جستجوی استاد راهنمای دانشگاهی

Ferreira Joao Carlosمشاهده پروفایل

استاد

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +۶ مورد دیگر

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

Guoquan Huangمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Robot Learning
• +۵ مورد دیگر

Guoquan Huang is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware. He holds a B.Eng. in Automation from the University of Science and Technology, Beijing (2002), and M.Sc. and Ph.D. degrees in Robotics from the University of Minnesota (2009 and 2012). Prior to his current role, he was a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on robotics, computer vision, and autonomous systems, emphasizing probabilistic perception, estimation, and control for ground, aerial, and underwater vehicles. He leads the development of the OpenVINS platform for visual-inertial estimation and has contributed to advancements in SLAM (Simultaneous Localization and Mapping), sensor fusion, and multi-robot coordination. Education: B.Eng in Automation (Electrical Engineering), University of Science and Technology, Beijing, 2002 M.Sc. in Robotics, University of Minnesota, Twin Cities, 2009 Ph.D. in Robotics, University of Minnesota, Twin Cities, 2012 His research interests span robotics, computer vision, and autonomous systems , with a focus on: Visual-inertial navigation and SLAM Sensor fusion (LiDAR, IMU, camera) Autonomous vehicle control and safety Multi-robot cooperative localization His recent publications (2023–2025) emphasize robust algorithms for navigation in GPS-denied environments, real-time sensor calibration, and dataset development for aerial visual localization. He has pioneered techniques like decoupled error-state estimation and consistent parallel frameworks for SLAM. Labs/Teams: Leads the development of the OpenVINS research platform, focusing on visual-inertial state estimation. Collaborates on projects involving human-swarm interactions and resilient ground vehicle navigation.

• Model-based Systems Engineering
• Safety-Critical Systems
• Systems Engineering
• +۳ مورد دیگر

Dr. Siyuan Ji is a Reader in Model-based Systems Engineering (MBSE) at Loughborough University, serving as Deputy Head of the Manufacturing, Systems & Management Academic Community and Deputy Director of the Doctoral Training Centre in MBSE. He previously held a Senior Lecturer position in Systems Engineering at the University of York, where he led the MSc Programme in Safety-Critical Systems Engineering. His academic journey includes a PhD and MSc in Physics from the University of Nottingham, followed by research roles in model-based systems engineering at Loughborough University. His research focuses on advancing model-based techniques for systems engineering, particularly in safety-critical systems, formal methods, and complex system design. He has contributed to areas such as hazard management (e.g., BSafeML framework), response time analysis in real-time systems, and model synchronization for requirements engineering. His work bridges theoretical foundations with practical applications in automotive systems, embedded software, and educational technology. Dr. Ji holds the title of Fellow of the Higher Education Academy and has published extensively on topics ranging from quantum technology reporting to conversational tutoring systems. His research emphasizes interdisciplinary collaboration, evident in projects like the EPSRC-funded analysis of vehicles as complex systems. He actively contributes to both academic and industrial advancements in systems engineering methodologies and education innovation. His professional roles include managing doctoral training programs, overseeing academic communities, and advancing systems engineering education. Collaborations span industry partnerships and international academic networks, reflecting his commitment to impactful research and training the next generation of systems engineers.

• Soft Robotics
• Actuators
• Self-Healing Materials
• +۴ مورد دیگر

Julie Legrand is an Assistant Professor in the Mechanical Engineering department at Eindhoven University of Technology , affiliated with the Group Van de Molengraft. Her work focuses on soft robotics , self-healing materials , and medical robotics applications . She designs actuators and sensors for adaptive robotic systems, emphasizing resilience through self-healing mechanisms and embodied intelligence. She teaches courses including Control of a Flexible Robot System , Haptics and Soft Robotics , and Robot-Arm , reflecting her expertise in both theoretical and applied robotics. Her research spans actuator design , material science integration , and minimally invasive surgical robotics , with notable contributions to self-healing actuator validation and continuum robot end-effectors for surgical applications. Legrand collaborates internationally on topics like shape memory alloys and anisotropic materials , and her work has been featured in media for breakthroughs in self-healing polymer limitations in soft robots. She actively contributes to the Medical Robotics research theme at TU/e, advancing interdisciplinary approaches to robotic systems in healthcare.

Sharad Mehrotraمشاهده پروفایل

استاد

• Data Management
• Distributed Systems
• Database as a Service
• +۷ مورد دیگر

Sharad Mehrotra is a Distinguished Professor at the University of California, Irvine (UCI), leading the Center for Emergency Response Technologies (CERT) and directing the NSF-funded RESCUE project. He previously served at the University of Illinois, Urbana-Champaign, and holds a Ph.D. from the University of Texas at Austin (1993). His research focuses on data management, IoT systems, privacy-preserving technologies, and smart spaces, with contributions to frameworks like TIPPERS and MARS. Education: Ph.D., Computer Science, University of Texas at Austin, 1993 Research Interests: His work bridges database systems, security, and IoT, emphasizing privacy in smart environments. Notable projects include sentient space technologies for disaster response, cryptographic methods for encrypted data queries, and semantic IoT integration. Recent efforts address privacy in multi-owner data systems and resilient community water infrastructure. Awards & Recognition: ACM Fellow (2024) SIGMOD Best Paper (2001), DASFAA Best Paper (2004) NAVWAR Innovation Award (2021) Outstanding Graduate Mentor (2005) Grants & Leadership: As RESCUE PI, he managed $12.5M NSF funding, developing crisis-response software deployed by emergency agencies. Collaborations include the Cal-IT2 institute (UCSD/UCI) and the US Navy’s TIPPERS platform. He co-leads initiatives like the NSF Civic Innovation Challenge for disaster resilience in aging communities. Labs & Teams: Directs UCI’s Information Systems Group and CERT, fostering interdisciplinary research with 60+ members. His teams produce open-source tools (e.g., SEMIoTIC, PrivacySphere) and engage in global partnerships via Fulbright Visiting Scholar programs.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Danesh Tarapore is an Associate Professor at the University of Southampton specializing in robotics and AI. He focuses on human-robot interaction, swarm intelligence, and autonomous systems. His current research involves developing resilient robotic teams and optimizing learning algorithms for constrained environments. He supervises 6 PhD students in the iPhD MINDS and Computer Science programs. Dr. Tarapore's work bridges theoretical advancements with practical applications in autonomous navigation, multimodal dataset creation, and quality-diversity optimization. His publications span conferences like HRI and journals in robotics and AI. He collaborates with institutions like the University Hospital Southampton and the Boldrewood Innovation Campus. Research Interests: Human-robot collaboration, swarm systems, machine learning, and adaptive control Key Contributions: HRI-SENSE dataset, evolutionary subset selection algorithms, forest navigation frameworks Grants and Funding: Active projects in multi-agent systems and resilient robotics Dr. Tarapore maintains active roles in the robotics community through conference participation and interdisciplinary collaborations.

• Neural control of movement
• Infant development
• Motor control
• +۵ مورد دیگر

Beth Smith, PT, DPT, PhD is an Associate Professor of Pediatrics and Biokinesiology & Physical Therapy at the University of Southern California. She directs the Infant Neuromotor Control Laboratory, where she leads research on neural control of movement during infancy and develops interventions for infants with or at risk for developmental delay. Dr. Smith's research focuses on several critical areas in pediatric development: Neural mechanisms underlying infant motor development Application of wearable sensor technology for objective movement measurement Early identification of developmental delays through quantitative analysis Evaluation of intervention effectiveness for at-risk infants Cross-cultural studies of infant development in diverse settings including rural Guatemala Her work demonstrates a strong integration of technology and clinical practice, with recent publications highlighting innovative approaches to measuring infant movement in natural environments. Dr. Smith's research on algorithmic detection of developmental disabilities using wearable sensors represents a significant advancement in early identification methods. Her studies on telehealth administration of developmental assessments have gained particular relevance following the COVID-19 pandemic, potentially expanding access to early intervention services globally. As director of the Infant Neuromotor Control Laboratory, Dr. Smith oversees a research program that bridges neuroscience, engineering, and clinical practice to improve outcomes for infants with developmental challenges. Her work has important implications for developing evidence-based interventions that can be implemented across diverse healthcare settings.

• Robotics
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Pawel Ladosz is a Lecturer in Engineering Systems for Robotics at the Department of Mechanical and Aerospace Engineering, The University of Manchester. His research focuses on applying machine learning and computer vision to mobile robots, particularly in extreme environments such as total darkness or cluttered spaces. He is actively involved in developing autonomous navigation systems, wireless signal mapping, and high-level decision-making for robotic swarms. He teaches courses including Robotic Systems Design Project and Autonomous Mobile Robots. Education: PhD in Establishing and Optimising Unmanned Airborne Relay Networks (Loughborough University, 2014–2019) MEng in Aerospace Engineering (The University of Manchester, 2010–2014) Research Interests: Ladosz’s work emphasizes reinforcement learning for robotics, vision-based autonomous systems, and exploration in challenging environments. His projects often intersect with UN Sustainable Development Goals, contributing to innovations in robotic autonomy and sensor networks. Awards: He received the 2nd Autonomous Flying Technology Competition award in 2021, recognizing his contributions to autonomous flight systems. His research has also led to the establishment of the Centre for Robotic Autonomy in Demanding and Long-Lasting Environments (CRADLE), fostering cross-disciplinary collaborations. Grants & Projects: As Principal Investigator in the Aerospace Engineering initiative (2010–2035), he explores UAV communication networks and trajectory planning. His work addresses urban environment challenges, including relay positioning and signal prediction. Labs/Teams: Ladosz contributes to CRADLE, advancing robotic autonomy in extreme scenarios. His lab focuses on integrating AI and robotics for real-world applications.

Shuangquan Wangمشاهده پروفایل

استادیار

• Mobile & Wearable Computing
• Activity Recognition
• Smart Health
• +۳ مورد دیگر

Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.

• Computer Science
• Electrical Engineering
• Signal Processing
• +۳ مورد دیگر

Guo Li is affiliated with the Beijing Institute of Technology, School of Management and Economics. Their research spans computer vision, optimization algorithms, signal processing, and machine learning. Collaborations include work on image super-resolution, sensor networks, and energy systems. Publications are distributed across journals like Comput. Electron. Agric. , IEEE Trans. Circuits Syst. , and Entropy . Research interests focus on computational methods for image processing, algorithm design, and interdisciplinary applications in agriculture and energy. Recent work emphasizes lightweight neural network architectures, sparrow search algorithms, and thermodynamic modeling in materials science. Notable contributions include advancements in citrus fruit detection, fatigue life assessment of superalloys, and load forecasting techniques. Active in international conferences such as CVPR, ICC, and NSDI, with a strong publication record since 1998.

Marjan Alaviمشاهده پروفایل

استادیار

• Fault Diagnosis
• Prognosis
• Power Electronics
• +۳ مورد دیگر

Dr. Marjan Alavi is an Assistant Professor at McMaster University's W Booth School of Engineering Practice and Technology, affiliated with the Mechanical Engineering department as an Associate Member. She holds a Professional Engineer (P.Eng.) license in Ontario and has over 15 years of academic and industrial experience in electrical engineering. Her research focuses on model-based and data-driven approaches for fault diagnosis, prognosis, and fault-tolerant control in hybrid systems, with applications in power electronics, energy systems, and smart infrastructure. Education: B.Sc. (2004) from K.N. Toosi University of Technology, M.Sc. (2007) from Sharif University of Technology, Ph.D. (2014) from Nanyang Technological University (Singapore), and a Postdoc (2015) at the University of Toronto's Energy Systems Group. Teaching: Instructs courses on Real-Time Systems (SEP 6ES3, SFWRTECH 4ES3), Smart Cities and Communities (SMRTTECH 4SC3), integrating real-world engineering challenges with theoretical frameworks. She emphasizes hands-on learning through remote labs and experiential projects. Professional Contributions: Serves as IEEE Toronto Section Executive Member, Technical Reviewer for IEEE Transactions on Industrial Electronics, and Vice Chair of IEEE Industrial Applications Society (2015). Founded Intelligent Diagnosis Corporations, a Canadian startup focused on research and innovation in diagnostics technologies. Key Projects: Developed fault diagnosis strategies for electro-hydraulic actuators, vehicle-mounted infrastructure monitoring systems, and remote laboratory platforms for emergency traffic control. Research spans predictive maintenance, smart city technologies, and railway systems certification benefits. Awards: Recipient of the Singapore International Graduate Award (SINGA) 2010. Recognized for her work in bridging academic research with industrial applications, particularly in enhancing system reliability through advanced control methodologies.

Dimitra Georgiadouمشاهده پروفایل

دانشیار

• Flexible Nanoelectronics
• Green Manufacturing
• Neuromorphic Computing
• +۳ مورد دیگر

Dr Dimitra Georgiadou is an Associate Professor at the University of Southampton's School of Electronics and Computer Science, leading the Flexible Nanoelectronics Lab. She is a UKRI Future Leaders Fellow and serves as Deputy Impact Champion in the UKRI Centre for Doctoral Training in Machine Intelligence for Nanoelectronic Devices and Systems (MINDS-CDT). Her research focuses on green manufacturing of flexible nanoelectronics, photonic synapses for neuromorphic computing, and solution-processed non-toxic materials. Key projects include developing Pb-free perovskite photoelectric memristors and radiofrequency Schottky diodes for IoT applications. Education: PhD in Chemical Engineering/Organic Electronics from the National Technical University of Athens (NTUA). Prior roles include Industrial Fellow at Imperial College London and Marie Skłodowska-Curie Fellow. Research spans nanoscale optoelectronic devices, energy harvesting, and flexible substrates. Publications highlight advancements in photodetectors, memristors, and neuromorphic systems, with recent work on self-powered organic photodetectors and γ-ray-induced memristor effects. She has over 40 peer-reviewed articles, including in Laser & Photonics Reviews , Optica , and Advanced Electronic Materials . External roles include editorial positions at Frontiers in Nanotechnology and IEEE Sensors Letters , and membership in the EPSRC Peer Review College. She actively participates in conferences, delivering invited talks on nanosciences, flexible electronics, and neuromorphic devices.

Soufiene Djahelمشاهده پروفایل

استاد

• Connected and Autonomous Vehicles (CAVs)
• Unmanned Aerial Vehicles (UAVs)
• Cyber Security
• +۷ مورد دیگر

Soufiene Djahel is a Professor at the Centre for Future Transport and Cities (CFTC) at Coventry University, UK. His research focuses on connected and autonomous vehicles (CAVs), unmanned aerial vehicles (UAVs), cyber security, and smart cities. He holds a PhD in Secure Routing and Medium Access Protocols from Université des Sciences et Technologies de Lille (2010), and has held academic positions including Senior Lecturer at the University of Huddersfield and Manchester Metropolitan University. His research interests include CAV coordination protocols, cyber-physical security solutions, and intelligent transportation systems. Djahel leads projects such as the £1.2M AeroPharma Logistics initiative and has secured funding from the Newton Fund and JSPS. He is a recipient of the 2021 JSPS Invitational Fellowship and has published extensively in IEEE journals and conferences. Current projects explore UAVs-as-a-service, digital twins for CAVs, and B5G/6G for smart infrastructure. He advises PhD students on topics like AI-based threat mitigation and transport electrification. Djahel also serves as an external examiner and editorial board member for journals like IEEE Transactions on Intelligent Transportation Systems.

Omprakash Gnawaliمشاهده پروفایل

دانشیار

• Internet of Things
• Smart Devices
• Wireless Sensor Networks
• +۳ مورد دیگر

Omprakash Gnawali is an Associate Professor in the Department of Computer Science at the University of Houston, with expertise in Internet of Things, wireless sensor networks, and artificial intelligence. His research focuses on advanced networking protocols, mobility analysis, and safety monitoring systems. Postdoctoral work at Stanford University PhD in Computer Science from University of Southern California Masters and Bachelors from Massachusetts Institute of Technology His research interests include Ultra-Wideband (UWB) localization, network protocol design, edge computing for monitoring systems, and mobile sensor networks. He leads the Networked Systems Laboratory , where he develops frameworks like the Collection Tree Protocol and CodeDrip for efficient data dissemination. Recent publications highlight trends in UWB-based safety monitoring, routing optimization in dual-radio networks, and deception detection in cybersecurity. He has secured NSF Student Travel Grants for ACM SenSys conferences in 2016 and 2017. Scientific Awards NSF Student Travel Grant (2017) NSF Student Travel Grant (2016) He actively mentors students in research projects and teaches courses such as Research Methods in Computer Science and Computer Networks . His service roles include Technical Program Committee memberships and chairing the TinyOS Network Protocol Working Group.

Ramana Vinjamuriمشاهده پروفایل

دانشیار

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +۴ مورد دیگر

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.