Pedro Ribeiro is a Lecturer at the Department of Computer Science , University of York. His research focuses on formal specification and verification of cyber-physical and autonomous systems, particularly using model-based approaches with domain-specific languages like RoboChart. His work addresses heterogeneous formal semantics to capture complex system behaviors, including timing, concurrency, and continuous dynamics. Recent publications highlight verification of robotic autonomous systems, safety assurance frameworks, and algebraic models for concurrency. Quantum computation Formal verification Concurrency theory Model-driven engineering He contributes to the development of tools like RoboTool and RoboStar, enabling combined proof, simulation, and testing for robotics applications.
Chris Rogers is a Professor of Mechanical Engineering at Tufts University School of Engineering and a Professor of Education at Tufts University School of Arts and Sciences. He serves as the John R. Beaver Professor and a CEEO Fellow , focusing on Engineering Education , Human-Robot Interaction , and Educational Robotics . His work bridges Mechanical Engineering with Artificial Intelligence and Music Engineering . Education: PhD in Mechanical Engineering, Stanford University (1989) MS in Mechanical Engineering, Stanford University (1985) BSME, Stanford University (1984) Research Interests center on STEM Engagement , Educational Technology Development , and Teacher Professional Development . He designs tools like Smart Motors and LEGO-based platforms to enhance K-12 engineering education. His work explores Augmented Reality , Reinforcement Learning , and Accessible Makerspaces . Recent Publications demonstrate trends in Robotics Education , AI Integration , and Collaborative Learning Frameworks . Key themes include Low-Cost Hardware , K-12 AI Literacy , and Visual Instructional Design . Scientific Awards Tufts ASME 'Best Professor Award' Harry C. Bigglestone Award National Science Foundation Director’s Distinguished Teaching Scholar Award LabVIEW Programming Prize Robert Knapp Award for Best Paper (ASME) Grants and Projects include collaborations with LEGO Foundation , National Science Foundation , and Parametric Technology Corporation . His initiatives span Preparation for Indonesian STEM Education , Autism Therapy Robotics , and Novel Engineering-Literacy Integration . Labs & Outreach: As founding member of the Tufts Center for Engineering Education and Outreach (CEEO) , he develops global teacher training programs and tools like LEGOEngineering.com , impacting thousands of educators annually.
Ayham Alharbat is a Contract PhD Candidate in Robotics and Mechatronics and a Researcher at Saxion University of Applied Sciences since November 2021. His academic work focuses on aerial robotics systems, particularly unmanned aerial vehicles (UAVs) with physical interaction capabilities. His research spans Robotics , Unmanned Aerial Vehicles , and Aerial Manipulation , emphasizing control systems, machine learning integration, and open-source software frameworks. Key contributions include nonlinear model predictive control for physical interaction, learned-model-based external wrench estimation, and reinforcement learning for disturbance rejection in flight systems. Recent publications (2024-2025) demonstrate a trend toward practical implementation, highlighted by the open-source Sarax framework for aerial manipulators and data-driven approaches to force estimation. His 2022 foundational work established core paradigms for UAV physical interaction, evolving into current AI-enhanced control strategies addressing real-world challenges like environmental disturbances.
Ángel Llamazares Llamazares is a Researcher at the Department of Electronics, University of Alcalá. His work focuses on robotics, autonomous systems, and sensor fusion technologies. He is affiliated with the Robesafe (Service Robotics and e-Safety) and former INVETT (Intelligent Vehicles and Traffic Technologies) research groups. He earned his PhD in 2017 with the thesis Laser-based detection and tracking of moving obstacles to improve perception of unmanned ground vehicles , supervised by Dr. Manuel Ocaña. His research spans topics like SLAM algorithms, obstacle detection, autonomous vehicle perception, and human-robot interaction. Key research areas include autonomous driving systems, multimodal sensor fusion (LiDAR/camera/RADAR), driver activity recognition, and robotics education through competitions. He has explored applications in traffic management, elderly care via voice assistants, and explainable AI for decision-making. Notable contributions include game-theoretic models for electric vehicle charging, modular autonomous driving software architectures, and fusion techniques for HD map validation. His work bridges theoretical advancements with real-world applications in safety-critical systems. Awards: None explicitly listed. Grants: Focus on robotics competitions and university-driven projects. Labs/Teams: Active in Robesafe and INVETT groups, collaborating on robotics competitions like Eurobot Spain to foster skill development among students.
Martin Schörner is a Researcher at the Institute for Software & Systems Engineering within the Faculty of Applied Computer Science at the University of Augsburg, Germany, where he has been affiliated with the Chair of Software Engineering since 2019. His academic qualifications include: Master in Computer Science and Engineering (University of Augsburg, 2017-2019) Bachelor in Computer Science and Engineering (University of Augsburg, 2013-2017) His research centers on Software-driven Robotics and Automation , specializing in UAV-based inspection systems, distributed robot ensembles, and reconfigurable hardware architectures. His work bridges theoretical software engineering with practical robotics applications, focusing on autonomous navigation, emergency control systems, and semantic integration for industrial automation. Analysis of his 2020-2023 publications reveals a concentrated research trajectory in UAV inspection methodologies, featuring advancements in offline/online path planning, ROS 2 interfaces, and plug-and-play systems. Key thematic clusters include real-time route optimization for large-component inspection, emergency control architectures for UAV swarms, and semantic frameworks for hardware reconfiguration. No formal scientific awards or fellowships are documented in the available sources. While no student advisement records or specific grant funding details are publicly listed, his collaborative work with Prof. Wolfgang Reif and colleagues indicates active participation in institutional research initiatives. Current projects emphasize industrial automation through UAV ensembles and reconfigurable systems. He operates within the Software Engineering team at the Institute for Software & Systems Engineering (ISSE), which conducts fundamental and applied research under Prof. Reif's direction. The institute focuses on robotics software engineering, with applications in industrial inspection and autonomous systems development.
Harold TRANNOIS serves as a Lecturer at the University of Picardy Jules Verne (UPJV) within the Networks and Data Domain (REDO), with office 309 contactable at phone 5911. His academic focus bridges theoretical computer science and applied engineering disciplines. His research program demonstrates deep specialization in intelligent systems, particularly emphasizing: Machine Learning architectures including spiking neural networks and autoencoders Multi-agent coordination frameworks for complex environments Anomaly detection systems in IoT-enabled infrastructure Cloud robotics and domain-specific language development Discrete element modeling for physical simulations Analysis of his publication trajectory reveals evolving expertise from foundational multi-agent traffic simulation (1998) toward contemporary biomedical AI applications (2023). Recent work concentrates on neural network implementations for healthcare monitoring and smart building analytics, with consistent methodological focus on uncertainty quantification and architectural optimization. Active involvement in DAAB and SmartAngel projects demonstrates applied research translation into healthcare and robotics domains, leveraging his dual expertise in neural networks and distributed systems for real-world problem solving.
Dr. Aradi Szilárd serves as Associate Professor at Budapest University of Technology and Economics, Faculty of Transportation Engineering, Department of Control for Transportation and Vehicle Systems. He also holds a Senior Research Fellow position at SZTAKI since 2022. His academic career shows steady progression from PhD Student (2005-2009) to Assistant Lecturer (2009-2016), Senior Lecturer (2016-2021), and finally Associate Professor (2021-present). His educational background includes an MSc in Transportation Engineering (2005) and PhD (2015). His research spans vehicle mechatronics, embedded control systems, reinforcement learning applications in transportation, and railway traffic management. Dr. Aradi has led significant projects including TruckDAS (2009-2011), Integrated Railway Energy System (2013-2015), Bosch R&D Project 'Umbrella' (2014-2016), VKE 2018-40 (2018-2022), and the Autonomous Systems National Laboratory (2020-present). His recent publication trend reveals a strategic shift toward reinforcement learning applications in transportation systems, with particular emphasis on rail traffic optimization and autonomous vehicle control. Since 2020, over 70% of his work involves reinforcement learning techniques applied to real-world transportation challenges, demonstrating his commitment to bridging theoretical AI with practical engineering solutions. Dr. Aradi teaches Vehicle On-board Systems I-II, On-board Communication, Automotive Environmental Sensing, and I&C Technologies courses while supervising PhD students. His industrial collaborations with Bosch, Siemens, and other transportation technology companies provide valuable real-world context for his academic work. His research laboratory focuses on vehicle mechatronics, with specialized facilities for railway traffic control, automotive environmental sensing, and reinforcement learning applications. The department maintains strong connections with industry partners including HungaroControl, Műszer Automatika, PowerQuattro, SWARCO Traffic Hungária, Robert Bosch, and SIEMENS.
Christopher Timperley is a Senior Systems Scientist in the Software and Societal Systems Department at Carnegie Mellon University, with additional appointments in the Robotics Institute and National Robotics Engineering Center within the School of Computer Science. His work is primarily conducted in the context of squaresLab. Timperley's research focuses on the intersection of software engineering, robotics, and program analysis, with particular emphasis on developing techniques for automatically detecting, locating, and repairing faults in large-scale software systems. His recent work has applied these techniques specifically to robotics systems, particularly autonomous vehicles. His research interests span search-based software engineering and testing, with applications in security and privacy, embedded systems security, formal methods for security, intrusion and anomaly detection, and security of AI and ML systems. His recent publications demonstrate a strong focus on robotics software systems, particularly those built using ROS (Robot Operating System). His work addresses critical challenges in robotics software development including architecture misconfigurations, behavioral modeling, test automation through simulation, and empirical studies of the robotics ecosystem. The publications reveal a consistent theme of applying software engineering principles to improve the reliability and security of robotics systems. Timperley received his Ph.D. in computer science from the University of York, UK, with a dissertation on search-based program repair under the supervision of Professor Susan Stepney. His work bridges theoretical advances in software engineering with practical applications in robotics, contributing significantly to the development of more robust and reliable robotics software systems.
Pangcheng David Cen Cheng is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) , affiliated with the College of Computer, Film, and Mechatronics Engineering at Politecnico di Torino. He actively contributes to teaching roles in Robotics, Mechatronic Engineering, and Computer Engineering programs. Research Interests Automation and Robotics Autonomous Robotic Systems Control Systems His publications focus on low-resource robotics, safe human-robot interaction, and path planning in dynamic environments. Key venues include IEEE IECON, IEEE ETFA, and IEEE ISIE conferences. He supervises PhD students Cesare Luigi Blengini and Rosario Francesco Cavelli , both enrolled in Electrical, Electronic, and Communications Engineering (40th cycle, 2024–present).
Joni-Kristian Kämäräinen serves as Professor of Signal Processing within the Computing Sciences department at Tampere University, where he leads research in the Vision Group. Previously, he held faculty positions at LUT University's School of Engineering Science for five years before joining Tampere University in 2012 (tenured 2017, promoted to full professor in 2020). His academic journey includes a postdoctoral fellowship at the University of Surrey's Center of Vision, Speech and Signal Processing under Josef Kittler. His research centers on robot vision and robot learning , with significant contributions to computer vision and machine learning. Key focus areas include visual place recognition, RGB-D tracking, color constancy, and anthropometric measurements. His group maintains strong industry collaborations with Huawei, Nokia Technologies, and Business Finland-funded projects. His publication portfolio shows a clear trajectory toward real-world robotic applications, with recent work emphasizing visual place recognition under varying conditions (2022-2024), depth-aware video processing (2023-2024), and reinforcement learning for industrial manipulators (2023-2025). The 2023 textbook Koneoppimisen perusteet (Machine Learning Fundamentals) demonstrates his commitment to education. Expert Statement for Finnish Parliament (2022) on AI solutions Contributor to Finnish Roadmap: Robots and the Future of Welfare Services (2021) Featured in YLE Uutiset (2018), Aamulehti (2021), and multiple technical press outlets He has supervised 20 PhD students since 2007, including Vivienne Huiling Wang (2025), Samu Koskinen (2025), and Fatemeh Shokollahi Yancheshmeh (2024), with alumni placed at Aalto University, Ericsson AB, and Huawei. His group receives funding from the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies. The Vision Group operates from Tampere University's Hervanta Campus, maintaining close ties with industrial partners through applied research projects.
Lu Gan is an Assistant Professor at the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, where she joined in January 2024. She leads the Lu's Navigation and Autonomous Robotics (Lunar) Lab and serves as core faculty for the Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech. Dr. Gan earned her educational degrees from prestigious institutions: B.S. in Automation from the University of Electronic Science and Technology of China (2013), M.S. in Control Engineering from Beihang University (2016), and both M.S. and Ph.D. in Robotics from the University of Michigan, Ann Arbor (2021, 2022). Prior to her appointment at Georgia Tech, she completed a two-year postdoctoral fellowship at the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT) and the Center for Autonomous Systems and Technologies at Caltech. Her research focuses on robot perception, robot learning, and autonomous navigation, exploring the integration of computer vision, machine learning, estimation, probabilistic inference, kinematics, and dynamics to develop autonomous systems for ground, air, and space applications. Her work addresses challenges in highly unstructured environments under harsh sensing conditions for tasks including search and rescue, daily housework, and scientific exploration. Dr. Gan's recent publications demonstrate strong trends in applying advanced machine learning techniques, particularly graph neural networks and multi-modal fusion approaches, to robotics challenges. Her work spans multiple subfields including legged locomotion, semantic scene understanding, contact perception, and thermal imaging applications for robotic systems operating in challenging environments. DAAD AInet Fellowship (2023) Rising Star in Data Science, University of Chicago (2022) National Graduate Scholarship of China (2015) IROS Best Paper Award on Agri-Robotics (Finalist, 2023) Dr. Gan actively mentors students in the Lunar Lab, with current advisees working on cutting-edge robotics research. She serves as an Associate Editor for IEEE Transactions on Robotics (T-RO) and IEEE Robotics and Automation Letters (RA-L), and is Associate Co-Chair of the IEEE RAS Technical Committee for Computer & Robot Vision. Her lab has secured various research grants supporting their work in robotics and autonomous systems. The Lunar Lab maintains strong collaborations with leading institutions and researchers in the robotics community, hosting seminars and participating actively in major conferences like ICRA. The lab's research directions include physics-informed learning for robotics, multi-modal 3D scene understanding, and heterogeneous multi-robot active SLAM.
Rui P. Rocha is an Associate Professor at the University of Coimbra , specializing in Robotics and Multi-robot Systems . With over 15 years of active research, his work spans autonomous navigation , swarm intelligence , and human-robot collaboration , particularly in precision forestry and active aging applications. University of Coimbra (Current) ISR-UC (Institute of Systems and Robotics - University of Coimbra) Ingeniarius Lda. (Collaborative Partner) His research integrates AI with ROS (Robot Operating System) for cooperative perception and multi-robot coordination . Recent projects include EuroAGE+ for elderly quality-of-life improvement and SEMFIRE for forestry maintenance using multi-robot systems. Key contributions include comparative analyses of 2D/3D SLAM techniques, fractional-order PSO algorithms, and Bayesian learning frameworks for scalable patrolling missions. His work balances theoretical advancements with practical implementations in environmental and healthcare robotics .
Zhishan Guo is an Associate Professor in the Department of Computer Science at North Carolina State University, affiliated with the College of Engineering and the Operations Research Graduate Program. His research bridges real-time scheduling theory, machine learning theory, and cyber-physical systems. Ph.D., Computer Science, UNC-Chapel Hill (2016) M.Phil., Mechanical and Automation Engineering, The Chinese University of Hong Kong (2011) B.E., Computer Science and Technology, Tsinghua University (2009) Guo's work spans embedded and real-time systems, parallel/distributed systems, and healthcare information technology, with a focus on integrating machine learning into real-time scheduling and cyber-physical systems. Recent publications highlight advancements in neural network-based scheduling, adversarial defense mechanisms, and healthcare diagnostics for embedded systems. His scientific honors include the ACM SIGBED CAREER Award (2023), Best Paper Awards at RTSS (2023), ICIST (2023), and RTAS (2025), alongside the Humboldt Fellowship (2024). Guo's research is supported by grants from the National Science Foundation and NHK International Corporation, addressing scalable heterogeneous computing and intelligent robotics.
Osman Ervan is a Research Assistant at the Department of Control and Automation Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University. He has been actively contributing to academic research since 2017, with expertise in robotics, autonomous systems, and control theory. His research focuses on robotics and sensor data processing, particularly involving LiDAR systems, point cloud registration, and tensor voting methods. Recent projects include advanced sampling techniques for point cloud data, autonomous robotic imaging systems, and fuzzy logic-based obstacle avoidance algorithms. Key publication trends show a strong emphasis on 3D point cloud processing (2019-2023), sensor configuration optimization (2015-2016), and autonomous navigation systems (2021-2023). His work spans both theoretical and applied aspects of robotics. Dr. Ervan is based at ITU Ayazaga Campus, Istanbul, Turkey. He has published 8 research outputs since 2015, with active collaborations in multi-robot systems development and sensor integration research.
Maya Cakmak is a Professor at the University of Washington's Paul G. Allen School of Computer Science & Engineering, where she directs the Human-Centered Robotics Lab and participates in the Robotics Group and UW+Amazon Science Hub. Her work bridges artificial intelligence, human-centered computing, and physical interaction systems. Her academic foundation includes: B.Sc. in Electrical & Electronics Engineering from Middle East Technical University, Turkey M.Sc. in Computer Engineering from Middle East Technical University, Turkey Ph.D. in Robotics from Georgia Institute of Technology (2012) Postdoctoral research at Willow Garage, Inc. with Leila Takayama Cakmak's research centers on democratizing robot programming for diverse users through human-robot interaction, end-user programming, and assistive robotics. She specifically targets accessibility challenges in home environments, developing systems where robots accommodate unique user needs and preferences without requiring technical expertise. Her expertise spans human-centered AI, accessibility engineering, and physical assistive technologies. Recent publications (2023-2024) reveal a concentrated focus on accessible teleoperation interfaces and intuitive robot control mechanisms, particularly for domestic assistive applications. Key themes include customizable web-based control systems, novel input devices for manipulation tasks, and social dining assistance, demonstrating a clear trajectory toward practical in-home robot deployment. Her scientific recognition includes: Best Short Contribution award at HRI 2024 for robot manipulator control research Best Video award at HRI 2024 for in-home robot deployment documentation HRI 2023 Best Design Paper award for assistive feeding systems As Principal Investigator of NSF-funded AccessComputing (since January 2024), Cakmak leads nationwide efforts to broaden participation of people with disabilities in computing fields. She mentors students through undergraduate research courses (CSE 390R) and lab supervision, while her RO-MAN 2024 keynote 'Towards Physically Assistive Robots in the Home' outlines her vision for future home-care robotics. Her lab actively collaborates with industry partners through the UW+Amazon Science Hub to advance real-world assistive technologies. The Human-Centered Robotics Lab operates within the Allen School's Robotics Group, maintaining strong ties to the Socially Intelligent Machines Lab (Georgia Tech) and Kovan Lab (Middle East Technical University) where her research journey began under Andrea Thomaz and Erol Sahin respectively.
