جست‌وجوی استاد و پژوهشگر

Feyza SELAMETمشاهده پروفایل

استادیار

• Deep Learning
• Computer Vision
• Electromagnetic Pollution Analysis
• +۳ مورد دیگر

Feyza SELAMET serves as Assistant Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences, where she has held academic positions since completing her PhD in Computer Engineering in 2023. Her institutional roles include research and teaching within Turkey's national higher education framework. Her academic foundation includes: Bachelor of Science in Computer Engineering, Sakarya University (2009-2013) Master of Science in Computer Engineering, Sakarya University (2013-2015) - Thesis: Electromagnetic pollution analysis with K-means algorithm Doctor of Philosophy in Computer Engineering, Sakarya University (2015-2023) Research focuses on deep learning applications spanning industrial computer vision systems and biomedical image analysis. Her work pioneers automated defect detection in metallic manufacturing processes and develops neural network architectures for dermatological diagnostics. Complementing this, she conducts environmental health research analyzing electromagnetic field exposure from telecommunications infrastructure and renewable energy installations using advanced data mining methodologies. Publication trends reveal a strategic shift from electromagnetic pollution studies (2015-2017) toward deep learning specialization (2020-2023). Early work established frameworks for measuring radiation exposure in urban and industrial settings, while recent publications demonstrate expertise in real-time defect classification systems and medical imaging pipelines using convolutional neural networks. She leads the research project Metalik parçaların bilgisayar görmesi ile kalite kontrolünün sağlanması (Quality Control of Metal Parts via Computer Vision) targeting industrial automation applications. No graduate student advisement records appear in current institutional documentation.

Tuğrul TAŞCIمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۳ مورد دیگر

Tuğrul TAŞCI serves as an Assistant Professor in the Department of Information Systems Engineering at Sakarya University's Faculty of Computer and Information Sciences, where he has maintained continuous academic service since 2001. His career progression includes Research Assistant positions across multiple university units before advancing to his current faculty role in 2016. His academic credentials include: Doctorate in Computer and Information Engineering (2014) from Sakarya University Institute of Science, thesis: Real-Time Motion Tracking with Particle Filtering Based on Data Fusing Master's degree in Computer and Information Engineering (2004) with thesis: Design of an Integrated Web-Based Distance Education System Bachelor's degree in Computer Engineering (2001) with thesis: Course Scheduling with Genetic Algorithms Dr. TAŞCI's research centers on Artificial Intelligence applications, particularly Natural Language Processing for Arabic text and Computer Vision . His work integrates particle filtering , data fusion , and optimization algorithms (e.g., Artificial Bee Colony, Firefly) to solve problems in text summarization, motion tracking, and image processing. Recent publications demonstrate expansion into deep learning for industrial defect detection and time series analysis. Analysis of his 2019-2024 publications reveals three dominant research trajectories: (1) Arabic NLP with focus on extractive summarization using PageRank and word embeddings, (2) Computer vision systems for motion tracking and text detection leveraging particle filters and curvature features, and (3) Hybrid optimization techniques applied to diverse domains from emergency management to customer churn prediction. Current academic advising activities and research grant details are not publicly documented in available sources. Similarly, no institutional laboratories or research teams are explicitly associated with his profile in the provided materials.

Pascual Campoy Cerveraمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Unmanned Aerial Vehicles (UAVs)
• +۶ مورد دیگر

Pascual Campoy Cervera is a Full Professor at the Universidad Politécnica de Madrid (UPM) and holds visiting professor positions at Delft University of Technology, Tongji University, and Queensland University of Technology. His work focuses on Control Systems , Machine Learning , and Computer Vision for Unmanned Aerial Vehicles (UAVs) . As Principal Investigator of the Computer Vision and Aerial Robotics group at UPM's Center for Automation and Robotics (CAR), he has led over 40 R&D projects with European, national, and industrial funding. Current affiliations: UPM, TU Delft, CAR-UPM Research themes: UAV autonomy, swarm robotics, embedded vision systems His research integrates cutting-edge technologies in image processing, control theory, and artificial intelligence to enhance UAV capabilities in unstructured environments. Recent projects include: Autonomous firefighting systems High-speed drone racing frameworks Swarm-based solar farm inspection Thrust vectoring for heavy UAVs Notable scientific awards include multiple international prizes at UAV competitions (IMAV12–17). His team has developed the Aerostack and Aerostack2 frameworks for aerial robotics, which address execution control, mission planning, and sensor fusion challenges.

Michel Guillaumeمشاهده پروفایل

استاد

• Aircraft Design
• Structural Integrity
• Fatigue Analysis
• +۴ مورد دیگر

Michel Guillaume serves as Professor and Head of the Centre for Aviation at Zurich University of Applied Sciences (ZHAW) School of Engineering, concurrently leading the Centre for Aircraft System Design and Integration. His academic responsibilities include lecturing in aerodynamics, structural integrity, systems engineering, and unmanned aviation, while directing the Master's programme profile commission for MSE in Aviation and coordinating the MSE Advanced Aircraft System Design module. His research centers on sustainable aircraft development through collaborative digital engineering and establishing drone testing infrastructure (indoor/outdoor). Key interests span structural integrity, fatigue analysis, unmanned aerial systems, aerodynamics, and systems engineering, with emphasis on damage tolerance methodologies for aging aircraft and AI-driven drone technologies. Current projects include the Aviation Research Ecosystem Advanced Novel Approach and LINA (Shared Large-scale Infrastructure for Autonomous Systems). Recent publications (2023-2024) demonstrate evolving focus from traditional fatigue analysis toward AI-integrated drone systems and structural risk assessment frameworks. His work consistently bridges theoretical research with practical aviation applications, particularly evident in Swiss aeronautical fatigue reviews and drone safety validation studies. Guillaume directs multiple funded projects including the completed Engineering Documentation for Junkers Ju-52 and Safety Analysis for Structural Integrity of Ageing Airplanes. As Director of the Aviation Research Center Switzerland (ARCS), he manages contractual research efforts through ZHAW consulting projects with annual fixed budgets. He actively participates in professional networks including the International Committee on Aeronautical Fatigue (ICAF) as Swiss national delegate, American Institute of Aeronautics and Astronautics (AIAA), and Swiss Aerospace Cluster, while developing ZHAW's drone test infrastructure as critical research infrastructure for the Swiss aviation ecosystem.

• Signal Processing
• Computer Vision
• Robotics
• +۵ مورد دیگر

Petros Maragos is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, where he directs the Division of Signals, Control and Robotics. He founded the Computer Vision, Speech Communication & Signal Processing Lab (1999) and the Hellenic Robotics Center of Excellence (2025). His research spans signal processing, computer vision, robotics, and machine learning, with 450+ publications and leadership in 50+ EU/Greek/US projects. Education includes a Dipl.Ing. from NTUA (1980), M.Sc./Ph.D. from Georgia Tech (1982/1985), and faculty positions at Harvard University (1985-1993) and Georgia Tech (1993-1998). Research Focus: Multimodal perception, nonlinear systems, assistive robotics, and deep learning. Recent work integrates tropical geometry with neural networks, robotic healthcare applications, and sign language technologies. Articles emphasize neural architectures, real-world robotics, and AI for social good. Awards: IEEE Fellow (1995), EURASIP Fellow (2010) IEEE W.R.G. Baker Prize (1995) NSF Presidential Young Investigator Award (1987-1992) CVPR/PETRA Best Paper Awards (2022-2025) Advising & Grants: Supervised 30+ PhDs and 130+ Master's students. Secured funding from EU Horizon 2020, NSF, and Greek national programs for projects like i-Walk (robotic mobility) and e-Prevention (mental health monitoring). Labs: Leads NTUA's Intelligent Robotics Lab and co-founded the Robotics Institute at Athena Research Center, focusing on human-robot interaction and perception systems.

Arthur G Richardsمشاهده پروفایل

استاد

• Trajectory Optimization
• Unmanned Aerial Vehicles (UAVs)
• Model Predictive Control
• +۴ مورد دیگر

Arthur G Richards serves as Professor of Robotics and Control within the Dynamics and Control department at the University of Bristol's School of Engineering Mathematics and Technology. His research specializes in trajectory optimization for aerospace applications, focusing on UAV autonomy, spacecraft rendezvous, and air traffic management through advanced optimization techniques. His educational foundation includes an M.Eng. from the University of Cambridge and S.M./Ph.D. degrees from MIT. Research interests center on solving complex aerospace challenges through: Non-convex optimization for obstacle avoidance in cluttered environments Robust model predictive control for real-time disturbance compensation Distributed optimization enabling large-scale vehicle cooperation Scalable algorithms for high-traffic scenarios with minimal fuel consumption Analysis of his 132 research outputs reveals evolving emphasis on reliability-aware UAV path planning, interpretable reinforcement learning for aircraft control, and swarm robotics with real-world validation. Recent work increasingly integrates machine learning with traditional control theory while addressing practical constraints like sensor noise and system failures. Professor Richards has supervised 22 research students and secured funding for 11 projects, including the active Aerial Robotics for Search and Rescue (2022-2026) and PORTAL (2022-2024) initiatives. His industry collaborations with Thales and focus on technology transfer demonstrate strong academic-industrial integration. He actively contributes to the Smart Networks for Sustainable Futures and Robotics research groups, developing frameworks for multi-robot systems that balance theoretical rigor with practical deployment requirements in conservation, inspection, and exploration scenarios.

Genda Chenمشاهده پروفایل

استاد

• Structural Health Monitoring
• Structural Control
• Interface Mechanics and Deterioration
• +۳ مورد دیگر

Genda Chen, Ph.D., P.E., F.ASCE, F.SEI, F.ISHMII , is a Professor and the Robert W. Abbett Distinguished Chair in Civil Engineering at Missouri University of Science and Technology (Missouri S&T) . He also serves as Director of the Center for Intelligent Infrastructure (CII) , Director of the INSPIRE University Transportation Center , and Associate Director of the Mid-America Transportation Center (MATC) . Education: Ph.D. in Civil Engineering, State University of New York at Buffalo (1992) Postdoctoral training, State University of New York at Buffalo (1993) Research Interests: His research spans structural health monitoring (SHM) , interface mechanics and deterioration , adaptive passive dampers , multi-hazards assessment , forensic studies , seismic retrofit , soil-structure interaction , and bridge engineering . His work integrates interdisciplinary collaborations with electrical, mechanical, materials, and computer engineering to advance smart infrastructure systems. Scientific Awards: 2019 SHM Person of the Year 1998 NSF CAREER Award 2004 Academy of Civil Engineers Faculty Achievement Award Missouri S&T Faculty Research Awards (2009, 2011, 2013) Inducted into the Academy of Civil Engineers at Missouri S&T (2016) Honorary member of Chi Epsilon Research Leadership: Dr. Chen has authored or co-authored over 400 technical publications , including 168 journal papers, 5 book chapters, and 24 keynote/invited presentations. He holds three patents in structural sensing and corrosion protection technologies. He led the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-9) in 2019 in St. Louis, Missouri. Labs & Centers: Director, Center for Intelligent Infrastructure (CII) Director, INSPIRE University Transportation Center Associate Director, Mid-America Transportation Center (MATC) Director, System and Process Assessment Research Laboratory (SPAR Lab)

Kristen Donnellمشاهده پروفایل

دانشیار

• Microwave Nondestructive Testing
• Thermography
• Frequency Selective Surfaces
• +۳ مورد دیگر

Kristen Donnell serves as an Associate Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology (Missouri S&T), where she holds the Woodard Associate Professor of Excellence title. She concurrently directs the Microwave Sensing (mSense) Laboratory and serves as Interim Director of the Center for Infrastructure Engineering Studies (CIES). Her academic appointments are exclusively at Missouri S&T, with no cross-appointments at other institutions. Her research spans microwave nondestructive testing, thermography, and frequency selective surfaces with applications in structural health monitoring and materials characterization. Dr. Donnell specializes in active microwave thermography for defect detection in composites, concrete structures, and additively manufactured components. Her work integrates electromagnetic theory with thermal imaging to develop novel sensing methodologies for infrastructure assessment. Analysis of her recent publications reveals a strong focus on advancing frequency selective surface (FSS) technologies for embedded sensing applications, microwave-based quality assessment of additively manufactured parts, and thermographic signal processing techniques. Her research demonstrates consistent innovation in microwave-millimeter wave nondestructive evaluation methods with practical applications in transportation infrastructure and composite materials. Scientific Awards and Recognition: Woodard Associate Professor of Excellence Senior Member of IEEE Active member of IEEE Instrumentation and Measurement Society Administrative Committee Dr. Donnell maintains significant leadership in professional organizations, having served in multiple appointed and elected roles within the IEEE Instrumentation and Measurement Society since 2007. Her research program bridges academic inquiry with practical infrastructure applications through collaborations with the Missouri Department of Transportation (MoDOT) and industry partners. She previously contributed to engineering development at Raytheon Company before pursuing her academic career. Her laboratory work focuses on the Microwave Sensing (mSense) Laboratory, which specializes in developing microwave and thermographic techniques for structural health monitoring. Current projects emphasize embedded sensing systems for infrastructure assessment and advanced characterization of additively manufactured components using microwave methods.

Turgut Öztürkمشاهده پروفایل

استاد

• Structural Engineering
• Earthquake Engineering
• Railway Infrastructure
• +۳ مورد دیگر

Turgut Öztürk is a Professor at Istanbul Technical University's Department of Civil Engineering in the College of Engineering. His research focuses on structural engineering, earthquake resilience, and railway infrastructure, with a particular emphasis on seismic behavior analysis and innovative monitoring techniques. Research Interests: Earthquake Engineering Railway Infrastructure Bridge Engineering Seismic Analysis Computer Vision Applications in Structural Monitoring Building Slab Design Research Trends: Recent work includes leveraging smartphone-based vision algorithms for structural health monitoring and analyzing lessons learned from past earthquakes to improve curved bridge designs. His studies often intersect with Istanbul's urban infrastructure challenges. Projects: Investigated construction engineering topics in municipal contexts (2022) Supervised strengthening works for excavation support systems in Sarıyer (2021) Conducted research on infrastructure engineering challenges (2021-2022) Metrics: Holds an h-index of 24 with over 4 citations, and has been actively publishing since 2004 with notable activity in 2023.

Jong-Jae Leeمشاهده پروفایل

استاد

• Structural Dynamics
• Smart Structure Technologies
• Structural Health Monitoring
• +۳ مورد دیگر

Jong-Jae Lee is a Professor at Sejong University's Department of Civil and Environmental Engineering. His research focuses on structural dynamics, smart structure technologies, and structural health monitoring. Institution: Sejong University Department: Civil and Environmental Engineering Academic Rank: Professor Research Interests : Structural Health Monitoring (SHM) systems Neural Networks and Deep Learning Applications Computer Vision for Civil Infrastructure Finite Element Modeling and Sensor Data Analysis Recent Research Trends : His recent publications emphasize AI-driven solutions for infrastructure monitoring, including computer vision-based glass bead quality analysis, transformer-CNN hybrid models for anomaly detection, and automated pavement distress detection. Labs & Teams : Leads the Dynamics and Smart Structures Laboratory , mentoring Ph.D. and Master’s students in civil engineering and AI applications.

• Machine Learning
• Computer Vision
• 3D Geometry
• +۲ مورد دیگر

Dr. Jun Li is a Senior Lecturer at the School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Australia. He received his Ph.D. in Computer Science from Queen Mary University of London in 2009 and is affiliated with the Australian Artificial Intelligence Institute (AAII) at UTS. His research spans multiple domains within artificial intelligence, with primary focus on Machine Learning applications in computer vision and 3D geometry. Dr. Li has published extensively in high-impact journals including IEEE Transactions (TPAMI, TIP, TNNSLS) and Pattern Recognition, with recent work expanding into interdisciplinary research in earth science and marine applications. His research output demonstrates consistent productivity with numerous publications each year across diverse AI application areas. Dr. Li's work shows strong thematic progression from foundational computer vision techniques to applied interdisciplinary research. Early work focused on face hallucination and video super-resolution, while more recent publications address environmental applications using Graph Neural Networks for wave prediction and damage classification for disaster response. His research consistently bridges theoretical AI advances with practical real-world applications across healthcare, autonomous systems, and environmental science. AI to assist disaster emergency response (2023-2026) Applying Generative Adversarial Network in Medical Image Analysis (2020-2021) Big Massive Open Online Course (MOOC) Data Retrieval (2017-2020) As an educator, Dr. Li teaches core courses including '31005 Machine Learning' and '32513 Advanced Data Analytics Algorithms' at UTS, and is available for Masters Research and PhD student supervision, contributing to the development of next-generation AI researchers.

Elin Anna Toppمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Cognitive Science
• Human-Robot Interaction
• +۳ مورد دیگر

Elin Anna Topp is a Senior Lecturer in the Department of Computer Science at Lund University's Faculty of Engineering (LTH), holding the title of Excellent Teaching Practitioner (ETP) and serving as Head of Department. She manages projects including NEXTG2COM (a Vinnova Competence Centre) and contributes to Lund's Natural and Artificial Cognition and AI and Digitalization profile areas. Her research centers on establishing common ground between humans and machines through Artificial Intelligence, Cognitive Science, and Human-Robot Interaction. She develops robotic systems that communicate effectively with users in industrial/service robotics and autonomous system monitoring (e.g., drones, self-driving cars), focusing on resolving ambiguities from mismatched human-robot expectations. This work stems from her PhD research on Human Augmented Mapping and addresses critical needs for flexible, human-comprehensible interfaces in increasingly autonomous systems. Recent publications (2023-2025) reveal strong thematic cohesion in human-robot interaction for autonomous systems, with dominant trends in situational awareness assessment (UAV operations), conflict simulation (autonomous driving), and conversational interfaces. Her work consistently bridges cognitive theory with practical robotics applications, emphasizing human-centered design for safety-critical domains. Scientific recognition includes: Excellent Teaching Practitioner title from LTH's Pedagogical Academy (2022-present) She actively shapes graduate education as WASP Graduate School Management member (since 2019) and COMPUTE Graduate School Steering group member, coordinating specialized courses like WASP's Interaction, Collaboration, and Visualisation. Major research funding comes from AISA (AI-powered situational awareness, 2023-2026) and NextG2Com (2024-2028), supporting her work on shared autonomy and digital infrastructure. Her leadership extends to supervising PhD projects including 'Get a Grip - Accurate robot–human handovers'. Topp co-manages RobotLab LTH infrastructure and collaborates across Lund's robotics ecosystem through initiatives like Robotics Week for schools and the Natural and Artificial Cognition conference series, fostering interdisciplinary teams spanning computer science, engineering, and cognitive science.

• Knowledge Representation and Reasoning
• Geometry Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Stavros Nousias is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich , focusing on applications of Artificial Intelligence in the Built Environment . His work bridges Knowledge Representation and Reasoning , Geometry Processing , and Machine Learning to advance construction informatics and digital twinning. Research Interests: AI for building evacuation prediction, technical drawing segmentation, BIM optimization, and respiratory disease modeling. Publications: 15+ peer-reviewed articles on topics including graph neural networks for construction simulations, pulmonary airflow analysis, and heritage site monitoring. Supervised Theses: Guided projects on AI-based BIM command prediction and robotized construction simulation . Labs: Active in the BIM-Lab and Robotic Fabrication Lab . Teaching: Co-instructor for courses like Artificial Intelligence in Engineering and Computation in Engineering 1 .

Robert Smykمشاهده پروفایل

استادیار

• FPGA
• Remainder Arithmetic
• Residue Number System
• +۵ مورد دیگر

Dr. Robert Smyk is an Assistant Professor in the Department of Automation at Gdańsk University of Technology's Faculty of Electrical and Automation Engineering. His office is located on floor 112 of the faculty building, and he can be contacted via email at robert.smyk@pg.edu.pl or by phone at +48 58 347 1332. Dr. Smyk's research spans several interconnected domains: Hardware-focused computing : FPGA implementations, residue arithmetic systems, and high-speed digital converters Algorithm development : Novel approaches to residue number system operations and signal processing optimizations Engineering applications : Computer vision for infrastructure inspection, cryptographic systems, and sensor data processing His publications demonstrate consistent focus on improving computational efficiency through mathematical optimizations and hardware-aware designs. With 67 publications documented in institutional repositories, his recent works explore: algorithmic improvements for residue arithmetic (2023-2025), thresholding/edge detection for industrial vision systems (2018-2022), and hardware architectures for specialized converters. Teaching activities include extensive involvement with 163 documented educational engagements.

Fenghui Yaoمشاهده پروفایل

استاد

• Image Processing
• Robotics
• UAVs/UGVs
• +۱ مورد دیگر

Dr. Fenghui Yao is a Professor of Computer Science and Director of Graduate Studies at Tennessee State University's Department of Computer Science. His academic journey includes positions at Kyushu Institute of Technology, University of East Asia, and Shimane University prior to joining TSU in 2004. Education background: Ph.D. in Computer Engineering, Kyushu Institute of Technology, Japan (1992) M.E. in Computer Engineering, Kyushu Institute of Technology, Japan (1988) B.A. in Electronic Engineering, Dalian Maritime University, China (1984) Research focuses on interdisciplinary applications in: Image Processing : Developing algorithms for real-time target detection and calligraphy analysis Robotics : Designing systems for infrastructure inspection and cultural preservation Parallel Architecture : Exploring quantum-dot cellular automata for nanosensor data processing Publications demonstrate consistent focus on hardware-software integration, with recent work in nanotechnology (2014) complementing earlier robotics research (2003-2010). His 2004 papers on robotic calligraphy represent innovative human-robot interaction studies. Grant involvement includes multi-year sensor systems research as Co-PI: Cross Layers Decision Making and Fusion Models for Automated Sensor Exploitation ($334,678; 2013-2018)