جست‌وجوی استاد و پژوهشگر

Jin Woo Songمشاهده پروفایل

دانشیار

• Navigation and Sensor Systems
• Control Theory
• Estimation Theory
• +۷ مورد دیگر

Jin Woo Song is an Associate Professor in the Department of Artificial Intelligence and Robotics at Sejong University's School of Intelligent Mechatronics Engineering. He has held academic positions at Seoul National University and Hoseo University prior to joining Sejong University in 2017. His professional experience also includes serving as CTO/COO at Microinfinity Co., Ltd. from 2004-2014. Currently, he holds multiple leadership roles including Director of Sejong Enterprise Support Foundation, Director of SW-AI Convergence Center, and Provost of Sejong University. Dr. Song received his B.S., M.S., and Ph.D. in Control and Instrumentation Engineering/Electrical Engineering from Seoul National University. His educational background laid the foundation for his expertise in navigation systems and control theory. Dr. Song's research focuses on navigation and sensor systems, control theory, and estimation theory with applications in unmanned systems. His work spans MEMS inertial sensor technologies, Kalman filtering techniques, optimal and robust control theories, and fault detection algorithms. His research has significant applications in unmanned aircraft systems, autonomous vehicles, robotics, and military intelligence systems. His recent publications (2023-2025) demonstrate continued innovation in navigation systems, with emphasis on fault tolerance, sensor fusion, and improved positioning accuracy in challenging environments. His work shows strong trends toward integrating multiple sensor modalities and developing robust algorithms for real-world navigation challenges. Best student paper award, NAECON2000 (IEEE, National Aerospace and Electronics Conference), USA, 2000 Ministry Award, 'IT R&D Engineer of the Year', Ministry of Information and Communication, Republic of Korea, 2005 Best Presentation Award, ICASS 2015, 2015 Best Paper Award, KIEE Conference, 2019 Distinguished Teaching Award, Sejong University, 2021 Distinguished Research Award, Sejong University, 2022 Distinguished Professor, Sejong University, 2024 Dr. Song has served as Editor-in-Chief for ICROS journal and holds editorial positions for multiple prestigious journals including the International Journal of Aeronautical and Space Sciences. He has been actively involved in numerous research grants and industry collaborations, including a recent academic-industrial partnership with Hanwha Systems. As Vice-President of The Institute of Control, Robotics and Systems (ICROS) and various other professional organizations, he plays a significant role in shaping research directions in his field. He leads the Intelligent Navigation and Control Systems Lab at Sejong University, where his team develops cutting-edge navigation sensors and systems, optimal controllers for autonomous systems, and embedded systems for reliable navigation and control. The lab's work has direct applications in unmanned aircraft systems, autonomous vehicles, robotics, and military intelligence systems.

He David Zhangمشاهده پروفایل

استادیار

• Computer Vision
• Robotics
• SLAM
• +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)

• MEMS Sensors and Actuators
• Semiconductor Materials
• Nanostructure Materials
• +۵ مورد دیگر

Dr. Quan Nguyen serves as a Lecturer in Mechatronics at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS). His academic credentials include: PhD in Electrical Engineering, Griffith University (2021) MSc in Mechanical & Aerospace Engineering, University of Missouri (2017) Dr. Nguyen's research centers on MEMS sensors and actuators, semiconductor and nanostructure materials, and sensor networks for smart cities and harsh environments. He has authored over 30 high-impact journal publications in top-tier Scopus-indexed journals, demonstrating significant contributions to advanced sensing technologies. Analysis of his 2021-2025 publications reveals a consistent focus on piezotronic and photovoltaic effects in semiconductor heterojunctions (particularly 3C-SiC/Si and GaN systems), wafer bonding technologies for MEMS integration, and highly sensitive tactile/position detection systems. His work achieves record sensitivities (e.g., 70680 strain sensitivity, 603.65 mV/mm position sensitivity) through innovative material combinations and device architectures, with direct applications in harsh environment monitoring and smart infrastructure. No specific scientific awards are documented in available sources. Dr. Nguyen provides technological consultancy to industry partners including Dentroid, Geoinventions, and Samsung in Australia and Korea. He teaches Embedded Mechatronics Studio (41070) and maintains active research collaborations across semiconductor sensor development.

Juan Jesús García Domínguezمشاهده پروفایل

استاد

• Electronics Engineering
• Sensor Technology
• Biomedical Engineering
• +۳ مورد دیگر

Juan Jesús García Domínguez is a Professor in the Department of Electronics at the Universidad de Alcalá. His primary research focuses on sensor systems, wearable technology, and their applications in healthcare and smart environments. He leads the GEINTRA research group, dedicated to Electronic Engineering Applied to Intelligent Spaces and Transport. Dr. García Domínguez holds a PhD from Universidad de Alcalá (2006) with a thesis on infrared obstacle detection in railway environments. His research interests include biomedical sensors, inertial measurement units (IMUs), indoor localization systems (BLE/UWB), and elderly care technology. He has pioneered work on non-invasive behavioral monitoring, gait analysis, and wearable devices for vulnerable populations. Recent projects involve developing systems for activity recognition, fall detection, and telemedicine applications. Key contributions include the FrailWear wearable IoT device, BLE-based behavioral analytics frameworks, and multi-sensory systems for long-term patient monitoring. His work bridges electronic engineering with healthcare, emphasizing practical solutions for aging-in-place and smart transportation safety. Teaching includes digital electronics and active learning methodologies in engineering education. Dr. García Domínguez has published extensively on topics like acoustic positioning systems, IMU calibration algorithms, and NILM techniques for energy management. His lab focuses on translating sensor data into actionable insights for medical and environmental applications.

Mohammad Taher Pilehvarمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Lexical Semantics
• Semantic Representation
• +۳ مورد دیگر

Mohammad Taher Pilehvar is a Senior Lecturer at Cardiff University's School of Computer Science and an Affiliated Lecturer at the University of Cambridge's Department of Theoretical and Applied Linguistics. His research focuses on lexical semantics and semantic representation in NLP, particularly in word sense disambiguation and embedding space isotropy. He has held academic positions at Tehran Institute for Advanced Studies (TeIAS) and Sapienza University of Rome, where he completed his PhD under Dr. Roberto Navigli. Key Research Interests Natural Language Processing (NLP) Lexical Semantics & Semantic Representation Contextualized Embeddings & Word Sense Disambiguation Knowledge-Infused Language Models His recent publications (2021-2013) demonstrate expertise in semantic similarity frameworks, metonymy resolution, and biomedical NLP applications like PheneBank. He supervised PhD students including Jose Camacho-Collados and Costanza Conforti, now at top institutions. Awards 2023 AIJ Prominent Paper Award 2014 Sapienza Starting Grant 2015 Best PhD Paper (Sapienza University) He has collaborated with the University of Cambridge's Language Technology Lab on MRC-funded projects and co-organized SemEval tasks. Current roles include editorial board membership at JNLE and co-chairing ACL 2025.

Clifton Paul Robinsonمشاهده پروفایل

استادیار

• Wireless Network Security
• Deep Learning-based Security Solutions
• AI-driven Spectrum Sensing
• +۳ مورد دیگر

Clifton Paul Robinson serves as Assistant Professor of Computing and Data Science at Wentworth Institute of Technology while maintaining his research affiliation as Research Associate at Northeastern University's Institute for the Wireless Internet of Things under Professor Tommaso Melodia. He completed his Ph.D. in Cybersecurity at Northeastern in December 2024, building upon his M.S. in Cybersecurity (2020) and B.S. in Computer Science and Mathematics magna cum laude from Bridgewater State University (2018). His educational credentials include: Ph.D. in Cybersecurity, Northeastern University (2024) M.S. in Cybersecurity, Northeastern University (2020) B.S. in Computer Science and Mathematics, Bridgewater State University (2018) Dr. Robinson's research program focuses on the critical intersection of wireless security and artificial intelligence. His pioneering work on DeepSweep enables parallel and scalable spectrum sensing through convolutional neural networks with 98% accuracy while maintaining sub-millisecond inference times. He has made significant contributions to wireless security through his eSWORD framework for emulating jamming attacks and his award-winning TwiNet system that establishes bidirectional links between physical networks and their digital twins. His research addresses fundamental challenges in spectrum management, adversarial signal detection, and secure wireless communications infrastructure. His publication trajectory shows increasing sophistication in digital twin applications for wireless networks, with recent work demonstrating how bidirectional communication between physical and virtual networks can transform spectrum utilization and security monitoring. This research direction has important implications for 5G/6G networks, military communications, and critical infrastructure protection. His scientific recognition includes: Best Paper Award at IEEE GLOBECOM 2024 for TwiNet paper IEEE WCNC Student Travel Grant KCCIS Graduate Fellowship As an educator, Dr. Robinson has served as Instructor of Record for CY 2550 - Foundations of Cybersecurity at Khoury College, developing curriculum that integrates real-world case studies with theoretical concepts. His teaching extends to guest lectures on OSI model layers and cybersecurity ethics. His research has received support through the Global Resilience Institute's Critical Infrastructure Network project funded by the U.S. Department of Energy, and he completed a Signal Analysis Internship at The MITRE Corporation focusing on RF fingerprinting. Dr. Robinson works within Northeastern's Wireless Networks and Embedded Systems (WiNES) Laboratory, collaborating with a multidisciplinary team on cutting-edge wireless security projects. His work with METEOR and Colosseum represents significant advancement in large-scale wireless network emulation capabilities, bridging the gap between theoretical research and real-world implementation.

• Randomized algorithms
• Machine learning
• Big Data
• +۳ مورد دیگر

Devdatt Dubhashi is a Professor at the Data Science and AI 3 department at Chalmers University of Technology. His research spans multiple domains within computer science and data science, with a focus on theoretical foundations and practical applications of algorithms and machine learning. Professor Dubhashi's primary research interests include the design and analysis of randomized algorithms, machine learning for Big Data, and computational biology. His work demonstrates a strong interdisciplinary approach, connecting theoretical computer science with practical applications in diverse fields such as quantum computing, transportation modeling, genomics, and social sciences. His research often bridges the gap between theoretical foundations and real-world implementations, as evidenced by his numerous collaborations across different domains. Dubhashi's recent publications reveal a consistent research trajectory focusing on algorithmic foundations of machine learning, with increasing emphasis on interdisciplinary applications. His work shows significant contributions to bandit algorithms, kernel methods, graph-based learning, and the theoretical understanding of deep learning models. There's also a growing focus on societal implications of AI, as seen in his publications addressing responsible AI development and policy considerations. Among his notable scientific contributions are publications in prestigious venues including ACM, IEEE, and Nature journals, covering topics from fundamental algorithm design to applications in computational biology and social sciences. His work has been cited extensively across multiple disciplines, reflecting its broad impact. Professor Dubhashi actively supervises PhD students and collaborates with researchers across Chalmers and internationally. His research group appears to focus on the intersection of theoretical computer science and practical machine learning applications, with projects spanning quantum computing, transportation modeling, and biological applications.

Ye Hongمشاهده پروفایل

پژوهشگر

• Geoinformatics
• Urban Planning
• Transportation Engineering
• +۳ مورد دیگر

Dr. Ye Hong is a Researcher affiliated with the Institute of Cartography and Geoinformatics at ETH Zürich, specifically within the Department of Geoinformation Engineering. Their work focuses on geospatial data analysis, urban mobility modeling, machine learning applications in transportation systems, and sustainable urban planning. They contribute to open-source tools like Trackintel for mobility analysis and have published extensively on topics such as mobility data synthesis, traffic prediction, and privacy-preserving techniques. Research interests emphasize integrating multi-source geospatial data with deep learning to address challenges in urban sustainability, poverty reduction, and transportation efficiency. Their articles highlight innovations in trajectory generation, uncertainty quantification, and contextual-aware neural networks for spatial-temporal prediction. Dr. Hong’s publications explore the interplay between urban infrastructure, human behavior, and environmental impact. Key themes include accessibility measurement in cities, carbon footprint analysis, and the ethical implications of tracking mobility data. Their work bridges theoretical advancements in geoinformatics with practical applications in urban policy-making and infrastructure design. Notable contributions include frameworks for causal intervention in mobility data, methods to estimate poverty reduction efficiency using remote sensing, and analyses of tracking duration effects on location privacy. These efforts demonstrate a commitment to leveraging geospatial technologies for socially impactful research.

• Communications Engineering
• Positioning Technologies (GNSS)
• Embedded Processor Systems
• +۴ مورد دیگر

Jari Nurmi is a Full Professor at Tampere University's Faculty of Information Technology and Communication Sciences, Department of Electrical Engineering. With over 30 years of experience in academia and industry, he specializes in communications engineering, positioning technologies, embedded systems, and reconfigurable computing. His roles include Director of the national DELTA doctoral training network, head of the European Joint Doctorate A-WEAR program, and coordinator of APROPOS MSCA ITN. He has supervised 32 PhD dissertations and over 160 MSc theses. His research focuses on embedded processor systems, reconfigurable computing, approximate computing, and positioning technologies (especially GNSS receiver architectures). He is actively involved in organizing international conferences like IEEE Nordic Circuits and Systems Conference and serves on editorial boards of three journals. His recent work includes advancements in neural network inference on FPGAs, 5G NR localization, and energy-efficient edge AI for autonomous systems. Nurmi has been recognized for his contributions to conference organization and innovation. His publications span topics such as FPGA optimization, GNSS error modeling, and machine learning for healthcare applications. He leads initiatives like the EWOk dataset compression framework and the Hard SyDR benchmarking environment for GNSS algorithms.

• Optical Sensing
• Photonics
• Laser Dynamics
• +۵ مورد دیگر

Assoc. Professor Yanguang Yu holds a PhD from Harbin Institute of Technology (2000) and has been at the University of Wollongong (UoW) since 2007, currently serving as Associate Professor in the School of Electrical, Computer and Telecommunications Engineering. Her research focuses on optical sensing, photonics, and laser dynamics, with 200+ publications. She is recognized as a World’s Top 2% Scientist (2020–2024). Education: PhD in Instrumentation and Measurement (Harbin Institute of Technology, 1997–2000). Postdoctoral training at Tianjin University (2001–2002) and visiting roles at the University of Pavia (Italy), University of Toulouse (France), and UoW (2004–2005). Research Interests: Optical interferometry, microwave photonic sensing, lidar, precision displacement/velocity measurements, and embedded systems. Recent work includes advancements in self-mixing interferometry, dual optical feedback systems, and machine learning integration. Grants and Funding: Led projects on semiconductor laser dynamics, photonic sensing, and signal processing (e.g., 2010–2025 grants). Active in editorial roles for Optics and Lasers in Engineering and Optics Express . Labs/Teams: Oversees the Signal Processing for Instrumentation and Communications Lab. Supervises PhD/Master’s research in areas like integrated photonic lidar-radar and machine learning for sensing.

Linga Reddy Cenkeramaddiمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Autonomous Systems
• Robotics and Automation
• +۷ مورد دیگر

Linga Reddy Cenkeramaddi is a **Professor** in the Department of Information and Communication Technology at the University of Agder (UiA), Norway. He holds a PhD in Electrical Engineering from NTNU (Trondheim, Norway) and Master’s degrees from IIT Delhi and IIT Chennai. His research focuses on Cyber-Physical Systems (CPS), Autonomous Systems, Robotics, AI/ML, Wireless Sensor Networks, and 5G/6G-enabled technologies. He leads projects such as LUCAT (UAV communication tracking) and INCAPS (Indo-Norwegian collaboration in CPS). **Education**: PhD (NTNU), MSc in Physics (IIT Chennai), MSc in Integrated Electronics (IIT Delhi). **Research Interests**: Energy-efficient wireless communication, autonomous vehicles, edge intelligence, mmWave sensing, and smart cities. **Teaching**: Courses include embedded systems, mmWave sensing, and wireless prototyping. **Key Contributions**: Developed low-power embedded systems, contributed to UAV communication protocols, and pioneered AI-driven healthcare solutions. **Grants**: Co-PI in the INMOST offshore mechatronics project. **Labs/Teams**: Autonomous and Cyber-Physical Systems (ACPS), Electronics and IoT (E&IOT), Intelligent Mechatronics (iTron). His work bridges academia and industry, impacting healthcare, smart agriculture, and environmental monitoring. Future research includes multi-modal sensing, multi-agent systems, and 6G communication integration with AI.

• Nonlinear Algebra
• Metric Algebraic Geometry
• Numerical Algebraic Geometry
• +۳ مورد دیگر

Paul Breiding is a Professor for Mathematical Methods in Data Science at the University of Osnabrück, within the Faculty of Mathematics/Computer Science/Physics. He is part of the Applied Algebra and Data Analysis working group and the Research Unit Data Science. His research focuses on nonlinear algebra, metric algebraic geometry, and their applications in numerical methods and data science. He is a Fellow of the Junge Akademie Mainz and co-authored the book 'Metric Algebraic Geometry' with Kathlen Kohn and Bernd Sturmfels. His work includes developing the software HomotopyContinuation.jl (v.2.11), which is widely used for numerical algebraic geometry. His research interests span algebraic geometry, tensor decompositions, and computational methods. Recent publications investigate geometric properties of algebraic varieties, condition numbers in tensor approximations, and probabilistic aspects of algebraic structures. Key contributions include studies on the reach of algebraic manifolds, typical ranks of random tensors, and sensitivity analysis in numerical algorithms. His work bridges theoretical algebraic geometry with practical computational tools for data science applications. Software: HomotopyContinuation.jl (v.2.11) Labs/Teams: Applied Algebra and Data Analysis, Research Unit Data Science

Andreas Andreouمشاهده پروفایل

استاد

• Neuromorphic Engineering
• CMOS Microsystems
• Sensory Information Processing
• +۶ مورد دیگر

Andreas Andreou is a Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the JHU Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab, focusing on brain-inspired microsystems, neuromorphic engineering, and biomedical sensors. His research spans CMOS-based neuromorphic processors, event-based vision systems, and wearable health monitoring devices like the StethoVest. Key contributions include silicon retinas, polarization-sensitive imagers, and algorithms for pattern analysis. Research Interests: Neuromorphic Computing: Designing energy-efficient brain-inspired chips using 3D CMOS, FETs, and memristive technologies. Biomedical Microsystems: Wearable acoustic sensors for cardiac monitoring and vestibular prosthetics. AI Hardware: Neuromorphic accelerators for edge computing, leveraging LLMs for automated circuit design. Notable Achievements: IEEE Fellow (since 2020) Recipient of the 3rd Best Paper Award at IEEE BioCAS 2018 $2M DARPA grant for bio-inspired event cameras Labs/Teams: The Andreou Lab collaborates with the Kavli Neuroscience Discovery Institute and NSF-funded neuromorphic projects. Ongoing work includes neuromorphic Ising machines, LLM-driven chip design, and quantum sensing for medical applications.

Andrea Ciniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Graph Deep Learning
• Time Series Forecasting
• +۳ مورد دیگر

Andrea Cini is a postdoc researcher affiliated with the Graph Machine Learning Group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) at the University of Lugano (USI). He also holds a position as a SNSF postdoc fellow at the University of Oxford under Prof. Michael Bronstein, focusing on machine learning for time series forecasting and graph processing. His research integrates graph deep learning methodologies with spatiotemporal dynamics, emphasizing applications in healthcare, energy systems, and intelligent systems. Education: PhD in Computer Science and Engineering (USI, 2020), supervised by Prof. Cesare Alippi MSc and BSc in Computer Science and Engineering (Politecnico di Milano) Visiting researcher at Imperial College London (Prof. Danilo Mandic) Research interests span graph neural networks , time series forecasting , and spatiotemporal data processing . His work has introduced influential methods such as the Torch Spatiotemporal library, and has been recognized with a best paper award. Recent publications emphasize applications in relational conformal prediction, hierarchical forecasting, and energy grid optimization. Awards include the Best Paper Award for contributions to graph-based forecasting methodologies. Current projects are funded by the Swiss National Science Foundation, exploring graph-based reinforcement learning and spatiotemporal modeling at the University of Oxford. Collaborations include affiliations with the Northernmost Graph Machine Learning group at UiT the Arctic University of Norway. His research bridges theoretical advancements and industrial applications in fields like healthcare dynamics prediction and smart grid optimization.

Carl Sechenمشاهده پروفایل

استاد

• Digital Integrated Circuits
• Analog Integrated Circuits
• Computer-Aided Design (CAD)
• +۵ مورد دیگر

Carl Sechen is a Professor in the Electrical & Computer Engineering Department at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on design and computer-aided design (CAD) of digital/analog integrated circuits, with a strong emphasis on secure ICs and transistor-level programmable fabrics. He leads projects like the TRAP fabric, which achieves 10X logic density over traditional FPGAs while enabling secure hardware through IC redaction. Education: Ph.D. (1987, UC Berkeley), M.S. (1977, MIT), B.E.E. (1975, University of Minnesota). Awards include IEEE Fellow (2002), UTD Distinguished Teaching Award (2014), and multiple Semiconductor Research Corporation awards. His work bridges theoretical CAD advancements with practical hardware security solutions. Research Highlights: Secure IC design, transistor-level programmability, FPGA architecture, hardware security. Key Projects: TRAP fabric development, anti-reverse engineering techniques, low-power design methodologies. Awards: Over 20+ honors including best paper awards at PRIME (2017) and DATE (nominated 2017). Publications span journals like IEEE Transactions on CAD and conferences such as DATE, ISCAS, and VTS. Current work emphasizes secure hardware through obfuscation and programmable logic innovations.