جست‌وجوی استاد و پژوهشگر

Bernd Bickelمشاهده پروفایل

استاد

• Computational Design
• Digital Fabrication
• Computer Graphics
• +۷ مورد دیگر

Bernd Bickel is a Full Professor for Computational Design at ETH Zurich, embedded in the Design++ research center. He holds a Master's degree from ETH Zurich and a PhD from ETH Zurich under Markus Gross. Previously, he was at IST Austria (2015–2023), Disney Research, and TU Berlin as a visiting professor. His research focuses on computational design, digital fabrication, and simulation, with applications in robotics, computer vision, and material science. Key interests include physics-based simulation, geometry processing, and interdisciplinary engineering. Notable awards include the Academy of Motion Picture Technical Achievement Award (2019), SIGGRAPH's Significant New Researcher Award (2017), and the EUROGRAPHICS Best PhD Thesis (2012). He leads the Computational Design Lab at ETH Zurich and collaborates with institutions like Inria Nancy on projects such as MFX team collaborations. His work spans academic contributions (over 50 publications) and industrial applications, including the FlexMaps Pavilion (First Prize at IASS 2019) and computational tools for 3D printing. He actively mentors PhD students and oversees grants like ERC Starting Grant 'Materializable'.

Ardhendu Beheraمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Multimodal AI
• +۴ مورد دیگر

Ardhendu Behera is a Professor of Computer Vision & AI at Edge Hill University and co-director of the Intelligent Visual Computing Research Centre. He holds a post-doctoral background from the University of Leeds and has contributed to interdisciplinary research in AI-driven healthcare, autonomous systems, and defense applications. His work bridges computer vision with multimodal AI, emphasizing real-world impact through collaborations with institutions like the University of Bristol, IISc Bangalore, and UCL. Prof Behera’s research spans deep learning, healthcare diagnostics, and autonomous vehicle systems. He has pioneered AI tools for dementia monitoring (via social robotics and multimodal data) and developed algorithms for traffic scene analysis, driver behavior recognition, and medical imaging. His recent projects include breast cancer detection networks and plant disease recognition systems using transfer learning. Key Awards: Fellow of the UK Higher Education Academy (HEA) Editorial Roles: Associate Editor for IEEE Transactions on Image Processing Outreach: Media coverage on BBC News, ITV, and The Times for dementia research; keynote speaker at NCVPRIPG 2019 and ICIoT 2019 His research emphasizes interdisciplinary collaboration, with over 100 peer-reviewed publications in top-tier venues like CVPR, ECCV, and IEEE journals. He actively mentors students in PhD/MRes programs focusing on AI and computer vision applications.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Wenwen Zhang is a Professor at the Department of Electronic Engineering, College of Information Science and Electronic Engineering, Zhejiang University. With an extensive publication record spanning from 2016 to 2025, Dr. Zhang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer vision, machine learning, and sensor systems. Her work demonstrates significant contributions to medical imaging, sensor array systems, wireless communications, and AI-assisted applications. Dr. Zhang's research interests encompass a wide range of topics including medical image analysis, sensor array systems, wireless communications, and AI-assisted applications. Her work demonstrates particular expertise in developing innovative deep learning architectures for medical imaging tasks such as cardiac segmentation and nuclei detection, as well as creating sophisticated models for gas sensing and wireless communication systems. She has made significant contributions to the fields of one-shot object detection, medical image segmentation, and sensor fusion techniques, with her research often bridging theoretical advancements with practical applications in healthcare and engineering. Analysis of Dr. Zhang's recent publications reveals a strong focus on cutting-edge deep learning approaches applied to medical imaging and sensor systems. Her work shows increasing sophistication in model architectures, moving from traditional CNNs to more complex transformer-based and hybrid models. There's a clear trajectory toward more explainable and clinically relevant AI systems, particularly in medical applications. Her research also demonstrates growing interest in multimodal approaches, combining different types of data and sensors to improve system performance. Dr. Zhang maintains active collaborations with researchers at Zhejiang University, particularly with Yuanjin Zheng and Zhiping Lin in the field of electronic engineering and sensor systems. She also collaborates extensively with Fei-Yue Wang from the University of Chinese Academy of Sciences, evidenced by multiple publications on parallel vision frameworks. Her international collaborations include work with researchers from institutions in Canada on intelligent knee sleeves and other biomedical applications. Her publication record shows consistent productivity with 12 publications in 2025 (as of this writing), 25 in 2024, and 26 in 2023, indicating an active and growing research program across multiple high-impact journals and conferences.

• Machine Learning
• Process Systems Engineering
• Industry 4.0
• +۵ مورد دیگر

János Abonyi is a Professor at the Department of Process Engineering, Faculty of Engineering, University of Pannonia, Hungary. With over 118 publications spanning from 2000 to 2024, he has established himself as a leading researcher in process systems engineering, machine learning applications, and Industry 4.0 technologies. His work bridges theoretical computational intelligence with practical industrial applications, particularly in process optimization, fault detection, and smart manufacturing systems. Abonyi's research interests focus on applying machine learning and data mining techniques to industrial process systems. His work spans several key areas including soft sensor development, fault detection and diagnosis, optimization algorithms for manufacturing, and Industry 4.0/5.0 applications. He has pioneered approaches combining sequence mining with deep learning for alarm management, reinforcement learning for disassembly line optimization, and explainable AI for industrial applications. His research demonstrates a consistent trajectory from fundamental computational intelligence methods to their practical implementation in real industrial settings. His recent publications (2020-2024) show a strong emphasis on Industry 4.0 and 5.0 applications, with particular focus on human-machine collaboration, real-time locating systems, digital twins, and explainable AI for industrial applications. The interdisciplinary nature of his work is evident through collaborations across computer science, electrical engineering, and chemical engineering domains. His research group has produced significant contributions in optimization algorithms, particularly in reinforcement learning applications for manufacturing and process systems. Abonyi has supervised numerous PhD students who have become active researchers in their own right, including Tamás Ruppert, Ágnes Vathy-Fogarassy, and Balazs Feil. His collaborative network extends internationally, with publications in top journals including IEEE Access, Sensors, and Computers & Chemical Engineering. His work demonstrates strong industry relevance with practical implementations in manufacturing, process control, and industrial automation contexts.

• Computer Science
• Geospatial Engineering
• Urban Planning
• +۳ مورد دیگر

Yoshihide Sekimoto is a researcher with extensive contributions to computer science, geospatial engineering, and urban planning. His work focuses on leveraging deep learning, remote sensing, and GIS data for infrastructure analysis, human mobility modeling, and disaster response systems. Specializes in road damage detection using federated learning and computer vision Develops synthetic human mobility datasets for urban and disaster scenarios Integrates spatio-temporal modeling with graph networks for transportation systems Applies AI to environmental monitoring (e.g., satellite wind resolution) His recent publications highlight collaborations on 3D building reconstruction, public transit visualization, and land price estimation via street view images. Articles trends include multi-modal data fusion, cross-country infrastructure analysis, and agent-based simulations for urban dynamics.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۲ مورد دیگر

Lazaros T. Tsochatzidis is a researcher with significant contributions to computer vision , machine learning , and their applications in medical imaging and autonomous driving . He has published extensively in top-tier venues like Pattern Recognition , IEEE Access , and Robotics and Autonomous Systems , often collaborating with researchers such as Ioannis Pratikakis and Konstantinos Zagoris . Research Interests Specializes in LiDAR-based 3D object detection for autonomous vehicles Developed domain adaptive object detection techniques using mean teacher models Innovated part-aware graph fusion for skeleton-based action recognition Expert in medical image analysis for breast cancer diagnosis Contributed to historical document transcription and sclera segmentation research Scientific Awards No explicit awards mentioned in available data Notable Collaborations Long-term collaboration with Konstantinos Zagoris (6 co-authored papers) Extensive work with Ioannis Pratikakis (11 co-authored publications) Multidisciplinary collaborations with experts in medical imaging , autonomous systems , and biometric security

• Data Science
• Computer Vision
• Artificial Intelligence
• +۷ مورد دیگر

Alessandro Sebastian Russo is a PhD student in Computer and Systems Engineering at Politecnico di Torino , with a focus on scalable architectures for neurosymbolic AI in scene interpretation and generation. He is affiliated with the Department of Control and Computer Science (DAUIN) and part of the GRAINS group (Graphics and Intelligent Systems). His academic role includes Lecturer responsibilities as an external teaching assistant. His educational background includes a Master's degree in Data Science (2021) from Politecnico di Torino, with thesis work on Machine Learning and Transformer Neural Networks. Prior to his PhD, he completed a one-year research fellowship in medical machine learning. Russo's research bridges neural and symbolic AI through Logic Tensor Networks (LTNs) , aiming to enhance scene understanding for autonomous driving applications. Current work explores integration of Large Language Models (LLMs) and Commonsense Knowledge Bases to improve logical consistency. His teaching experience includes collaborations on courses in Mobile Application Development and Innovation Management . Key publications span medical imaging ( Medical Image Analysis ), neurosymbolic learning (NeSy workshops), and computer vision (IJCNN, ISVC). He is also an inventor on the national patent Deep Neural Networks for Multi-View Mammography Classification . No scientific awards are currently listed in the available information.

• Mathematical modeling and simulation in biology
• Scientific computing and numerical methods
• Computational fluid dynamics
• +۴ مورد دیگر

Ying Chen is a Visiting Assistant Professor of Mathematics at Purdue University's Department of Mathematics, affiliated with the College of Science. Her research focuses on mathematical modeling in biology, scientific computing, and computational fluid dynamics. She has contributed to interdisciplinary projects involving autonomous systems, human-computer interaction, and medical visualization. Her work bridges theoretical mathematics with practical applications in healthcare systems, environmental monitoring, and autonomous vehicle technologies. Key research interests include developing algorithms for 3D scene reconstruction, optimizing vehicle interface safety, and applying AI to clinical decision support. Her recent publications explore topics like neural motion flow for autonomous driving, haptic feedback in bicycle safety systems, and LiDAR-camera fusion techniques. Collaborations span computer science, engineering, and biomedical fields. Publications from 2023–2025 reveal a focus on spatiotemporal data visualization, VR training systems, and multi-modal forest monitoring. Notable trends include integrating machine learning with real-world challenges in transportation safety and environmental analysis. While no awards are explicitly listed, her work has been recognized in VAST Challenge competitions for innovative visualization methodologies. Ying Chen’s teaching and research emphasize bridging abstract mathematical concepts with tangible applications. She has developed educational tools like the Mindwriter academic writing environment and conducted studies on visual learning strategies for students with math difficulties. Her work on car dashboard interface design and in-car entertainment systems highlights a commitment to user-centered technology solutions.

• Computer Vision
• Machine Learning
• Sports Analytics
• +۳ مورد دیگر

David Lau is a Lab Instructor/Supervisor at the University of Waterloo, specializing in computer vision and machine learning applications across sports analytics and environmental monitoring. His work focuses on developing algorithms for hockey puck localization, player tracking, and sea ice classification using SAR imagery. He contributes to datasets like AI4Arctic and explores synthetic data augmentation techniques for improving model robustness. Research interests include deep learning architectures for multi-task learning, contextual cue utilization in vision systems, and the integration of remote sensing data with climate models. Key areas of exploration involve improving hockey video analysis through homographic projections and leveraging Bayesian methods for uncertainty quantification in environmental predictions. Recent work emphasizes domain-specific adaptations like sports field localization and ice hockey event detection. His contributions span from foundational algorithms (e.g., IceGCN for SAR imagery) to practical systems like PuckNet for puck location estimation in broadcast videos. Advising and grants: No specific grants or advisees listed in available information. Collaborates on projects involving hockey analytics teams and environmental monitoring initiatives, though specific team names are not disclosed.

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +۵ مورد دیگر

Dr. Alexander Wong is a Professor in the Department of Systems Design Engineering at the University of Waterloo, cross-appointed in other departments. His research focuses on advancing artificial intelligence (AI), machine learning, and computer vision with applications in healthcare, biomedical engineering, and robotics. Key areas include medical imaging technologies like OCT+ERG systems for neurovascular analysis, AI-driven diagnostics (e.g., prostate cancer segmentation), and radar-based monitoring for aging wellness. He leads interdisciplinary projects in health informatics, synthetic data generation, and AI ethics. His work integrates hardware-software systems for real-world challenges, such as non-invasive retinal imaging and in-home gait analysis. Notable contributions include the Cancer-Net framework for cancer detection, the NutritionVerse dataset for dietary monitoring, and the COVID-Net for pandemic response. His research also addresses public health issues like HIV/HCV co-infection and mental health impacts on healthcare utilization in marginalized populations. Dr. Wong's innovations span from foundational AI algorithms to clinical and industrial applications, emphasizing cross-disciplinary collaboration to bridge theoretical advancements with practical solutions.

Son Tung Nguyenمشاهده پروفایل

پژوهشگر

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Son Tung Nguyen is a PhD Researcher at the Queensland University of Technology (QUT) Centre for Robotics (QCR) , affiliated with Prof. David Milford’s research group. His work focuses on advancing visual localization techniques, particularly in adapting these methods to low-resolution imaging scenarios. He holds a Master’s degree from the University of Stuttgart, where his thesis explored integrating modern computer vision with task and motion planners for robotic manipulation. Education: Master’s Degree: University of Stuttgart (Thesis: Robotic manipulation combining computer vision and task planning) PhD Candidate: Queensland University of Technology (QUT) Research Interests: Nguyen’s research spans robotics, computer vision, and machine learning. His current projects emphasize visual localization in challenging environments, such as those requiring low-resolution imaging, and the application of these techniques to augmented reality (AR) systems. He has also contributed to self-supervised learning frameworks for robotic sequential manipulation and representation learning in scene-image analysis. Advising & Collaborations: As part of Prof. Milford’s group, Nguyen contributes to projects at QCR. His research aligns with the Centre’s focus on advancing autonomous systems, robotics, and AI-driven solutions. While no personal grants or awards are explicitly listed, his work is embedded in the group’s broader achievements, such as recent grants and recognitions highlighted in QCR’s news. Labs & Teams: Nguyen is actively involved in the QCR research community, collaborating with experts in robotics and computer vision to bridge theoretical advancements with practical applications in autonomous systems.

• Machine Learning
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Hichem Snoussi is Full Professor (Classe Exceptionnelle) at the University of Technology of Troyes, serving as Deputy Director of LIST3N and leading the M2S research group. His research spans machine learning, computer vision, nuclear imaging, and sensor networks. He co-founded two startups: Damavan Imaging (nuclear Compton cameras) and AQUILAE (computer vision). Dr. Snoussi leads major projects including RailMon (€8M railway infrastructure monitoring) and DreamScanner (€1.5M nuclear imaging). He has supervised 19 PhD graduates and currently mentors 7 doctoral candidates. His research output includes 120+ international journal articles and 15 patents. Key awards include the 2023 French Presidency Prize for Made in France Technology.

• Decision support systems
• Computational intelligence
• Recommender systems
• +۴ مورد دیگر

Distinguished Professor Jie Lu is Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where she also serves as Director of the Australian Artificial Intelligence Institute (AAII). With over two decades of academic service at UTS, she has held progressively senior roles including Professor since 2007, Head of School of Software (2011-2014), and Director of the Centre for Artificial Intelligence (2017-2020) before assuming her current leadership positions. Professor Lu earned her PhD from Curtin University in 2000 and joined UTS the same year. Her academic journey progressed from Lecturer (2000-2001) to Senior Lecturer (2002-2004), Associate Professor (2005-2007), and ultimately Professor (2017-present). Professor Lu's research spans computational intelligence with particular expertise in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. Her work bridges theoretical innovation with practical applications across transportation, telecommunications, healthcare, and education sectors. She has pioneered approaches in data-driven decision making that deliver tangible economic benefits and risk management solutions for industry partners. Her recent publications demonstrate continued leadership in AI research, with a focus on addressing challenges in non-stationary environments, out-of-distribution detection, cross-domain recommendation, and healthcare applications. Her work increasingly integrates large language models with specialized domain knowledge for enhanced decision support. Officer of the Order of Australia (2022) 2023 NSW Premier's Prize for Excellence in Engineering or Information & Communication Technology Australian Laureate Fellow (2019) IEEE Fellow for contributions to fuzzy machine learning and decision making (2018) Fellow of International Fuzzy Systems Association (2017) Australia's Most Innovative Engineer Award (2019) Multiple IEEE Transactions Outstanding Paper awards Professor Lu has supervised over 50 PhD students to completion, with half pursuing academic careers and half working in industry. She has secured over A$10 million in research funding as lead Chief Investigator, including 10 highly competitive ARC Discovery Projects. Her industry collaborations include significant projects with Optus, Sydney Trains, Domain Holdings, and healthcare organizations. As Director of the Australian AI Institute, Professor Lu leads Australia's largest AI research hub with over 250 researchers and PhD students. Under her leadership, AAII has secured 47 ARC grants and over 110 industry projects since 2017. She also serves as Editor-in-Chief of Knowledge-Based Systems, a leading journal in the field.

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +۷ مورد دیگر

Dimitris Vrakas is an Assistant Professor at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki (AUTh). He holds a PhD in Intelligent Planning Systems and has conducted post-doctoral research focusing on Planning for the Semantic Web. His professional experience includes roles as a Lecturer (per Greek law 407/80), Informatics Instructor in multiple educational institutions, and IT consultant for medium-sized enterprises through the Go-online Project. Education: Bachelor of Science (1995–1999): Informatics, Aristotle University of Thessaloniki PhD (2000–2004): Intelligent Planning Systems, Dept. of Informatics, AUTh Postdoctoral Research (2005–2007): Planning for the Semantic Web, AUTh Research Interests: Automated Planning, Heuristic Functions, Intelligent Autonomous Systems, Machine Learning Applications (Energy Management, NILM), Semantic Web Services, and Smart Environment Technologies. His work bridges theoretical AI advancements with practical implementations in energy systems and emergency response. Publications: Over 45 papers (including 15 journal articles) and 5 book chapters, with a focus on energy disaggregation, evacuation algorithms, and semantic web composition. His work demonstrates expertise in applying advanced techniques like GANs for data generation and reinforcement learning for adaptive planning. Awards: 2025 ACM SIGMOD Test-of-Time Award for contributions to time-series clustering Advising & Grants: Active in guiding PhD candidates (as per recent calls) and coordinating projects like the IRIS RISK PARADIGM for industrial safety. His work involves developing smart university platforms for energy monitoring and collaborating on EU-funded initiatives. Labs & Teams: Member of the Intelligent Systems Lab at AUTh, contributing to projects like the BOnSAI ontology for smart buildings and the IRISPortal for risk management. He also coordinates the Hellenic Artificial Intelligence Society and chairs conferences like SETN 2016.