Dr. Peter Wolf is a Lecturer at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich. His research focuses on robot-aided motor learning and sports engineering, particularly exploring concurrent feedback strategies for efficient motor task learning and applications in rowing and climbing. He advises students in the Medical Technology and Human Movement Science & Sport majors, emphasizing interdisciplinary approaches combining biomechanics, mechatronics, and coding. His research group develops tools to support athletes and rehabilitation patients, with a focus on exosuits, exoskeletons, and robotic feedback systems. Notable projects include optimizing gait parameters for adolescents with crouch gait and enhancing wearable robotic systems for daily mobility support. Dr. Wolf also contributes to curriculum design, recommending courses like Biomedical Engineering, Neural Control of Movement, and Machine Learning for Healthcare. His work bridges biomechanical analysis, robotics, and clinical applications, with recent innovations in vestibular stimulation for sleep disorders and thermal stress monitoring for occupational workers. Collaborations span sports science, rehabilitation engineering, and human-robot interaction, reflecting his commitment to applied and interdisciplinary research.
Professor Peng Shi is a faculty member in the School of Electrical and Mechanical Engineering at the University of Adelaide, specifically within the Department of Electrical and Electronic Engineering. He holds a Professor rank and is actively involved in research and academic leadership. Academic Rank: Professor Affiliations: University of Adelaide, School of Electrical and Mechanical Engineering Research Interests: Automation and control systems, cyber-physical systems, networked control systems, autonomous robotics, artificial intelligence, and control theory. Professor Shi has an extensive track record in research, with over 30 years of contributions to advanced control theory and interdisciplinary applications. He has authored 25 monographs, 800+ journal articles, and numerous conference papers, generating over 90,000 citations with an h-index of 164. His work emphasizes cyber-physical systems, human-machine collaboration, and resilient control under cyber attacks. His recent publications (2023–2025) focus on advanced control methodologies for robotics, power systems, and cybersecurity, addressing challenges such as fault diagnosis, consensus control, and secure estimation in networked environments. Scientific Awards: Fellowships from IEEE, IET, IMA, IEAust, and IETI Highly Cited Researcher (2014–present) Professor Shi advises students in advanced control systems and collaborates on grants related to smart grids, robotics, and cybersecurity. He leads research groups focused on resilient control systems and has developed methodologies for distributed control and fault-tolerant designs.
Dr. Gamal ELGHAZALY is a Research scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), affiliated with the Ubiquitous and Intelligent Systems department. His work focuses on autonomous driving technologies, V2X communication systems, robotics, and control systems. Key research areas include 5G-enabled teleoperated driving, high-definition map construction, and 3D perception for autonomous vehicles. He has developed platforms like RoboCar and contributed to frameworks like FastCycle for modular automated systems. Research interests span robotics kinematics (planar parallel manipulators), adaptive control methodologies (sliding mode control, fuzzy logic), and hybrid systems (cable-driven robots). His publications emphasize real-time motion planning, sensor fusion, and safety-critical systems. Recent works (2023-2025) highlight advancements in 4D perception, cloud-assisted 3D reconstruction, and V2X-enabled collaborative systems. Publications from 2017-2018 reflect early contributions on parallel manipulator modeling and control, while recent efforts focus on integrating cutting-edge technologies like 5G and edge computing into autonomous systems. His work bridges theoretical robotics with practical implementations, addressing challenges in both dynamic environments and industrial applications.
Kuan Fang is an Assistant Professor of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning-based perception and control systems. Previously, he was a postdoc at UC Berkeley under Sergey Levine and earned his Ph.D. and M.S. from Stanford University under Fei-Fei Li and Silvio Savarese, with a B.S. from Tsinghua University. He has also worked at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia. Education: Ph.D. & M.S., Computer Science, Stanford University Bachelor's Degree, Tsinghua University Research Interests: Robot manipulation and control Reinforcement learning and policy optimization Robot perception and vision-language integration Generalization in robotics across tasks, environments, and robots Open-world robotic systems leveraging large-scale data Teaching: CS 6758: Deep Learning for Robotics (Fall 2024) CS 4756: Robot Learning (Spring 2025) Lab & Collaborations: His lab at Cornell develops scalable algorithms and systems for robotic perception and control, emphasizing data-driven methods. Notable work includes ReLIC for interlimb coordination, GLIDE for bimanual manipulation, and TRA for compositional task execution. He collaborates with institutions like Boston Dynamics AI Institute and UC Berkeley.
Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.
François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.
Dr. Elvedin Kljuno serves as a Visiting Professor at the International University of Sarajevo (IUS), Bosnia and Herzegovina, where he contributes expertise in defense-related engineering disciplines. His academic profile centers on advanced computational methodologies applied to complex mechanical systems and explosive dynamics. His research spans five core domains: Ballistics (armor penetration mechanics, artillery projectile dynamics, and fragmentation effects) Structural Engineering (stress/strain analysis of piping systems using 3D scanning and numerical methods) Blast Engineering (internal/external blast load prediction and overpressure modeling) Aerodynamics (trajectory modeling for irregular bodies under extreme forces) Robotics (cable-driven locomotion systems and bipedal mechanics) His work consistently integrates finite element analysis, computational fluid dynamics, and experimental validation to solve high-stakes engineering problems. Analysis of his 15 most recent publications (2019-2024) reveals a pronounced focus on defense technology applications, particularly in projectile dynamics (40% of works), structural integrity under explosive loads (30%), and advanced simulation techniques (30%). Key trends include the development of novel numerical frameworks for blast wave propagation, refinement of armor penetration models, and innovative stress analysis methodologies using 3D scanning. His research demonstrates strong interdisciplinary connections between mechanical engineering, materials science, and military technology. While no scientific awards or doctoral students are documented in available sources, Dr. Kljuno maintains active research output through IUS's engineering programs. His work shows particular relevance to military R&D institutions focused on munitions design, structural survivability, and high-speed aerodynamics.
Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.
Professor Jinjun Shan is a Full Professor of Space Engineering and former Department Chair (2018-2023) in the Department of Earth and Space Science and Engineering at York University's Lassonde School of Engineering. An internationally recognized expert in dynamics, control and navigation, he joined York University as an Assistant Professor in 2006, was promoted to Associate Professor in 2011, and became a Full Professor in 2016. Dr. Shan received his B.Eng., M.Eng., and Ph.D. degrees from Harbin Institute of Technology, China, in 1997, 1999, and 2002, respectively. Before joining York, he was a Post-Doctoral Fellow at the University of Toronto Institute for Aerospace Studies (2003-2006) and a Research Assistant at City University of Hong Kong (2002-2003). His research focuses on dynamics, control and navigation, autonomous systems, multi-agent systems, smart materials and structures, space instrumentation, active vibration control, and orbit dynamics. Dr. Shan has made significant contributions to national and international space missions including NEOSSat and has attracted over $5 million in research funding from governmental agencies and industry partners. His laboratory, the Spacecraft Dynamics Control and Navigation Laboratory (SDCNLab), which he founded in 2006, conducts cutting-edge research in space engineering. Dr. Shan's extensive publication record includes over 200 peer-reviewed journal and conference papers, with his most recent work focusing on multi-agent formation control, autonomous vehicle decision-making, quadrotor control systems, and smart material applications. His research shows a clear progression from fundamental dynamics and control theory toward increasingly complex multi-agent systems and real-world applications in autonomous vehicles and space engineering. Fellow of Canadian Academy of Engineering (CAE) Fellow of Engineering Institute of Canada (EIC) Fellow of American Astronautical Society (AAS) Associate Fellow of AIAA Alexander von Humboldt Research Fellowship JSPS Fellowship Lassonde Educator of the Year Award (2022) Named in Stanford's list of world's top 2% researchers Dr. Shan has successfully mentored numerous graduate students and post-doctoral fellows, with current advisees working on cutting-edge projects in multi-agent systems, UAV control, and smart materials. His research is supported by substantial funding from NSERC, CSA, and industry partners. As the founding director of SDCNLab, he has built a comprehensive research facility for spacecraft dynamics, control, and navigation, recently expanding to include autonomous unmanned vehicle research through a CFI JELF award. His laboratory continues to make significant contributions to both theoretical advancements and practical applications in space engineering and autonomous systems.
Philip W. T. Pong is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology. His research focuses on electromagnetic sensing technologies for smart grid applications and nanotechnology. Education includes: Ph.D. in Engineering from University of Cambridge (2005) B.Eng. in Electrical and Electronic Engineering from University of Hong Kong (2002) Research expertise spans electromagnetic sensors, smart grid monitoring, nanotechnology applications in energy systems, and magnetic materials. Recent publications demonstrate strong focus on current sensing technologies (both contact and non-contact), fault diagnosis in power systems, wind turbine monitoring, and magnetic materials characterization. Article analysis reveals consistent emphasis on magnetoresistive sensors, wireless power transfer, renewable energy systems monitoring, and spintronics applications. Professional distinctions include Fellow status in multiple engineering institutions (IET, Energy Institute, IOM3, NANOSMAT) and chartered engineer credentials. Serves on editorial boards of IEEE journals. No information available regarding scientific awards or current students.
Jean-Pierre Barbot is a Full Professor at Ecole Centrale Nantes , affiliated with the Quartz Team and CODEx Team within LS2N UMR CNRS 6004. His work focuses on Non-Linear Automation with applications to renewable energies and low-carbon mobility. He actively contributes to community initiatives like the GDR MACS and SAGIP . Research Interests Non-Linear Automation Renewable Energy Systems Low-Carbon Mobility Control Theory for Cyber-Physical Systems Publications Trends : His recent work (2024-2025) emphasizes advanced control strategies for renewable energy systems (e.g., PV inverters, wind turbines), fault-tolerant control, and nonlinear differentiators. Key methodologies include flatness-based control, sliding mode techniques, and hybrid discretization approaches. Labs & Teams Quartz Team CODEx Team, LS2N UMR CNRS 6004
Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.
Dmitry Berenson is an Associate Professor in the Robotics Department and Electrical Engineering and Computer Science Department at the University of Michigan. He holds a B.S. from Cornell University (2005) and a Ph.D. from Carnegie Mellon University (2011). His research focuses on algorithms for robotic manipulation, motion planning, and control, emphasizing integration with real-world systems and open-source distribution. He has received the IEEE RAS Early Career Award and NSF CAREER Award. His academic journey includes postdoctoral work at UC Berkeley (2012) and faculty positions at Worcester Polytechnic Institute (2012-2016). He leads the ARM Lab, exploring topics such as deformable object manipulation, tactile control, and learning-based planning. Teaching responsibilities include courses like ROB 502 (Programming for Robotics), EECS 465 (Algorithmic Robotics), and ROB 520 (Motion Planning). Education: B.S., Electrical and Computer Engineering, Cornell University (2005) Ph.D., Robotics Institute, Carnegie Mellon University (2011) Postdoctoral Research, UC Berkeley (2012) Research Interests: Learning and motion planning for manipulation Control theory and optimization Deformable object interaction Robot perception and tactile systems Key Contributions: Development of algorithms for manipulation under uncertainty Advances in motion planning with contact feedback Integration of learning with classical robotics methods Recent publications emphasize probabilistic modeling, tactile-driven control, and generalization in learned dynamics. His work addresses challenges in cluttered environments, deformable objects, and safe human-robot collaboration.
Blair Thornton is a Professor of Marine Autonomy at the University of Southampton's Faculty of Engineering and Physical Sciences. He leads research in autonomous marine robotics, sensing, and AI for marine science, and co-directs the FEPS In situ and Remote Intelligent Sensing (IRIS) Centre of Excellence. His work focuses on developing low-cost robotic platforms, 3D seafloor mapping systems, and AI-driven data interpretation methods. Education: PhD in underwater robotics (University of Southampton, 2006), postdoctoral research at University of Tokyo (2006-2016), and over 55 ocean expeditions (30 as PI). Teaching includes Maritime Robotics and Intelligent Mobile Robotics modules with Python-based practicals. Key Projects: BioCam (NERC), TechOceanS (EU), DriftCam (EPSRC), and Smarty200 (EPSRC). Collaborations include Sonardyne International, National Oceanography Centre, and JAMSTEC. Awards include the Okamura Kenji Prize (2014), Shell Ocean Discovery Xprize (2019), and IEEE Mid-Career Rising Star Award (2022). Active in journal editing (IEEE Oceanic Engineering, Robotics and Automation Letters) and external speaking roles.
Cameron Riviere is an Adjunct Assistant Professor in the Department of Rehabilitation Science and Technology at the University of Pittsburgh and a faculty member at the Robotics Institute, Carnegie Mellon University. He leads the Surgical Mechatronics Laboratory, focusing on robotic systems for microsurgery and medical interventions. Ph.D. in Mechanical Engineering, Johns Hopkins University (1995) B.S. in Ocean Engineering, Virginia Tech (1989) B.S. in Aerospace Engineering, Virginia Tech (1989) His research spans medical robotics , surgical mechatronics , and bioengineering , with emphasis on handheld micromanipulators, tremor suppression, and robot-assisted intraocular and cardiac procedures. He develops systems that enhance precision, safety, and autonomy in surgery through advanced sensing, control, and machine learning. The recent publications highlight a strong trend in image-guided surgical robotics , particularly using monocular vision and electromagnetic tracking for retinal and cardiac interventions. Themes include physiological motion compensation, force control, needle steering, and real-time localization. His work bridges engineering innovation with clinical application, aiming to improve surgeon workflow and patient outcomes. Second Place, 1995 Whitaker Student Paper Competition, IEEE Engineering in Medicine and Biology Society As director of the Surgical Mechatronics Laboratory, Dr. Riviere mentors students and researchers in developing next-generation surgical robots. While no specific grants are listed, his sustained publication record suggests continuous funding in robotics and biomedical engineering. His work includes collaborations across institutions and disciplines, particularly in integrating robotics into ophthalmology and cardiology. He directs the Surgical Mechatronics Laboratory at Carnegie Mellon, where his team develops innovative robotic tools such as the Micron handheld device, epicardial wire robots, and soft actuators for ablation catheters. The lab focuses on practical, deployable systems that address clinical challenges in minimally invasive surgery.
