Associate Professor David Rye is an Honorary Associate Professor in the School of Aerospace, Mechanical and Mechatronic Engineering at the University of Sydney, affiliated with the Australian Centre for Field Robotics. His research focuses on interdisciplinary robotics, blending engineering, social sciences, and art to explore human-robot interaction, tactile sensing, and autonomous systems. Key areas include social robotics, cooperative robot behavior, and creative robotics design. His work spans theoretical and applied robotics, including studies on robot collaboration dynamics, tactile feedback systems, and robotic excavation. Notable contributions include developing EIT-based sensitive skins for robots and analyzing human comfort in multi-agent interactions. He has led projects such as the Fish-Bird art-robotics collaboration and the experimental human-robot interaction facility funded by ARC grants. Publications highlight advancements in human-robot collaboration ethics, motion planning for social robots, and control systems for autonomous machinery. His interdisciplinary approach bridges robotics engineering with creative arts, fostering innovations in both technical and artistic domains.
Dr. Fatemeh Golpayegani is an Assistant Professor at the School of Computer Science, University College Dublin. She leads the Multi-agent Systems and Sustainable Solutions lab (MAS3.ucd.ie) and has secured over €1.5M in research grants. Her academic roles include BSc Stage 4 Coordinator and Chair of Women@CS (2012–2023). She holds a PhD from Trinity College Dublin (2018) and professional qualifications in university teaching from UCD. Education: PhD in Computer Science, Trinity College Dublin (2018) Professional Diploma in University Teaching & Learning, University College Dublin (2024) Professional Certificate in University Teaching & Learning, University College Dublin (2023) Research Interests: Focuses on multi-agent systems, sustainability, intelligent transport systems, autonomous decision-making, and edge computing. Her work integrates reinforcement learning, ontology-based models, and adaptive systems to address challenges in smart cities, energy grids, and infrastructure monitoring. Grants & Projects: Principal Investigator for the EU-funded RE-ROUTE project (€multi-million, 2023–2026) on intelligent transport networks. Co-Principal Investigator for the Augmented CCAM project on connected/cooperative autonomous mobility. Funded investigator in SFI centres (I-Form, CONNECT, Biorbic). Awards & Recognition: Researcher of the Year Award (2022) Member of Young Academy of Ireland (2023) Teaching & Mentoring: Coordinates modules in algorithms, Java programming, and operating systems. Supervises PhD students in SFI centres and mentors postdoctoral researchers. Active in promoting EDI as Chair of d-real doctoral training centre. Labs & Collaborations: Leads the MAS3 lab, collaborating on projects like CAPTAIN CARBON (sustainable transport gamification) and ontology-enhanced traffic signal control systems.
Dr. Wenjuan Yu is a Lecturer at the School of Computing and Communications (SCC), InfoLab21, Lancaster University, UK. She holds a PhD in Communication Systems from Lancaster University and has held prior roles, including Research Fellow at the 5G Innovation Centre (5GIC), University of Surrey (2018–2020), and part-time Research Officer at the University of Essex (2017–2018). She is a Fellow of the Higher Education Academy and a Senior Member of IEEE. Her research focuses on communication systems, with emphasis on radio resource management, mMTC, low-latency communications, B5G/6G, MEC, and machine learning. She actively contributes to IEEE conferences as a Technical Program Committee (TPC) member and holds editorial roles, including Executive Editor for Transactions on Emerging Telecommunications Technologies (2019–2022). Current teaching includes CNSCC141 Professionalism in Practice and CNSCC365 Advanced Networking . Dr. Yu supervises PhD students in EE/CS, particularly welcoming applicants from China via CSC scholarships. Her projects include DSI-funded initiatives on sustainable AI-driven resource allocation for 6G and Smart Multi-RAT Traffic Steering for V2X systems. She leads research groups in Security Lancaster (Networks, Systems, Distributed Systems).
Zhanna Sarsenbayeva is a Lecturer in the School of Computer Science at the University of Sydney. Previously, she held a Doreen Thomas Postdoctoral Research Fellowship at the University of Melbourne. Her research focuses on Human-Computer Interaction (HCI), Ubiquitous Computing, Accessibility, and Affective Computing. She earned a PhD in Engineering from the University of Melbourne, an MSc in Computer Science and Engineering from the University of Oulu, and a BSc in Computer Science from University College London. Research Interests: Dr. Sarsenbayeva explores how technology can enhance accessibility, improve emotion recognition in mobile contexts, and address situational impairments. Her work spans wearable sensors, mobile health applications, and ethical AI methodologies. Awards & Honors: 2022–2023: Australia-Germany Joint Research Cooperation Scheme 2021: CIS ECR Grant 2020: Doreen Thomas Postdoctoral Fellowship 2019: Gaetano Borriello Outstanding Student Award Advising & Grants: She currently supervises five PhD students researching topics like mixed reality collaboration and emotion recognition. Her grants include projects on accessibility standards and fairness in AI. International Collaborations: Engages with researchers at Aalborg University (Denmark), University of Oulu (Finland), and LMU Munich (Germany) on interdisciplinary projects.
Alva L. Couch is an Associate Professor at Tufts University's School of Engineering, Department of Computer Science, with a career spanning over 30 years. His work bridges network/system administration, autonomic computing, and hydrologic data science, focusing on scalable solutions for data management and automated system administration. Education: Ph.D. in Mathematics (1988), B.S. in Architecture (1978), and B.A. in Bassoon/Contrabassoon Performance (1978). Research Interests His research centers on: Network and System Administration: Tools like SLINK, Maelstrom, and Babble for dependency analysis, cloud migration, and policy enforcement. Geo-informatics: MEDFORD metadata language and HydroShare platform for hydrologic data curation and discovery. Autonomic Computing: Promise theory, convergent operators, and closure models for self-managing systems. Recent Work Trends His 2024-2018 publications emphasize: Cloud-based hydrologic data management (AnVILMEDFORD, HydroShare) Metadata standards for interdisciplinary research Machine learning for system administration Agent-based resource sharing models Scientific Awards Liebner Teaching Award (1996) Seymour Simches Advising Award (2017) Best Paper Awards: LISA 1996, AIMS 2008, LISA 2001 LISA 2000 Best Student Paper (with Michael Gilfix) Contributions He developed key software like Peep (network auralization) and Slink (configuration management), supported by NSF grants and industry partnerships. His work with CUAHSI's Water Data Center shapes national hydrologic data infrastructure. He also advocates for science education and privacy in computing.
Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.
Dr. Wei Song is a Professor and the Coordinator of Software Engineering at the Faculty of Computer Science, University of New Brunswick (UNB) in Fredericton, New Brunswick, Canada. She has been with UNB since 2009, after completing her postdoctoral studies at UC Berkeley, and has established herself as a leading researcher in mobile networking and wireless communications. Her office is located in room ID419 and she can be reached at wsong@unb.ca. Education Ph.D. in Electrical and Computer Engineering, University of Waterloo (2003-2007) Postdoctoral Fellow, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2008-2009) Research Focus Dr. Song's research spans multiple cutting-edge areas in mobile and wireless networking, with a strong emphasis on integrating artificial intelligence and machine learning techniques. Her work addresses fundamental problems in mobile social networks, Internet of Things, vehicular networks, and mobile cloud computing. She explores how cooperative intelligence and distributed AI can enhance network performance while addressing practical constraints such as energy efficiency and user incentives. Her recent work particularly focuses on intelligent edge computing, mobile crowdsensing with deep reinforcement learning, and social-aware data dissemination through device-to-device communications. She investigates how to turn decentralized mobile "crowds" into coherent working groups and how social connections can be leveraged to improve data dissemination efficiency. Publication Trends Dr. Song's recent publications (2016-2023) demonstrate a clear evolution from traditional wireless networking to AI-driven approaches. While her earlier work focused on fundamental problems in device-to-device communications and resource allocation, her recent publications increasingly incorporate deep reinforcement learning, graph neural networks, and other AI techniques to solve complex optimization problems in mobile crowdsensing and edge computing. This shift reflects broader trends in the field toward intelligent, adaptive networking solutions. Scientific Recognition Best Paper Award from IEEE ICC (2018) UNB Merit Award (2014) Best Student Paper Award from IEEE CCNC (2013) Top 10% Award from IEEE MMSP (2009) NSERC postdoctoral fellowship (2008) Best Paper Award from IEEE WCNC (2007) Professional Service and Mentoring Dr. Song serves as Senior Member of IEEE and has held significant leadership roles, including Chair of the Joint Computer and Communications Chapter of IEEE New Brunswick Section (2014-2020). She has chaired symposia at major conferences including IEEE VTC Fall 2023, 2017, and 2016. As a supervisor, she mentors graduate students in areas including intelligent edge computing and deep learning for networking, and is currently recruiting students for Winter 2024 and Fall 2025.
Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.
Guanrui Li is an Assistant Professor at Worcester Polytechnic Institute's Robotics Engineering Department and the director of the Aerial-robot Control and Perception Lab (ACP Lab). He holds a Ph.D. in Electrical and Computer Engineering from NYU (2024), an M.S. in Robotics from the University of Pennsylvania (2018), and a B.E. in Theoretical and Applied Mechanics from Sun Yat-sen University (2016). His research focuses on aerial robotics, including control methodologies for collaborative transportation, human-robot interaction, and perception-aware systems. Key contributions include Hybrid Perception-Aware MPC frameworks, cooperative manipulation algorithms, and simulation tools like RotorTM. Education: Ph.D., Electrical and Computer Engineering, NYU (2024) M.S., Robotics, University of Pennsylvania (2018) B.E., Theoretical and Applied Mechanics, Sun Yat-sen University (2016) Research Interests: Control and perception of aerial robots, human-robot collaboration, cooperative manipulation, and autonomous systems. Notable work addresses challenges in payload transportation, sensor fusion, and safety-critical navigation. Awards: NSF CPS Rising Stars (2023) Outstanding Deployed System Paper Finalist (IEEE ICRA 2022) NYU Dante Youla Award (2022) NYU Outstanding Dissertation Award (2024) Lab & Team: ACP Lab focuses on advancing aerial robotics through interdisciplinary research. Current projects include mixed reality interfaces for human-robot interaction and fault-tolerant control systems. Recent collaborations include workshops on Rust for Robotics (ICRA 2025) and embodied-AI for aerial systems (ICUAS 2025).
Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.
David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.
Dr. Hakki Erhan Sevil is an Associate Professor in the Department of Intelligent Systems and Robotics at the University of West Florida, within the Hal Marcus College of Science and Engineering. He holds a Ph.D. in Mechanical Engineering from the University of Texas at Arlington and has extensive research experience in robotics, intelligent systems, and autonomous control. His work spans theoretical and applied domains, focusing on resilient and intelligent robotic systems. Ph.D., Mechanical Engineering, University of Texas at Arlington M.S., Mechanical Engineering, Izmir Institute of Technology B.S., Mechanical Engineering, Izmir Institute of Technology Dr. Sevil's research interests lie at the intersection of robotics, artificial intelligence, and control systems. He specializes in autonomous navigation, fault detection and isolation (FDI), multi-agent coordination, computer vision, and bio-inspired computational methods. His work emphasizes real-world implementation in unmanned and self-sustained systems, particularly in challenging environments. His recent publications and projects highlight a strong trend toward intelligent, resilient, and distributed robotic systems. Themes include entropy-based behavior modeling for UAV swarms, assistive robotics for household tasks, post-disaster damage assessment using aerial vision, and advanced guidance for GPS-denied navigation. These reflect a multidisciplinary approach combining machine learning, control theory, and robotics engineering. 2024 Faculty Excellence in Teaching Award, UWF 2024 Faculty Excellence in Undergraduate Research Mentoring Award, UWF DURIP Grant ($478,000) from ONR (with IHMC) USDA Grant ($728,000) with New Mexico State University US Air Force SBIR/STTR Grant ($110,000) with Catalano Aerospace AFWERX Funding for Distributed Behavior Research Dr. Sevil actively mentors Ph.D. and M.S. students and leads the Sevil Research Group, which has secured multiple internal and external grants from NSF, NASA, ARL, ONR, and USDA. He has served as PI and Co-PI on funded projects and advises student teams that have won national awards. His lab, the Intelligent Systems and Robotics Lab, is highlighted in university communications and national challenges. The group collaborates with IHMC, NMSU, and industry partners, fostering innovation in autonomous systems. The Sevil Research Group operates within the Intelligent Systems and Robotics Lab at UWF, conducting cutting-edge research in autonomous navigation, swarm intelligence, and resilient robotics. The lab collaborates with the Institute for Human and Machine Cognition (IHMC), New Mexico State University, and private aerospace firms. It supports student-led projects, participates in national robotics challenges, and maintains active GitHub repositories for open research dissemination.
Dr. Tim Schwartz is an Associated Member at the German Research Center for Artificial Intelligence (DFKI) located at the Saarland Informatics Campus in Saarbrücken, Germany. He is affiliated with the Ubiquitous Media Technology Lab (UMTL) where he conducts research at the intersection of human-robot interaction, multimodal interfaces, and industrial applications. His work spans over two decades with significant contributions to the fields of robotics, augmented reality, and Industry 4.0 implementations. Dr. Schwartz's research interests focus primarily on Human-Robot Interaction , Multi-modal Interaction , and Industry 4.0 applications. His work explores how humans and robots can effectively collaborate in industrial settings, with particular attention to communication modalities, task division, and intuitive interfaces. Recent projects include human-robot collaboration in assembly cells, social cognitive robots for warehouse environments, and augmented reality applications for aircraft manufacturing. Analysis of his publication history reveals a clear trajectory from foundational work in multimodal interfaces and context-aware computing (2005-2015) toward increasingly applied research in industrial robotics and human-robot collaboration (2016-2024). His most recent publications demonstrate a strong emphasis on practical implementations in manufacturing and warehouse environments, with a particular focus on optimizing work dynamics between humans and robots. Dr. Schwartz actively engages in academic supervision, offering thesis opportunities such as the user-study on optimal work dynamics in human-robot collaboration at the Power4Production Hall in Saarbrücken. His collaborations extend to institutions including ZeMA (Zentrum für Mechatronik und Automatisierungstechnik gGmbH), indicating strong industry-academia partnerships. At DFKI, he works within a vibrant research community that includes numerous colleagues in the Ubiquitous Media Technology Lab, contributing to a collaborative environment focused on cutting-edge research in human-robot interaction, multimodal systems, and industrial applications of artificial intelligence.
Ludovic Saint-Bauzel is a lecturer at Sorbonne University and head of the IRIS team at the Institute of Intelligent Systems and Robotics (ISIR). His work focuses on improving physical human-robot interaction for individuals with autonomy loss through disability or aging. Specializes in computational models of disabilities Develops user intent detection through sensor fusion Active in IFRH and Fedrha federations IEEE and True Life Lab member Research interests His research centers on Human-robot physical interaction with applications in Elderly care , Smart-walker development, and Walking exoskeleton systems. Key methodologies include: Sensor fusion (depth cameras, force sensors, IMUs) Adaptive robot control systems Pathological movement modeling Motor intent prediction algorithms Article trends show consistent focus on haptic communication (6/15), assistive robotics (9/15), and sensorimotor interaction (11/15) across 2013-2022 publications. Laboratory involvement : Leads the IRIS team at ISIR, part of Fedrha (Federation for Research on Disability and Autonomy) with 50+ research teams.
Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.
