sustainable agriculture. Key projects include using deep learning for crop yield estimation and developing exergaming systems to improve elderly health. She has authored 52 research outputs and secured funding for initiatives like AISENSE (EU-funded exergames) and plant classification via computer vision. Publications span robotics control, medical engineering, and agricultural informatics. Notable contributions include knee orthosis gait learning via deep reinforcement learning and low-resource Turkish speech recognition improvements. As Principal Investigator, she manages projects on plant classification using CNNs, drone-based depth mapping, and multimodal assisted living systems. Her research bridges AI with practical applications in healthcare, agriculture, and robotics.
Dr. Ben Mitchell is a Visiting Assistant Professor in the Department of Computer Science at Swarthmore College. He teaches courses in Artificial Intelligence, Machine Learning, and Computer Science fundamentals. His research focuses on AI, machine learning, and deep learning, with recent work emphasizing medical imaging applications and ethical implications of AI. He has been recognized with a Best Paper Award at FLAIRS 2019. Mitchell's teaching spans introductory programming to advanced topics like neural networks and computer ethics. His work also extends to robotics and medical engineering, as seen in collaborations on surgical manipulators and diagnostic models. Education: Not explicitly stated in provided texts. Key Roles: Course instructor for CS63/CS66, advisor on AI projects. Grants/Awards: Best Paper Award (2019). Research interests include the intersection of AI/ML with real-world applications like healthcare, while addressing ethical challenges in data usage. His labs focus on practical implementations of search algorithms, neural networks, and reinforcement learning.
Kostas Stathis is a Professor of Artificial Intelligence at the Department of Computer Science, Royal Holloway, University of London. He serves as Vice-Dean for Research and Knowledge Exchange in the School of Engineering, Physical, and Mathematical Sciences, and is the Interim Director of the Centre for AI and Skills. His research focuses on autonomous systems, cognitive agent models, game-theoretic strategies, and multi-agent environments, with applications in e-markets, regulation, and legal reasoning. Education details are not explicitly provided in the text. His research interests include knowledge representation, strategic interaction models, and programmable agent societies. He leads the DICE Lab, exploring AI-driven solutions for complex systems. Recent projects include iRealHAT-PEGA (human-agent teaming via eye gaze analysis) and collaborations on AI in regulatory decision-making for healthcare professions. He has supervised 9 research students and contributed to over 132 publications, including work on reinforcement learning in negotiation and LLM-enhanced legal reasoning frameworks. His work aligns with UN Sustainable Development Goals, particularly those addressing innovation and industry (Goal 9) and reducing inequalities through technological access (Goal 10).
Dean F. Hougen is the Lloyd and Joyce Austin Presidential Professor and Director of the School of Computer Science at the University of Oklahoma, within the Gallogly College of Engineering. He also holds affiliations with the School of Electrical and Computer Engineering, Data Science and Analytics Institute, and has served as Interim Director and Associate Director of the School of Computer Science. His research focuses on artificial intelligence, robotics, machine learning, and distributed systems, conducted in labs like the Robotic Intelligence and Machine Learning Laboratory and the Artificial Intelligence Research (AIR) SuperLab. Key areas include evolutionary computation, autonomous systems, and applied AI in healthcare and transportation. He has led over $24M in grants, including projects on intelligent aerospace systems, pandemic monitoring, and robotics for infrastructure safety. Notable awards include the Presidential Professorship, multiple best paper awards, and recognition for teaching excellence. Hougen has advised numerous students and contributed to interdisciplinary initiatives, including the CS INCLUDES program supporting Indigenous learners. His work spans academic leadership, industry collaborations, and advancing AI applications across domains.
Dr. Abolfazl Zaraki is a Senior Lecturer in AI and Robotics at the University of Hertfordshire's Department of Computer Science, part of the School of Physics, Engineering & Computer Science. He leads the Robotics Research Group and previously held roles at Cardiff University's School of Engineering and the IROHMS Research Centre. His academic journey includes a Master's in Mechatronics from University Technology Malaysia (2010) and a PhD in Automatic Robotic and Bioengineering from the University of Pisa (2014). He has held Research Fellow positions in Italy and the UK until 2019. Dr. Zaraki's research focuses on AI-driven autonomous systems, social robotics, and assistive technologies. Key projects include the EASEL, BabyRobot, and JAMES EU initiatives, alongside the Innovate UK-funded InSight project. His work emphasizes Human-Robot Interaction (HRI), trusted autonomy, and applications in healthcare and industrial contexts. Notable contributions include the development of the Kaspar humanoid robot for autism therapy and advancements in reinforcement learning for robotic control. His recent publications (2021–2025) explore agentic AI, memory-driven systems, and personalized LLMs for HRI, alongside technical advancements in robotic control, communication systems, and bio-inspired robotics. His research bridges theoretical AI innovation with practical applications in healthcare, education, and industrial automation. Zaraki has collaborated internationally across institutions and industry partners, contributing to 35+ research outputs. His work aligns with global trends in ethical AI, explainable systems, and human-centric robotics design.
Prof. Dr. Matthias Althoff is an Associate Professor of Cyber-Physical Systems at the Technical University of Munich (TUM), leading the Chair of Cyber-Physical Systems within the TUM School of Computation, Information and Technology. His research focuses on formal safety verification, model-based design, and reachability analysis for systems such as autonomous vehicles, robotics, and power grids. Education: He earned his diploma in Mechatronics and Information Technology (2005) and PhD (2010, summa cum laude) from TUM. He held postdoctoral positions at Carnegie Mellon University (2010–2012) and served as a junior professor at TU Ilmenau (2012–2013) before joining TUM as a full professor in 2013, becoming an associate professor in 2019. Research Interests: His work spans cyber-physical systems, formal methods for safety assurance, autonomous vehicles, modular robotics, and smart grid control. He develops tools like CommonRoad and CORA for scenario-based testing and reachability analysis. Awards: He has received the IEEE/ACM William J. McCalla ICCAD Best Paper Award (2012) and the Best Poster Award at the IEEE Intelligent Vehicles Symposium (2009). Labs/Projects: Leads the Cyber-Physical Systems group, collaborating on projects such as the Scenario Factory for automated vehicle testing and CommonPower for safe smart grid control. He also co-founded startups RobCo and aiina .
Ramin Hasani is a researcher at TU Wien's Cyber-Physical Systems department. He holds a Dr.techn. (Doctor of Engineering) and specializes in machine learning applications for robotics, control systems, and biologically-inspired neural networks. His work focuses on developing interpretable neural architectures like Liquid Time-Constant Networks and Neural Circuit Policies, emphasizing safety and stability in autonomous systems. Hasani's research bridges neural network theory with practical robotics challenges, including autonomous racing, medical data analysis, and adversarial robustness. Key research areas include continuous-time neural networks, formal verification of neural ODEs, and bio-inspired control mechanisms derived from biological neural circuits (e.g., Caenorhabditis elegans). He collaborates extensively with institutions like MIT and ETH Zurich, contributing to projects in health-monitoring systems and end-to-end robot learning frameworks. His publications consistently address real-world challenges such as sepsis prediction via reinforcement learning and robust CNN architectures for image classification. Recent work highlights include developing stable recurrent networks through Gershgorin loss functions and advancing zero-shot transfer learning for autonomous systems. Hasani's interdisciplinary approach integrates principles from neuroscience, control theory, and machine learning to create auditable, high-performance AI solutions for cyber-physical environments.
Alberto L. Sangiovanni-Vincentelli holds the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a pioneer in Electronic Design Automation (EDA) and co-founder of Cadence and Synopsys. His research focuses on Cyber-Physical Systems (CPS), embedded systems, hybrid systems, and formal methods for AI. He has authored over 800 papers and 17 books, with major contributions to design automation and methodologies. Education: Dr. Ing., EECS, Politecnico di Milano (1971) Research interests include design methodologies, CPS, and AI integration. He has received numerous awards, including the IEEE James Clerk Maxwell Medal (2008) and ACM/IEEE A. Richard Newton Technical Impact Award (2009). He serves on multiple corporate boards and advisory councils, including the Strategic Committee of the Italian Strategic Fund and the Executive Committee of the Italian Institute of Technology. His work spans academia, industry, and policy, with a focus on innovation ecosystems and CPS design automation. Awards: Over 20 major honors, including Fellowships from IEEE and ACM.
Dr. Avideh Zakhor is a Professor and Qualcomm Chair at the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She is affiliated with several research centers including the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Deep Drive Initiative, Video and Image Processing Lab, and Berkeley Center for New Media (BCNM). Her career spans over three decades with significant contributions to signal processing, 3D computer vision, robotics, and deep learning. 1983 B.Sc. in Electrical Engineering from Caltech 1985 S.M. in Electrical Engineering and Computer Science from MIT 1987 Ph.D. in Electrical Engineering and Computer Science from MIT Dr. Zakhor's research interests focus on 3D computer vision, autonomous systems and robotics, deep learning, and signal/image processing. Her work spans diverse areas including drone navigation, medical imaging analysis, indoor positioning, and building energy audits. She has led projects on drone-based 3D building reconstruction, legged robot locomotion, and melanoma detection using AI. The 15 most recent publications highlight her work in several key areas: person search pre-training techniques, drone-based indoor navigation and 3D modeling, medical image segmentation for melanoma detection, hexapod robot locomotion, and proximity detection for public health. These publications demonstrate her expertise at the intersection of computer vision, robotics, and AI applications. Dr. Zakhor has received numerous prestigious awards throughout her career: 2022 Winner of Phases 1 and 2, Department of Energy E-Robot Competition 2018 Electronic Imaging Scientist of the Year by SPIE 2004 Okawa Research Grant 2002 IEEE Fellow 1992 Office of Naval Research Young Investigator Award 1990 Presidential Young Investigator (PYI) Award from President George H.W. Bush 1990 Junior Faculty Development Award 1984-1988 Hertz Fellowship 1983 Henry Ford Engineering Award 1982-1983 General Motors Scholarship Dr. Zakhor has advised numerous research projects and has been involved in significant research grants. She has founded successful companies including Indoor Reality, which develops technologies for rapid 3D mapping and visualization of buildings and assets. She leads research initiatives in various cutting-edge technologies: Unmanned Aerial Vehicles (UAV) with focus on autonomy and obstacle avoidance, perception, path planning and control Deep learning applications in legged locomotion, unsupervised learning for multimodal sensors, misinformation detection, and learning-based image compression Wi-Fi proximity detection methods for contact tracing of diseases Temporal graphical neural networks for disease prediction Detection of small objects in ultra-high resolution images 3D reconstruction and recognition
Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.
Dr. Andreu Català Mallofré is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automatic and Industrial Informatics Engineering. He serves as the principal investigator at the CETpD - Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma (Technical Research Center for Dependency Care and Autonomous Living) and leads the TOC - Tecnologia Orientada a la Comunitat (Community-Oriented Technology) research group. With an academic career spanning over 30 years, he specializes in assistive technologies, human-computer interaction, and biomedical data analysis. His research interests focus on Wearable health monitoring systems Parkinson's disease management solutions Frailty assessment algorithms Emotion-driven computing Community-embedded technology Gerontechnology applications These areas have produced 367 academic outputs including 59 indexed journal articles, 148 conference papers, and 66 competitive R&D projects. Recent publications demonstrate a clear trend toward multimodal health monitoring combining inertial sensors with biometric data for aging populations . Key application areas include freezing-of-gait detection in Parkinson's patients and distress estimation during assisted mobility. His 2024 work explores agricultural sustainability through data-driven fertilizer optimization. Scientific recognition includes UPC Social Council Award (2013) 20+ years as conference committee member Multiple competitive R&D grants 23391773000 Scopus ID Orcid 0000-0001-8775-1955 As academic advisor, he has mentored 3 PhD candidates including Daniel Manuel Rodriguez-Martin (Human Movement Analysis, 2020) Leonid Ivonin (Physiological Signal Processing, 2014) John Neal Abram Brown (Calm Computing, 2014) His teams operate within Vilanova i la Geltrú's School of Engineering, with strong industry and healthcare sector collaborations.
Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems
Mattias Tiger is an Assistant Professor at the Department of Computer Science (IDA) at Linköping University , where he serves as an AI researcher and deputy lab leader for the Reasoning and Learning Lab (ReaL) . His work is supported by the WASP program and focuses on applied AI research in autonomous systems. Roles : Assistant Professor, AI Researcher, Deputy Lab Leader Affiliations : IDA, AIICS, WASP, AI Academy Research Focus : Artificial Intelligence, Robotics, and Autonomous Systems with specialization in motion planning, dynamic obstacle avoidance, and safety-aware AI. His work bridges theoretical foundations with real-world applications, particularly in urban environments and agile flight systems. Key technical areas: Lattice-based motion planning, deep reinforcement learning, 3D exploration algorithms Application domains: Autonomous road sweeping, drone navigation, retail automation Awards : EurAI award for postdoctoral thesis SAIS award for best degree project supervision Collaborations : Works with Professor Fredrik Heintz and colleagues in projects involving robotic platforms like Spot and Elsa. His research has been highlighted during the Royal Couple's visit to Linköping and through grants from the Norrköping Fund for Research and Development.
