جستجوی استاد راهنمای دانشگاهی

Kristian Muri Knausgård is a Lecturer at the Department of Engineering Sciences , University of Agder , Norway. He teaches courses in embedded systems, software development, and robotics. Current courses: MAS245 (Embedded Computer Systems), MAS417 (Software Development), MAS418 (Robotics Programming) Previous courses: MAS218 (Electrical Circuits), MAS234 (Embedded Systems) His research focuses on embedded systems , real-time systems , and technical cybernetics , with applications in artificial intelligence , computer vision , and systems engineering . He contributes to the university's research groups on Robotics and Automation and Systems Engineering and Modeling . Recent publications show a strong emphasis on: Autonomous systems (robotics, docking algorithms) Deep learning applications in marine ecology 3D reconstruction and computer vision techniques Fish detection/classification using neural networks Industrial automation for aquaponic systems His work bridges theoretical research with practical implementations in mechatronic systems and environmental monitoring.

• Embedded computer systems
• Real-time systems
• Technical cybernetics
• +۵ مورد دیگر

Yang Lin is an Assistant Professor at the University of Rhode Island , affiliated with the Department of Mechanical, Industrial & Systems Engineering . His research focuses on Microfluidics , Acoustofluidics , and Organ-on-a-Chip technologies, with applications in Environmental Monitoring , Food Safety , and Human Health . Education : Ph.D. in Mechanical Engineering (2019) and M.S. in Mechatronic Engineering (2015) from the University of Illinois at Chicago, and B.S. in Mechanical Design Manufacturing and Automation (2012) from Beijing Information Science and Technology University. Research Interests include: Acoustofluidics : Developing non-invasive, biocompatible fluid manipulation techniques using acoustic bubbles and membranes. AI-Enhanced Diagnostics : Leveraging convolutional neural networks for sample-to-answer diagnostic systems in public health. 3D Printed Microfluidics : Expanding additive manufacturing for low-cost, complex physiological structures in healthcare. Environmental Microfluidics : Detecting microplastics and contaminants in water and food systems. Publications highlight advancements in 3D printed microneedles , machine learning for nanoplastic detection , and magnetofluidic biosensors . Lab Members include current Ph.D. students and alumni who have completed M.S. and B.S. degrees under his mentorship.

• Microfluidics
• Acoustofluidics
• Environmental Monitoring
• +۷ مورد دیگر

Shi-Di Huang is a Tenured Associate Professor at the Department of Mechanics and Aerospace Engineering under the College of Engineering at Southern University of Science and Technology (SUSTech) , Shenzhen, China. He joined SUSTech in 2017 as Assistant Professor and was promoted to Associate Professor in 2018, with tenure since 2023. 2005–2009 : Bachelor of Science (Physics) at Fudan University 2009–2014 : Doctorate (Physics) at The Chinese University of Hong Kong Research Directions : Turbulence : Boundary layer dynamics, turbulent coherent structures, small-scale statistical characteristics, and applications in heat transfer and pollutant dispersion. Physical Oceanography : Laboratory and numerical modeling of diapycnal mixing, river plumes, water-sediment interface interactions, and wave-vortex coupling. Active Matter : Swimming dynamics of plankton/micro-robots, collective motion, hydrodynamic interactions, and environmental responses. Nonlinear Dynamics & Complex Networks : Chaos theory, pattern formation, and network analysis in fluid systems. Scientific Trends : His recent work focuses on turbulence scaling laws, centrifugal force effects in convection, Prandtl number dependencies, and aerodynamic phenomena in urban/transport systems. Collaborative studies with Ke-Qing Xia and others explore coherent structures, transport enhancement, and geophysical/environmental applications. Scientific Awards : 2019 Guangdong Province Pearl River Talent Program 2017 Shenzhen Peacock Program 2016 Hong Kong Young Scientist Award 2014 Chinese University of Hong Kong Young Scholars Thesis Award 2009 Shanghai Outstanding Graduate Research Leadership : He has directed four National Natural Science Foundation of China (NSFC) projects and actively recruits students/postdocs for turbulence, ocean dynamics, and active matter research. He contributes to academic service as a reviewer for journals like Physical Review Letters and Journal of Fluid Mechanics , and served as guest editor and session chair.

• Turbulence
• Natural Convection
• Physical Oceanography
• +۷ مورد دیگر

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +۵ مورد دیگر

Dr. Khoi Dang Ly is a Postdoctoral Research Associate at Cornell University, specializing in robotics with a focus on embedded control systems and soft robotics. He holds a Ph.D. in Mechanical Engineering (2021) from the University of Colorado Boulder and a B.Sc. in Mechanical Engineering (2017) from Texas Tech University. His research targets the integration of high-speed electro-hydraulic actuators, self-sensing mechanisms, and model predictive control to advance the autonomy of soft robotic systems. Ph.D. in Mechanical Engineering (University of Colorado Boulder, 2021) B.Sc. in Mechanical Engineering (Texas Tech University, 2017) Dr. Ly's work bridges theoretical innovation and practical application, with projects on soft robotic shape displays, electro-hydraulic rolling wheels, and magnetic sensing for actuator control. His research extends to tactile interfaces, bio-inspired locomotion, and energy-efficient designs. He has secured over $800,000 in grants from ARPA-E and DOE for robotics applications in subterranean excavation and renewable energy harvesting. His 15 most recent publications highlight expertise in soft robotics, nonlinear control, and embedded sensing technologies. Key areas include high-speed actuation, magnetic displacement sensing, and model predictive control for hybrid dynamic systems. While no formal scientific awards are explicitly listed, his grants and patent filings underscore significant contributions to the field. Advised 5 students on projects ranging from wave tanks to HASEL actuator characterization Developed embedded high-voltage power systems and control algorithms Co-invented 3 U.S. patents, including a magnetic sensing method for soft actuators Dr. Ly's work aligns with future goals of applying system design innovations to human-centric challenges, such as improving sensorimotor function for the elderly and enhancing human-robot interaction through tangible interfaces.

• Soft Robotics
• Cyber-Physical Systems
• Control Systems
• +۳ مورد دیگر

Qiang Zhang is an Assistant Professor in the Department of Mechanical Engineering and an Adjunct Faculty in the Department of Chemical and Biological Engineering at The University of Alabama, College of Engineering. He joined the Mechanical Engineering department in August 2023 after serving as an Advanced Rehabilitation Research and Training (ARRT) Post-Doctoral research fellow at the UNC/NCSU Joint Department of Biomedical Engineering. Dr. Zhang's educational background includes: B.S. in Mechanical Engineering from Wuhan University (2014) M.S. in Mechatronics Engineering from Wuhan University (2017) M.S. in Mechanical Engineering from The University of Pittsburgh (2019) Ph.D. in Biomedical Engineering from The University of North Carolina at Chapel Hill & North Carolina State University (2021) His research focuses on the intersection of robotics, biomechanics, and artificial intelligence to develop wearable robotic devices for rehabilitation. Key areas include biological signal-based neuromusculoskeletal modeling , human motion intent detection , nonlinear and adaptive control , and machine learning-based control . His work leverages surface electromyography and ultrasound imaging for real-time control of wearable robots, aiming to provide personalized assistance for individuals with mobility challenges such as stroke, spinal cord injury, and multiple sclerosis. Dr. Zhang's recent publications demonstrate a strong trend toward integrating advanced machine learning techniques, particularly reinforcement learning and deep learning, with wearable robotics. His research emphasizes closed-loop control systems that adapt to individual users, with applications in ankle, hip, and hand exoskeletons. He has pioneered the use of ultrasound imaging for prosthetic control and human-robot interaction. Among his notable scientific achievements: Finalist for the Best Student Paper Award, IEEE/RAS-EMBS ICORR 2019 UNC/NCSU BME Department Ph.D. Student Research Award 2021 ASME DSCD Rising Star Award 2022 Dean’s Distinguished Dissertation Award, UNC-Chapel Hill 2023 Finalist for the Journal of Biomechanics Award 2023 Diversity Travel Award at ASB 2023 Finalist at the NIDILRR-sponsored Early Career Investigator Symposium 2023 Dr. Zhang mentors graduate students including PhD candidates Yun Chen and Oluwasegun Akinniyi. His research is supported by grants including funding from the SEC Faculty Travel Program and the Office for Research and Economic Development (ORED) at The University of Alabama. He serves as an associate editor for the IEEE RAS EMBS 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2024) and as the ASME DSCD Mechatronics TC Secretary for 2024. He leads the Enhanced NeuroRobotics Autonomy and Biomechanical Engineering (ENABLE) Lab, which brings together expertise in robotics, biomechanics, neuromuscular modeling, and artificial intelligence to create effective systems for improving quality of life. The lab utilizes multiple robotic platforms including Baxter manipulator, Kinova arm, and quadcopters for control development.

• Wearable Robotics
• Neuromuscular Modeling
• Control Systems
• +۶ مورد دیگر

Professor Ingvars Birznieks is a Senior Research Fellow at the Department of Physiology, School of Medical Sciences, UNSW Medicine in Sydney, where he leads the Tactile Research Group at Neuroscience Research Australia (NeuRA). Previously, he held an academic position as Senior Lecturer (Physiology) at the School of Science and Health, Western Sydney University from 2011 to 2014. Dr. Birznieks is a sensory neurophysiologist specializing in sensory information encoding mechanisms, with research spanning tactile perception, neural coding, and bionic applications. His work integrates neuroscience, biomedical engineering, and clinical rehabilitation to understand how touch receptors encode information and how this knowledge can be applied to develop advanced prosthetics and rehabilitation technologies. His research program covers tactile receptors and sensorimotor control of the human hand, with applications in stroke rehabilitation, diabetic neuropathy, and bionic hand development. His recent publications reveal a strong focus on neural coding mechanisms in tactile perception, particularly the burst gap code for frequency perception, friction sensing mechanisms, and intensity coding. These studies consistently bridge fundamental neuroscience with practical applications in bionics and rehabilitation. His interdisciplinary approach connects neurophysiological findings with engineering solutions for artificial touch. Dr. Birznieks has secured significant grant funding, including ARC Discovery Projects and NHMRC Ideas Grants totaling over $2.5 million, supporting research on neural coding, sensorimotor control, stroke rehabilitation, and bionic technologies. His current major project 'The secret of tiny hand movements to feel and manipulate objects' (ARC DP230100048) investigates how humans use micro-movements to extract tactile information during object manipulation. He actively supervises students across neuroscience, biomedical engineering, and computer science disciplines, with recent publications featuring undergraduate and graduate students as first authors. His research group maintains collaborations with institutions in France and Sweden, providing international research opportunities for students. Dr. Birznieks' laboratory has developed unique non-invasive mechanical stimulation technology that allows precise control of neural communication at the single neuron level, enabling unprecedented investigation of how spiking activity influences perceptual experience.

• Sensory physiology
• Touch perception
• Tactile sensing
• +۱۱ مورد دیگر

R Prasanth Kumar is a Professor at the Indian Institute of Technology Hyderabad in the Department of Mechanical & Aerospace Engineering. His academic journey began with a BTech from NBKRIST (affiliated to SVU) in 2000, followed by MTech (2002) and PhD (2007) from IIT Kharagpur. Education : BTech (2000), MTech (2002), PhD (2007) His research focuses on Robotics , Multibody Dynamics , Control Systems , and applications of Machine Learning (particularly Reinforcement Learning ) to dynamic systems. His work explores locomotion mechanics, trajectory generation, and control strategies for biped and quadruped robots. Recent publications highlight advancements in parallel mechanisms for quadruped robots, underactuated biped control , and reinforcement learning for balancing tasks. These studies span gait cycle optimization , dynamic stability , and real-time trajectory planning under constraints. He leads the collaborative UAV payload maneuvering project (2021-) and contributes to the Autonomous Passenger Drone initiative (2019-). His patents cover innovations in robot walking stability , flexible manipulators , drones , and airborne vacuum cleaners .

• Robotics
• Multibody Dynamics
• Control Systems
• +۲ مورد دیگر

Bechir Tabia is a DPhil student at the Oxford Robotics Institute, working under the supervision of Prof. Perla Maiolino in the Soft Robotics Lab. His affiliation with Wadham College complements his academic pursuits. He combines his doctoral studies with a full-time role as a lead control system software engineer at UKAEA's RACE department, focusing on nuclear robotics. University: University of Oxford School: Wadham College Department: Oxford Robotics Institute Academic Rank: Researcher Research Interests: Bechir specializes in soft robotics and biomimetics, applying these principles to develop robots capable of navigating challenging environments and performing dexterous manipulations. His work bridges theoretical research with practical applications in underwater and nuclear robotics. Professional Background: Prior to his DPhil, Bechir spent seven years in the industry as a control system software engineer. Notably, he worked on the IBubble underwater autonomous remote camera for divers and contributed to robotic nuclear decommissioning projects at Oxford Technologies. Currently, he leads the control system design for the Mascot 6 remote handling system at UKAEA, enabling nuclear fusion reactor maintenance.

• Soft Robotics
• Biomimetics
• Robotics
• +۳ مورد دیگر

Edouard Laroche is a Professor of Automation (CNU 61 section) at the University of Strasbourg, where he has been teaching since 2008. He currently serves as Deputy Director of the Faculty of Physics and Engineering since 2016 and as Training Quality Officer for the Vice President of Training and Success Pathways since 2021. His academic journey includes being a Lecturer at the Louis Pasteur University of Strasbourg from 2000 to 2008, and he earned his Doctorate from ENS Cachan in 2000 with a thesis on multi-model methodologies for the identification and robust control of asynchronous machines. His educational background includes: Engineer from the National School of Electricity and Mechanics of Nancy DEA PROTEE in 1994 Former student of ENS de Cachan/Paris Saclay Associate Professor of Electrical Engineering in 1995 DEA in science and technology teaching (1996) Authorized to direct research in 2007 with HDR on 'Identification and Robust Control of Electromechanical Systems' Laroche's research focuses on robust control, modeling and identification, robot control, flexible systems, and control of electrical systems. His primary research area since 2002 has been the robust control of flexible robots, with collaborations with G. Mercère (LIAS, Poitiers) and O. Prot (XLim, Limoges). His work addresses LPV modeling and identification of series manipulators with flexibilities, robust control across workspace, and identification and control of cable parallel robots. He has secured funding from Région Grand-Est, FEDER, CNRS PEPS IDRAC (2010-2011), and Preciput-ANR (2009). His recent publications primarily focus on university pedagogy, examining student working methods development, online teaching enhancement, cross-curricular teaching implementation, curriculum redesign, and innovative assessment methods. These works reflect his dual commitment to advancing control systems research and improving educational practices in higher education. Notable scientific contributions include organizing major conferences: CDC 2019 (1600 participants) as local organization lead, IFAC World Congress 2017 (3000 participants) as registration manager, and ECC'14 as local organization lead. He also served as animator for the 'Methods and Tools for Synthesis and Robustness Analysis' working group of the GDR MACS from 2007 to 2014. His teaching spans multiple programs: Faculty of Physics and Engineering: Automatic Control, Methodologies of University Work Telecom-Physics-Strasbourg: Continuous Automatic Control, Sustainable Engineering, Robust Control Bachelor of Science in Health Sciences: Methodologies of University Work Laroche maintains his research laboratory at ICube - AVR (Bd S. Brant, BP 10413, F-67412 Illkirch cedex) and actively offers doctoral thesis topics and internships in automation and control systems.

• robust control
• modeling and identification
• robot control
• +۴ مورد دیگر

Sebastian Kerz is a Researcher at the Chair of Automatic Control Engineering, Technical University of Munich (TUM), with a focus on learning-based and data-driven control systems. He has been affiliated with TUM since 2019, previously working at the University of Toronto and Technische Universität Darmstadt. Education: Master of Science in Electrical Engineering and Information Technology (2015–2019) at TU Darmstadt, specializing in Automation Systems. Bachelor of Science in Electrical Engineering and Information Technology (2011–2015) at TU Darmstadt. Graduate Student at the University of Toronto's Department of Electrical & Computer Engineering (2017–2018). His research centers on Data-driven Stochastic Model Predictive Control (MPC) , integrating machine learning with control theory for systems in uncertain environments. Applications span power grids, building climate systems, and quadrocopters. He also explores robotic grasping in industrial recycling and Koopman operator learning for stable nonlinear systems. Recent publications highlight advancements in adaptive stochastic MPC, uncertainty sampling, and safe control under data imperfections. His work bridges theoretical control frameworks with practical implementations, emphasizing robustness and efficiency. At TUM, Kerz contributes to student supervision and project guidance, particularly in control and learning domains. He is available for thesis supervision and collaborative research endeavors.

• Control Systems
• Machine Learning
• Stochastic Control
• +۴ مورد دیگر

Fangzhou Liu is currently a Professor at the Research Institute of Intelligent Control and Systems, School of Astronautics, Harbin Institute of Technology, China (2022–Present). He previously held a postdoctoral position (2019–2022) and completed his PhD (2014–2019) at the Chair of Automatic Control Engineering, Technische Universität München, Germany. His academic journey includes a visiting PhD stint at the University of Groningen (2018) and degrees from Harbin Institute of Technology: M.Sc. (2012–2014) and B.Sc. (2008–2012) in Control Science and Engineering. Research Interests: Focus on control of complex systems, including diffusion processes over complex networks, opinion dynamics, networked control systems, model predictive control, and sliding mode control. Key themes: large-scale systems, dynamic optimization, and robust control. Publication Trends: Recent works emphasize robotic control (reinforcement learning, barrier functions), switched systems (parameter estimation, identification), and epidemic modeling (deep transfer learning for forecasting). Collaborations with Martin Buss and others span robotics, control algorithms, and optimization. Awards: PhD Award 2019 of the Department of Electrical and Computer Engineering Labs & Teams: Affiliated with the Chair of Automatic Control Engineering (Technische Universität München) and Harbin Institute of Technology’s Research Institute of Intelligent Control and Systems. Collaborates on projects involving intelligent control, robotics, and networked systems.

• Control Systems Engineering
• Networked Control Systems
• Diffusion Processes
• +۴ مورد دیگر

Giuseppe Oriolo is a Full Professor of Automatic Control and Robotics at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome, where he has been since 1994. He coordinates the DIAG Robotics Lab and has held teaching positions at the University of Siena, University of Cassino, and Roma Tre. Oriolo was a Visiting Scholar at the University of California, Santa Barbara, and serves on various editorial and program committees, including IEEE Transactions on Robotics and international conferences like ICRA and IROS. Education : Ph.D. in Systems Engineering (1992), Sapienza University of Rome His research focuses on robotics and control theory, particularly in robot control, motion planning, trajectory optimization, redundant robotic systems, control of underactuated systems, sensor-based localization, navigation for mobile robots, humanoid robots, and visual servoing. He has over 230 publications and contributes to advanced robotics frameworks. Recent publications highlight his work on Model Predictive Control (MPC) for humanoids and mobile robots, emphasizing robustness, singularity-free trajectories, and decentralized cooperation. His research addresses dynamic constraints, feasibility, and real-time adaptation in complex environments. Scientific Awards : 2017 IEEE Fellow, 2020 IEEE Robotics and Automation Magazine Best Paper Award, 2016 ANTS Best Paper Award, 2024 Human-Friendly Robotics Best Paper Award Oriolo's teaching includes Control Systems at Sapienza, with past roles in courses like Adaptive Systems, Nonholonomic Control, and Underactuated Robots. He has mentored numerous advisees through publications and lab activities. He leads the DIAG Robotics Lab, fostering innovation in humanoid robots, autonomous systems, and control theory.

• Robotics
• Control Systems
• Autonomous Mobile Robots
• +۳ مورد دیگر

He David Zhang is an Assistant Professor of Computer Science at Governors State University and a Postdoctoral Associate at Virginia Commonwealth University (VCU) Robotics Lab. His research focuses on computer vision, robotics, and assistive technologies for visually impaired individuals. Ph.D. in Electrical & Computer Engineering, University of Arkansas at Little Rock (2018) B.S. in Computer Science & Technology, Chinese University of Mining and Technology at Beijing (2009) Study Certificate in Computer Architecture, University of Chinese Academy of Sciences (2010) Dr. Zhang's research integrates vision-based algorithms with inertial sensors to create robust systems for indoor localization, 3D mapping, and human-robot interaction. Key projects include Smart Cane, CoRobotic Cane (CRC), Wearable Robotic Object Manipulation Aid (W-ROMA), and Quadrupedal Human-Assistive Robotic Platform (Q-HARP), which aim to enhance mobility for the visually impaired and elderly populations. His recent publications emphasize improving Visual-Inertial Odometry (VIO) accuracy through depth uncertainty modeling and hybrid perspective-n-point methods. He has contributed to benchmark datasets like VCU-RVI and explored SLAM applications in real-time assistive robotics. Awards include Best Conference Paper honors and competitive recognitions in IROS and 3MT competitions. Best Conference Paper Award, IEEE HSI 2019 2nd Place, IROS 2020 UZH-FPV VIO Competition System Engineering Award for Excellent Ph.D. Student, UA Little Rock (2018)

• Computer Vision
• Robotics
• SLAM
• +۳ مورد دیگر

Hiroyuki Ishii is a Professor at Waseda University's School of Creative Science and Engineering, Faculty of Science and Engineering. With over 15 years of academic experience at Waseda University, he was promoted to full Professor in 2022 after serving as Associate Professor from 2016-2022. His research spans robotics, control systems, and mechanical engineering, with particular expertise in soft robotics and continuum manipulators. His educational background includes: Ph.D. from Waseda University (2004-2007) Master's degree from Waseda University Graduate School of Science and Engineering (2002-2004) Bachelor's degree from Waseda University School of Science and Engineering (1998-2002) Professor Ishii's research focuses on Robotics and Intelligent Mechanics and Mechanical Systems , with particular emphasis on soft robotics, continuum manipulators, and human-robot interaction. His work bridges theoretical control systems with practical applications in medical robotics, rehabilitation devices, and inspection systems. He has pioneered novel approaches in pneumatic actuation, growing robots, and bimanual continuum robot control, demonstrating exceptional creativity in solving complex mechanical challenges. His recent publications reveal a strong trend toward medical applications of robotics, particularly in surgical assistance and rehabilitation. There's also significant work on soft growing robots and continuum manipulators with innovative actuation mechanisms. His research shows increasing interdisciplinary collaboration, spanning mechanical engineering, computer vision, and biomedical applications. His notable awards include: Best Paper Award Finalist at IEEE ICMA2024 The Robotics Society of Japan 3rd Outstanding Research and Technology Award (2022) Minister of Education, Culture, Sports, Science and Technology Young Scientists Award (2018) Advanced Robotics Best Paper Award (2015) ICRA2014 Best Cognitive Robotics Paper Award Finalist Professor Ishii actively participates in research committees and professional societies, serving as Director of the Robotics Society of Japan since 2024. His research has received substantial funding through competitive grants, supporting his work on innovative robotic systems with practical applications in healthcare, infrastructure inspection, and human-robot collaboration. His laboratory develops specialized robotic systems including small mobile robots for animal interaction, soft growing robots, and continuum manipulators for surgical applications. The team combines mechanical design expertise with advanced control algorithms to create robots capable of operating in complex environments.

• Robotics
• Control Systems
• Mechanical Engineering
• +۴ مورد دیگر