جست‌وجوی استاد و پژوهشگر

Hong Zhangمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Professor Hong Zhang serves as Chair Professor in the Department of Electronic and Electrical Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, having transitioned from his full professorship at the University of Alberta where he maintains an adjunct appointment. He directs the Centre for Intelligent Mining Systems (CIMS) and affiliates with the Robotics Research Laboratory, establishing new robotics research groups at both institutions. His academic foundation includes a BSc from Northeastern University (USA), PhD from Purdue University, and postdoctoral training at the University of Pennsylvania, joined the University of Alberta in 1988. Zhang's research spans robotics, computer vision, and image processing with emphasis on visual navigation, simultaneous localization and mapping (SLAM), and biologically-inspired collective robotics. His work develops cooperative multi-robot systems for challenging environments like mining and underwater inspection, addressing practical industrial challenges through vision-based sensing and navigation solutions. Recent publications (2019-2020) demonstrate leadership in robotic perception, particularly moving object detection under illumination changes and HDR reconstruction from polarization data, reflecting consistent innovation in computer vision applied to real-world robotics. ASTech Award (2008) IEEE Millennium Medal Best Conference Paper at IEEE ROBIO 2019 2018 IROS Distinguished Service Award Fellow of Canadian Academy of Engineering (2015) Fellow of IEEE (2014) NSERC Industrial Research Chair (2003-2017) Mentoring notable students including Xuesong Wu, Sean, and Moein who produced award-winning conference papers, Zhang's research has secured substantial funding through a decade-long Syncrude Canada partnership (NSERC/Alberta Innovates), the NSERC Industrial Research Chair, and a recent $7.1M Alberta Government grant for the Centre for Autonomous Systems. He leads the Centre for Intelligent Mining Systems at the University of Alberta while establishing a new robotics research group at SUSTech focused on intelligent sensing systems for industrial automation and navigation.

Fethiye Ecem Edisمشاهده پروفایل

استاد

• Building Science
• Construction Technologies
• Building Envelope
• +۹ مورد دیگر

Prof. Dr. Fethiye Ecem Edis , a faculty member at the Department of Architecture in Istanbul Technical University , specializes in Building and Construction Technologies/Systems . With a PhD in Building Science from the same institution, her career spans over 25 years, including roles from Research Assistant (1998-2015) to Associate Professor (2015-2024) and Professor (2024-present). She also served as Deputy Head of Department (2022-2023). Education : PhD in Building Science (2002-2006), MSc in Building Information with Thesis (1995-1997), BArch (1990-1995) – all from Istanbul Technical University. Edis' research focuses on building envelope performance , climate change adaptation , life cycle assessment , and machine learning applications in architectural design. Her work addresses thermal behavior of materials , façade durability , and energy-efficient systems , particularly in Mediterranean and hot-humid climates. Recent publications highlight trends in automated defect detection using thermal imaging, multi-objective optimization for retrofits, and LCIA-based material selection . These align with her projects on shape memory alloys in shading systems and thermal comfort indicators with machine learning integration. Notable Projects : 2025: Thermal Comfort Metrics with Machine Learning 2024: Low-Cost Infrared Moisture Detection 2022: Shape Memory Alloys in Shading Components

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.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

• 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.

• Electron device and electronic equipment
• Chemical reaction and process system engineering
• Wireless sensor networks
• +۴ مورد دیگر

Shigeyuki Tateno is a Professor at the Graduate School of Information, Production and Systems within the Faculty of Science and Engineering at Waseda University. Holding a Doctor of Engineering degree from Kyushu University, he has established himself as a leading researcher in wireless sensor networks, fault diagnosis systems, and corrosion engineering. His academic journey spans over three decades, with significant contributions to both theoretical frameworks and practical industrial applications. Dr. Tateno received his graduate education from Kyushu University's Division of Engineering, specializing in chemical engineering, completing his studies in 1992. His academic career progressed from Research Assistant in the Department of Chemical Engineering at Kyushu University (1992-2003) to Associate Professor at Waseda University's Graduate School of Information, Production and Systems (2003-2009), culminating in his current position as Professor. He maintains active membership in four professional societies: Japan Facility Management Society, Institute of Electronics, Information and Communication Engineers, Society of Instrument and Control Engineers, and Society of Chemical Engineers. His research portfolio demonstrates remarkable breadth across multiple engineering disciplines. Dr. Tateno's primary research thrusts include wireless sensor networks for human motion detection and indoor positioning, fault detection and diagnosis using signed digraphs and graph theory, and corrosion engineering with particular focus on corrosion under insulation in petrochemical plants. His work bridges chemical engineering with computer science and electronics, creating innovative solutions for challenges in industrial maintenance, autonomous systems, and healthcare monitoring. Recent projects have expanded into autonomous driving systems, elderly monitoring technologies, and rescue operation support systems. Analyzed across his 67 publications with 276 citations and an h-index of 8, Dr. Tateno's research shows clear evolution from theoretical fault diagnosis methods toward practical applications in safety-critical systems. His most recent work demonstrates strong interdisciplinary connections between electronics, chemical engineering, and computer science, with increasing emphasis on real-world implementations in automotive technology, elderly care facilities, and industrial maintenance systems. The consistent thread throughout his career is the application of measurement technology to solve practical engineering problems. Japan Facility Management Society Institute of Electronics, Information and Communication Engineers Society of Instrument and Control Engineers Society of Chemical Engineers Dr. Tateno has supervised numerous research projects addressing critical engineering challenges in industrial maintenance, safety systems, and sensor technology development. His research has been applied in diverse settings including petrochemical plants, elderly care facilities, and automotive systems. The practical impact of his work is evident in implemented systems for pipe corrosion management, autonomous vehicle control, and rescue operation monitoring. His approach consistently bridges theoretical frameworks with practical implementations, ensuring research outcomes deliver tangible benefits in real-world environments. His laboratory focuses on developing wireless sensor network applications for positioning systems, human motion detection, and industrial monitoring. The research team combines expertise in electronics, chemical engineering, and computer science to create innovative solutions for challenges in elderly care, rescue operations, and petrochemical plant maintenance. Current projects include autonomous driving systems for elderly mobility, real-time logistics monitoring, and drowsiness detection systems based on physiological changes, reflecting the team's commitment to addressing societal challenges through engineering innovation.

• 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 .

Nadia Polikarpovaمشاهده پروفایل

دانشیار

• Program Synthesis
• Program Verification
• Type Systems
• +۲ مورد دیگر

Nadia Polikarpova is an Associate Professor in the Computer Science and Engineering Department at the University of California, San Diego. She leads the Programming Systems group and serves as a member of IFIP Working Group 2.8 on Functional Programming since 2022. Her academic journey includes a PhD from ETH Zurich (Switzerland) under Bertrand Meyer's supervision in 2014, followed by postdoctoral work at MIT CSAIL with Armando Solar-Lezama. Her research interests center around program synthesis, program verification, and type systems, with a focus on building practical tools that enhance software security and reliability. Polikarpova's work bridges theoretical foundations with real-world applications, particularly in the emerging area of AI-assisted programming. Her recent publications demonstrate a strong trajectory in program synthesis techniques, with increasing integration of machine learning approaches. The research spans from foundational type-driven synthesis methods to practical applications for validating AI-generated code and synthesizing heap-manipulating programs. Her work frequently appears in top-tier programming languages venues including PLDI, POPL, ICFP, and OOPSLA. 2020 Sloan Fellow 2020 Intel Rising Stars Award 2020 NSF CAREER Award Distinguished paper awards at PLDI'21, ICFP'20, and POPL'19 Best paper award at FM'15 Polikarpova actively mentors PhD students and has advised numerous graduates who now work at Microsoft Research, University of Michigan, and various tech companies. She teaches core programming languages courses including CSE 130 and specialized graduate courses on program synthesis (CSE 291). Her service to the community includes program committee roles for major conferences and co-chairing the Haskell conference in 2022.

Hirut Grossbergerمشاهده پروفایل

استاد

• Railway Engineering
• Sustainable Mobility
• Infrastructure Lifecycle Assessment
• +۲ مورد دیگر

FH-Prof. Dr. Hirut Grossberger is a Senior Researcher at the Carl Ritter von Ghega Institute for Integrated Mobility Research and International Coordinator for the Department of Rail Technology and Mobility at University of Applied Sciences St. Pölten. She teaches in three programs: Rail Technology and Management of Railway Systems (MA), Rail Technology and Mobility (BA), and Rail Vehicle Technology (BA). Her research bridges railway engineering, sustainable infrastructure, and digital mobility solutions. Education includes: BSc in Agricultural Engineering (Debub University, Ethiopia) MSc in Water Management and Environment (BOKU Vienna) MSc in Land Management, Infrastructure and Civil Engineering (BOKU Vienna) Doctoral research at Institute of Structural Engineering (BOKU Vienna) Research focuses on: Railway Technology : Infrastructure lifecycle assessment, noise reduction, and sensor-based monitoring Sustainable Mobility : Eco-materials (e.g., clay noise barriers), emission reduction, and circular economy applications Digital Transformation : Mobile inspection tools, digital product passports, and acoustic sensing systems Recent publications (2019-2025) emphasize sustainability in transport infrastructure, with trends toward: Environmental action programs (CLEA) Eco-material innovation for noise control Distributed Acoustic Sensing for rail safety Digital tools for infrastructure management Leads/contributes to 20+ EU projects including: STAFFER (rail skill development) ZeroEmissionCityBahn (sustainable infrastructure) DPP4ALL (digital product passports) Smart Inspection (AI-assisted bridge maintenance) ECO-TCO (operational efficiency)

Sakeena Muntahaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Image Processing
• +۳ مورد دیگر

Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.

Theo Tryfonasمشاهده پروفایل

دانشیار

• Smart Cities
• Cybersecurity
• Systems Engineering
• +۴ مورد دیگر

Theo Tryfonas is a Reader (equivalent to Associate Professor) in Smart Cities at the University of Bristol's School of Civil, Aerospace and Design Engineering, teaching undergraduate Engineering Design and Civil Engineering programmes alongside the MSc Computer Science. His academic qualifications include: BSc in Computer Science from the University of Crete, Greece MSc in Information Systems from Athens PhD in Informatics from Athens, sponsored by Ernst & Young His research integrates systems engineering, cybersecurity, and software development to solve urban infrastructure challenges across transportation, utilities, healthcare, and government sectors. Key focus areas include smart city resilience, critical infrastructure protection, and cyber-physical system security, with practical applications in marine safety, port security, UAV protection, and public transport systems. Recent publications demonstrate a strong interdisciplinary trend, merging blockchain, quantum sensing, and AI with urban infrastructure management, particularly in transportation security, structural health monitoring, and data-driven city planning. Professional recognition includes: Fellow of the Royal Society of Arts Chartered Professional Member of the Chartered Institute for IT (MBCS CITP) Theo provides expert witness testimony and continuing professional development courses while teaching across multiple programmes. His industry background includes programming at Ulysses Systems and consultancy/auditing at Ernst & Young, though specific student advising details and grant information are not publicly documented. No dedicated research labs or specialized teams are referenced in available materials.

• Robotics
• Control Systems
• Machine Learning
• +۷ مورد دیگر

Matteo Saveriano is an Associate Professor at the University of Trento , affiliated with both the Department of Industrial Engineering and the Department of Information Engineering and Computer Science . His work focuses on Robotics , Control Systems , and Machine Learning , with applications in robotic manipulation, locomotion, and human-robot interaction. Email: matteo.saveriano@unitn.it Research interests include antifragile control systems, dynamic movement primitives, reinforcement learning, biomechanical characterisation, and motion planning on discrete manifolds. His teaching covers Automatic Control and Robotics , with a hybrid theoretical-practical approach using Python simulations. Recent publications explore topics such as neuro-symbolic robotics, radiation effects on embedded GPUs, surface defect identification with Bayesian filtering, and reactive navigation using control barrier functions. These works highlight interdisciplinary trends at the intersection of robotics , machine learning , and control theory .

• Computer graphics
• Virtual reality
• High performance computing
• +۵ مورد دیگر

Prof. Frank Van Reeth serves as Full Professor in the Department of Computer Sciences at Hasselt University's Faculty of Sciences, where he directs the Digital Future Lab and leads the Applied Computer Science Lab within the Expertise Centre for Digital Media (EDM) at Campus Diepenbeek. His institutional roles include Vice-Presidency of Examination Boards for Informatics and Computer Science programs. His research spans computer graphics, virtual reality, high performance computing, and human-computer interaction with strong emphasis on industrial applications. Current projects include NORM.AI for efficient AI rendering, MAXVR-INFRA for digital-physical workspaces, and MAX-R for mixed reality pipelines, demonstrating his focus on bridging theoretical computer science with practical implementations in computer vision and extended reality systems. Prof. Van Reeth has supervised 23 PhD students to completion since 1997, with recent graduates working on synthetic data training and spatial context-aware services. His active supervision of 4 doctoral candidates focuses on hardware-accelerated deep learning and industrial inspection systems. He teaches core courses in computer graphics, visual computing, and problem solving while maintaining leadership in EDM's €15M+ research portfolio. The Applied Computer Science Lab under his direction operates as EDM's primary research engine, specializing in real-time rendering, 3D modeling, and computer vision solutions. The Digital Future Lab he directs provides the infrastructure backbone for Hasselt University's mixed reality research, enabling collaborations across 15+ ongoing projects with industrial partners in manufacturing and digital media sectors.

• Computer Graphics
• Computer Vision
• Virtual Reality
• +۲ مورد دیگر

Dr. Jeroen PUT serves as a Researcher-expert and Research Manager within the Faculty of Sciences at Hasselt University. His primary academic appointment resides in the Capacity group Computer Sciences, with significant leadership roles in the Expertise Centre for Digital Media (EDM). He actively contributes to the Applied Computer Science Lab and Digital Future Lab, driving research in computational imaging and interactive systems. His research centers on Computer Graphics , Computer Vision , and Virtual Reality with industrial applications. Key focus areas include real-time rendering, deep learning for visual inspection, and VR-based assembly validation systems. This work bridges academic innovation with manufacturing and logistics sectors through projects like FARAD2SORT for automated product sorting. Current funding includes the Expertise Centre for Digital Media initiative (2024-2028) which he manages, providing resources for advanced computational infrastructure. His teaching portfolio demonstrates commitment to graduate education in visual computing disciplines despite no explicit student supervision details being published. Dr. PUT operates within the Applied Computer Science Lab ecosystem, which fosters collaboration between computer scientists, engineers, and industry partners. The lab maintains strong ties to manufacturing and logistics sectors for rapid prototyping and deployment of vision-based solutions in real-world production environments.