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Zih-Yun ChiuView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Medical Robotics
• +5 more

Zih-Yun (Sarah) Chiu is an incoming tenure-track Assistant Professor in the Department of Computer Science at Johns Hopkins University , starting in Fall 2025. She is also a member of the Johns Hopkins Data Science and AI Institute , focusing on developing intelligent robots that emulate the expertise of skilled medical professionals in complex environments. Chiu earned her PhD in Electrical and Computer Engineering (2025) from the University of California, San Diego , advised by Michael Yip , and holds a BS in Electrical Engineering (2017) from National Taiwan University . Her research centers on: Mathematical modeling of medical and general knowledge for robotic operation Integration of robotic perception, planning, control, and learning Advancing robot sensing, planning, and adaptability for autonomous interventions Uncertainty-aware trajectory optimization frameworks Deep reinforcement learning for surgical automation Her publications reflect trends in: Medical Robotics (autonomous suturing, tool tracking) Reinforcement Learning (policy fusion, incremental learning) Perception Systems (pose estimation, uncertainty modeling) Motion Planning (biomechanically safe trajectories, time-optimal control) Robotic Simulation (reverse curriculum generation) Scientific recognitions include: 2025 Robotics: Science and Systems Pioneer 2024 EECS Rising Star 2023 IEEE ICRA Best Paper Award in Medical Robotics 2023 ICRA Workshop Best Poster Award Chiu is actively recruiting students at all academic levels (undergraduate, master’s, PhD) and leads research at the intersection of robotics, AI, and healthcare innovation. Her recent projects include open-source implementations for knowledge-grounded reinforcement learning algorithms and surgical needle tracking systems.

• Robotics
• Agricultural Robotics
• Adaptive Control Systems
• +6 more

Antonio Candea Leite is an Associate Professor at the Department of Mechanical Engineering and Technology Management, Norwegian University of Life Sciences (NMBU). His research focuses on adaptive and robust control systems, visual servoing, robot manipulators, and agricultural robotics applications. His work emphasizes: Development of autonomous navigation systems for agricultural robots Integration of computer vision for precision agriculture tasks Control strategies for uncertain robotic systems Automation in food quality measurement and pest management Advanced sensor integration for manufacturing processes Recent research trends show strong emphasis on: CNN-based crop row detection for autonomous navigation (2024) Human-robot collaboration frameworks for fruit picking (2024) Robotics solutions for fatty acid measurement in food production (2023-2022) Precision pest control systems using smart automation (2023) No scientific awards or grants are explicitly listed in the provided information. He is actively involved in advising and developing robotic platforms for agricultural and industrial applications without specific student names mentioned here.

• Robotics
• Artificial Intelligence
• Reinforcement Learning
• +5 more

Abdeslam Boularias is an Associate Professor in the Department of Computer Science at Rutgers, The State University of New Jersey. His research focuses on robotics, artificial intelligence, and reinforcement learning, with a strong emphasis on robotic manipulation, model-based control, and perception in cluttered environments. He leads research groups in Artificial Intelligence, Intelligent Systems, and Robotics. Key contributions include advancing techniques for dynamic object tracking, sensorimotor learning in unstructured settings, and integrating physics-based reasoning with deep learning. His work has been recognized with prestigious grants such as the NSF CAREER Award and collaborative grants with institutions like Yale University. Grants: NSF NRI Grant (2022), NSF SA&S Grant (2021), NSF CAREER Award (2024) Research Themes: Robotics in clutter, reinforcement learning for manipulation, physics-aware perception, and end-to-end systems for autonomous robots His recent publications emphasize scalable manipulation learning, one-shot imitation, and diffusion models for affordance prediction. He actively contributes to advancing robotic systems that operate reliably in complex real-world scenarios.

Dimitrios PapageorgiouView profile

Assistant Professor

• Robotic Control Systems
• Physical Human-Robot Interaction
• Robot Learning
• +3 more

Dimitrios Papageorgiou is an Assistant Professor specializing in control and automation systems with robotic applications at the Hellenic Mediterranean University. His research develops advanced control frameworks for human-robot collaboration, motion learning, and adaptive manipulation. Key innovations include variable admittance controllers for heavy payload manipulation, constraint-aware motion learning algorithms, and active perception systems for robotic skill transfer. Recent work advances bimanual agricultural manipulation and quadruped locomotion control on challenging terrains. His publication trends demonstrate consistent focus on physical human-robot interaction paradigms, particularly kinesthetic teaching methods and safety-aware collaborative systems. Research integrates hardware development with control theory for industrial and service robotics applications.

Ashwin DaniView profile

Associate Professor

• Robotics
• Autonomous Navigation
• Estimation and Control Theory
• +5 more

Ashwin Dani is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Connecticut, affiliated with the School of Engineering. His research focuses on robotics, control systems, and autonomous navigation, with applications in manufacturing, biomedical engineering, and environmental monitoring. He holds a Ph.D. in Mechanical and Aerospace Engineering from the University of Florida (2011) and a B.S. in Mechanical Engineering from the University of Pune, India (2005). Prior to his faculty role, he served as a Post-doctoral Research Associate at the University of Illinois at Urbana-Champaign (2011–2013). His research interests span estimation theory, autonomous systems navigation, human-robot collaboration safety, sensor fusion, and neuro-prosthesis control. Notable contributions include advancements in vision-based control, GPS-denied navigation for UAVs, and adaptive control algorithms with safety guarantees. He leads the Robotics & Controls Lab, which develops solutions for industrial automation and assistive robotics. Dr. Dani has been awarded the Innovator’s Invention Award from the University of Florida for his work on nonlinear dynamic estimation. His research has produced patents on structure-motion estimation and over 50 peer-reviewed publications in top journals like IEEE Transactions on Automatic Control and Journal of Field Robotics. He actively explores intersections between machine learning and control theory to enhance system safety and adaptability in dynamic environments. His academic contributions include pioneering work on concurrent learning observers for depth estimation and developing probabilistic frameworks for human intent prediction in collaborative tasks. Ongoing projects focus on safe human-robot interaction protocols, deformable object manipulation, and real-time adaptive control systems for uncertain environments.

• Robotics
• Intelligent Systems
• Unmanned Aerial Vehicles (UAVs)
• +7 more

Petar Trslić is an Assistant Professor at the University of Limerick's School of Engineering, affiliated with the Centre for Robotics and Intelligent Systems. His research focuses on advanced robotics, autonomous systems, and sensor fusion technologies, with applications in maritime operations, wildlife monitoring, and infrastructure inspection. Key technical areas include Unmanned Aerial Vehicles (UAVs), Remotely Operated Vehicles (ROVs), path planning algorithms, and LiDAR-based systems. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure, climate action, and sustainable energy. Trslić has collaborated internationally on projects involving cooperative robotic systems, multi-sensor fusion for state estimation, and autonomous vehicle coordination in challenging environments. His recent publications highlight innovations in UAV wildlife surveillance, maritime robotics for offshore wind farms, and automated infrastructure inspection using 3D reconstruction. He has secured research funding for initiatives in autonomous underwater robotics and has developed novel algorithms for robotic docking and navigation under dynamic conditions. Trslić's lab emphasizes practical solutions for industrial automation challenges and environmental monitoring through interdisciplinary approaches.

• Multi-Agent Systems
• Control Systems
• Robotics
• +4 more

Zhiqi Tang is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology's Division of Decision and Control Systems (EECS Department), supervised by Professors Jonas Mårtensson and Karl Henrik Johansson. She previously held positions as a postdoctoral researcher at Instituto Superior Técnico (IST) in Portugal and as an Invited Assistant Professor in their Department of Electrical and Computer Engineering. She holds a double Ph.D. in Automatic Control and Robotics from IST and I3S-CNRS (Université Côte d'Azur), receiving the EDSTIC Ph.D. thesis prize, and a B.S. in Electrical and Computer Engineering from the University of Macau (2015). Her research focuses on estimation, control, and decision-making in multi-agent systems, with applications in Robotics and Transportation Systems. Key areas include cooperative estimation, networked systems, nonlinear dynamics, and formation control. She teaches the Nonlinear Control (EL2620) course at KTH. Her recent work emphasizes safe platooning for autonomous vehicles, collision avoidance in UAV formations, and distributed localization under dynamic conditions. Notable contributions include barrier function-based control for CAVs and bearing-rigidity theory for multi-agent coordination. Her research bridges theoretical control frameworks with practical implementations in robotics and transportation. Awards: EDSTIC Ph.D. thesis prize (2021) Grants/Advising: Supervises postdoctoral and doctoral research in multi-agent systems Labs/Teams: Involved with KTH's Decision and Control Systems group and IST's Institute for Systems and Robotics

• Solar Energy Optimization
• Control Systems
• Remote Laboratories
• +7 more

Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.

• Dynamical Systems
• Control Theory
• Machine Learning
• +3 more

Mario Sinani is a Researcher and Teaching Assistant in the Department of Aeronautics at Imperial College London's Faculty of Engineering. His research focuses on dynamical systems, control theory, and machine learning applied to nonlinear systems and fluid-solid interactions. He develops data-driven models for nonlinear control of flexible structures interacting with fluid flows under supervision of Dr. Andrew Wynn (Flow Control Group) and Prof. Rafael Palacios (Load Control and Aeroelastics Lab). Education includes a PhD in Data-Driven Modelling and Nonlinear Control from Imperial College London, and MSc/BSc in Mechanical Engineering with a focus on Control Theory and Robotics from National Technical University of Athens. Prior to Imperial, he worked at CERN on particle detector technologies and as an R&D engineer in Zurich developing dynamic light control systems. Teaching responsibilities include Control Systems for MSc students, Flight Dynamics and Control for MEng Aeronautical Engineering, and Applied Aerodynamics courses. His research integrates advanced mathematical analysis with cutting-edge machine learning techniques to address complex fluid-structure interaction challenges in aerospace systems.

• Multibody System Dynamics
• Flexible Multibody Systems
• Underactuated Mechanical Systems
• +12 more

Prof. Dr.-Ing. Robert Seifried is a Full Professor and Chair for Structural Mechanics at the Institute of Mechanics and Ocean Engineering , Hamburg University of Technology (TUHH) . He also serves as the Dean of the School of Multidisciplinary Engineering Science and Technologies since 2019. His leadership roles include Head of FIT, Head of AIW, Head of Engineering Science, and Head of Theoretical Mechanical Engineering. He is actively involved in research, teaching, and academic administration. PhD in Mechanical Engineering, University of Stuttgart (2005) Habilitation in Applied Mechanics, University of Stuttgart (2012) Postdoctoral research at UC Berkeley (2006–2007) Assistant Professor at University of Stuttgart (2008–2013) Full Professor at University of Siegen (2013–2014) Full Professor at TUHH (since 2014) Prof. Seifried’s research centers on multibody system dynamics , with a strong emphasis on underactuated and flexible systems . His work spans soft robotics , particle dampers , topology optimization , and ocean engineering . He investigates advanced control strategies such as funnel control, servo constraints, and stable inversion, particularly for systems with nonlinearities and impacts. His research integrates computational modeling (e.g., isogeometric analysis, adjoint sensitivity) with experimental validation in areas like wave energy converters and underwater robotics. The recent publications highlight a trend toward interdisciplinary robotics and smart ocean technologies . Key themes include soft robot control and sensing , particle damping for vibration suppression , topology optimization under uncertainty , and modeling of nonlinear ocean waves . These works demonstrate a consistent focus on enhancing system performance, robustness, and energy efficiency through advanced simulation and control. Lead investigator in multiple research projects on multibody dynamics and ocean systems Advisor to numerous PhD and Master’s students in robotics and mechanics Developer of simulation tools like Dynmanto for multibody systems Recipient of research funding for projects in soft robotics, wave energy, and vehicle dynamics Prof. Seifried leads a dynamic research group focused on mechanics and marine engineering . His team develops novel solutions for soft and underwater robots , hybrid particle dampers , and optimized marine structures . The group combines theoretical modeling, numerical simulation, and experimental testing, often in collaboration with industry and international institutions. Their work supports future advancements in autonomous marine systems, resilient infrastructure, and energy harvesting technologies.

• Aerospace Engineering
• Robotics
• Control Systems
• +4 more

Ahmet Talha Çetin is a Researcher at the Aviation Institute of Istanbul Technical University . His work focuses on aerospace engineering, robotics, and control systems, particularly in applications involving drones, quadrotors, and satellite attitude control. He has contributed to both theoretical advancements in optimal control algorithms and practical implementations of multi-drone systems and guidance strategies. Primary Affiliation: Istanbul Technical University, Aviation Institute Research Area: Control Systems, Trajectory Optimization, Autonomous Systems Research Interests : Çetin's research spans aerospace engineering and robotics, with a focus on drone navigation , stochastic optimal control , and attitude control systems . His work addresses challenges in autonomous guidance, uncertainty minimization, and multi-drone coordination. He applies mathematical frameworks like entropy minimization and model predictive control to real-world problems in aviation and telecommunications. Publication Trends : Over 2023-2025, Çetin's research output demonstrates a strong emphasis on drone engineering , quadrotor dynamics , and control optimization . His contributions include both theoretical advancements in non-Gaussian uncertainty handling and practical implementations like cold gas thruster systems for satellites. Collaborations with researchers like E. Koyuncu and İ. Özkol are prominent across his publications. Advising & Collaborations : No explicit student advising information is available. He collaborates extensively with Istanbul Technical University's aerospace engineering team on projects involving drone systems, satellite control, and network virtualization technologies.

Abolfazl MohebbiView profile

Associate Professor

• Robotics
• Control systems
• Applied Artificial Intelligence
• +10 more

Abolfazl Mohebbi is an Associate Professor in the Department of Mechanical Engineering at Polytechnique Montréal. He directs the Polytechnique Laboratory for Assistive and Rehabilitation Technologies (POLAR) and holds the TransMedTech Research Chair in Assistive and Rehabilitation Technologies. His academic appointments also include Adjunct Professor at McGill University's Department of Biomedical Engineering and membership in numerous research networks including REPAR, FRQNT INTER cluster, Pediatric Rehabilitation Technology Park, and several biomedical engineering institutes. Professor Mohebbi's research focuses on robotics, control systems, and applied artificial intelligence with applications in biomedical engineering. His laboratory develops innovative robotic and biomechatronic technologies to help humans relearn, restore, or enhance their neuromotor performance. Specific research areas include human balance control modeling, joint neuromechanics, deep learning for medical diagnosis, sensory restoration in prosthetic users, custom exoskeleton design, and robotic rehabilitation systems. His recent publications (2022-2025) show a strong focus on rehabilitation robotics, with significant contributions to exoskeleton control, prosthetic feedback systems, neuromuscular modeling, and AI applications in medical diagnostics. The research demonstrates interdisciplinary collaboration across engineering, neuroscience, and clinical rehabilitation. Professor Mohebbi has received research funding from various organizations including NSERC, as evidenced by multiple grants awarded to Polytechnique Montréal researchers. His work demonstrates strong connections between academic research and clinical applications in rehabilitation engineering. He teaches courses in dynamic systems analysis, mechatronics, and dynamic measurement at Polytechnique Montréal and has supervised five graduate students to completion (one PhD and four Master's students) with research topics directly aligned with his laboratory's focus areas.