جست‌وجوی استاد و پژوهشگر

• Mobile Computing
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Lei Wang is a Professor at Southeast University, affiliated with the School of Electronic Science and Engineering and the National Mobile Communications Research Laboratory in Nanjing, China. His research focuses on mobile and wearable computing, biomedical signal processing, and sensor networks. He leads projects in health monitoring using commercial devices, acoustic sensing, and privacy-preserving machine learning frameworks. His work bridges hardware innovation and algorithmic advancements, with applications in healthcare and secure data analytics. Wang has contributed to over 25 peer-reviewed articles since 2014, including seminal papers on smartphone-based vital sign monitoring and secure multi-party learning. His lab develops frameworks like SecretFlow-SPU and SCALAR, emphasizing practical implementations of theoretical concepts. Collaborations with industry partners and international institutions highlight his interdisciplinary approach. Award-winning projects include gait analysis systems for Parkinson’s disease and self-calibrated acoustic ranging techniques. He advises multiple graduate students and has secured grants from the National Natural Science Foundation of China. His lab’s work is deployed in clinical trials and commercial devices, emphasizing real-world impact.

• Artificial Intelligence
• Planning and Scheduling
• Reinforcement Learning
• +۵ مورد دیگر

Sheila A. McIlraith is a Professor in the Department of Computer Science at the University of Toronto, where she leads research at the intersection of artificial intelligence, knowledge representation, and formal methods. With an extensive publication record spanning over three decades, she has made significant contributions to planning, reinforcement learning, and epistemic reasoning in AI systems. Her research interests focus on developing formal frameworks for AI planning and decision-making, with particular emphasis on interpretable AI, reward specification in reinforcement learning, and multi-agent systems. She has pioneered work in reward machines for reinforcement learning, epistemic planning, and the application of formal methods to ensure safety and fairness in AI systems. Her recent work bridges symbolic AI with deep learning approaches to create more transparent and controllable intelligent agents. Analysis of her recent publications reveals a strong trend toward integrating formal specification languages with machine learning, particularly in reinforcement learning where she develops methods for specifying complex tasks using linear temporal logic and related formalisms. Her work increasingly addresses ethical considerations in AI, including fairness in sequential decision making and the impact of ethics education in computer science curricula. McIlraith has mentored numerous PhD students who have become prominent researchers in AI, including Rodrigo Toro Icarte, Toryn Q. Klassen, and Andrew C. Li. Her collaborative network spans major AI research institutions worldwide, with frequent collaborations with researchers at institutions including the Vector Institute and international universities. Her research group explores the theoretical foundations of AI planning while developing practical applications in areas including robotic assistance, ethics-aware AI, and interpretable decision systems. Current projects focus on language-guided reinforcement learning, multi-agent verification, and the development of tools for responsible AI development and deployment.

Jean Christoph Jungمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation
• Logic in Computer Science
• +۳ مورد دیگر

Prof. Jean Christoph Jung holds the Chair of Knowledge-Based Systems at TU Dortmund University since November 2022. He is affiliated with the Department of Computer Science within the Faculty of Computer Science. His academic journey includes a BSc in Computer Science (2006, TU Dresden), MSc in Computational Logic (2008, TU Dresden & Universidade Nova de Lisboa), and a PhD in Natural Sciences (2014, University of Bremen). Before his current position, he served as a research assistant at the University of Bremen (2009-2020), a postdoc at KU Leuven (2017/18), and an adjunct professor for Theoretical Computer Science at the University of Hildesheim (2020-2022). His research focuses on theoretical foundations of AI, logic-based knowledge representation (especially ontologies), and applications in computer science logic. Key contributions include work on ontology-mediated query answering, temporal logics, and PAC learning of logical concepts. He has co-authored numerous articles in top venues such as ICDT, DL, and AI conferences, addressing topics like query processing, logical separability, and formal verification. Though no specific grants or awards are mentioned, his extensive publication record highlights sustained contributions to the intersection of logic, databases, and AI. His work bridges theoretical advancements with practical applications in complex operational domains requiring robust knowledge representation systems.

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۴ مورد دیگر

Wei Cui is a Professor in the School of Automation Science and Engineering at South China University of Technology, with significant research contributions across artificial intelligence, signal processing, and remote sensing applications. Their work demonstrates strong interdisciplinary collaboration across engineering disciplines. Research interests span machine learning for multimodal data analysis, with particular expertise in fake news detection systems, transportation optimization, and remote sensing applications. Recent work shows increasing focus on robotics perception, quantum information processing, and healthcare technology applications. The research program integrates theoretical advances with practical implementations in real-world systems. Publication trends over the last three years reveal growing specialization in multimodal learning approaches, with approximately 40% of recent publications focusing on AI applications for social media analysis and disinformation detection, 25% on transportation and logistics optimization, 20% on remote sensing and environmental monitoring, and 15% on emerging applications in healthcare and robotics. Wei Cui's research has been supported through multiple collaborative projects addressing practical engineering challenges in urban infrastructure, healthcare logistics, and environmental monitoring systems. The work demonstrates strong industry-academia collaboration with practical implementations in power systems, transportation networks, and healthcare technologies. Current research directions include advancing multimodal perception systems for humanoid robots, developing more robust disinformation detection frameworks, and exploring quantum-inspired optimization techniques for complex engineering problems. The lab maintains active collaborations with researchers in China, the United States, and Europe across multiple engineering disciplines.

• Cryptography
• Side-channel Analysis
• Deep Learning Security
• +۵ مورد دیگر

Stjepan Picek serves as an Associate Professor at Delft University of Technology in the Intelligent Systems school and maintains a secondary affiliation with Radboud University's Digital Security Group in Nijmegen, The Netherlands. His research bridges cryptographic theory with practical machine learning applications, particularly focusing on security vulnerabilities in modern computing systems. Dr. Picek's research spans multiple critical areas in cybersecurity, with particular emphasis on deep learning-based side-channel analysis where he has published extensively on improving attack methodologies and countermeasures. His work on cryptographic Boolean functions explores evolutionary computation approaches to design functions with optimal cryptographic properties. More recently, his research has expanded to AI security , investigating backdoor attacks, membership inference vulnerabilities, and covert channels in large language models. Analysis of his recent publications reveals a clear trajectory toward integrating traditional cryptographic techniques with modern machine learning paradigms. His work demonstrates consistent innovation across three main axes: (1) advancing side-channel analysis through deep learning innovations, (2) developing evolutionary methods for cryptographic primitive design, and (3) identifying and mitigating security vulnerabilities in emerging AI systems. This multidisciplinary approach positions his research at the critical intersection of theoretical cryptography and practical security implementation. Dr. Picek maintains extensive collaborations with researchers across Europe, particularly with Domagoj Jakobovic (104 co-publications), Luca Mariot (56), Lejla Batina (56), and Guilherme Perin (56), indicating a well-established research network and leadership in his field. His publication record shows remarkable productivity with over 380 indexed publications, including 63 in 2023, 53 in 2024, and 24 in 2025, demonstrating sustained research momentum.

• Cybersecurity
• Smart Grids
• Internet of Things (IoT)
• +۳ مورد دیگر

Yang Xiao is a Professor in the Department of Computer Science at the University of Alabama (current affiliation) and previously affiliated with The University of Memphis. His research focuses on cybersecurity, smart grids, IoT systems, neural networks, control systems, and machine learning applications. He has contributed to advanced methods in ransomware detection, electricity theft prevention in smart grids, and real-time phishing attack identification using deep learning. Key research areas include: Cybersecurity frameworks for critical infrastructure (e.g., water distribution systems, maritime navigation) IoT-enabled smart building energy management and anomaly detection Control systems for unmanned vehicles and cyber-physical systems under adversarial conditions Machine learning models for imbalanced data and sensor-based decision systems Recent work emphasizes adaptive control algorithms for nonlinear systems, reinforcement learning in autonomous systems, and distributed energy management for maritime applications. His publications span top-tier journals like IEEE Transactions on Cybernetics, IEEE Internet of Things Journal, and IEEE Transactions on Neural Networks and Learning Systems.

• Computer Vision
• Machine Learning
• Continual Learning
• +۵ مورد دیگر

Tomasz Trzcinski is an active computer vision and machine learning researcher with a prolific publication record across top-tier conferences and journals including CVPR, ECCV, WACV, NeurIPS, and IEEE Access. His work spans multiple institutions, primarily collaborating with researchers from Polish academic and research organizations as evidenced by co-author patterns and institutional affiliations in his publications. 229+ publications documented in DBLP (2012-2025) Active contributor to computer vision and machine learning communities Regular presence at major conferences (CVPR, ECCV, NeurIPS) Trzcinski's research primarily focuses on continual learning , where he has made significant contributions to addressing catastrophic forgetting and knowledge retention. His work extends to 3D vision , particularly neural radiance fields (NeRF), Gaussian splatting, and point cloud processing, with applications in robotics and scene understanding. He also explores medical imaging applications, collaborating with medical researchers on projects involving pulmonary artery pressure estimation and fetal birth weight prediction. His methodological contributions include innovations in hypernetworks, vision transformers, and test-time adaptation techniques. Analysis of his recent publications (2023-2025) reveals a strong emphasis on continual learning methodologies, with approximately 40% of his work addressing challenges in this domain. His research demonstrates a consistent trajectory toward more efficient and robust neural network architectures, with increasing focus on practical applications in robotics, medical imaging, and real-world adaptation scenarios. The integration of vision-language models and contrastive learning approaches represents his latest research direction, indicating adaptation to emerging trends in the field. As a senior researcher, Trzcinski frequently serves as a corresponding author and collaborates with numerous junior researchers, suggesting an active supervisory role. His work shows evidence of securing research funding through collaborations with institutions involved in medical imaging and computer vision applications. Trzcinski maintains active research collaborations across multiple teams, particularly with groups focused on continual learning (Twardowski, Deja, Cygert), 3D vision (Kania, Kowalski), and medical applications (Sitek, Grzeszczyk). His research group appears to maintain strong connections between theoretical machine learning advancements and practical applications in healthcare and robotics.

• Control Theory
• Switched Systems
• H∞ Control
• +۵ مورد دیگر

Ruihua Wang is a researcher with a focus on Control Theory , Switched Systems , and H∞ Control , as evidenced by her extensive publication record in journals like Information Sciences and Trans. Inst. Meas. Control . Her work spans applications in engineering systems, biomedical devices, and educational technology. Ruihua Wang’s research interests include: Control Theory and Lyapunov Function approaches Switched and Markovian Jump Systems Machine Learning for predictive maintenance and signal processing Biomedical engineering for wearable health monitoring Educational technology in multimedia learning Her recent publications highlight trends in finite-time control strategies, resilient control under cyberattacks , and adaptive algorithms for systems like bearings and rotating machinery. She also explores interdisciplinary applications, such as instructor voice enthusiasm in educational tools.

Andrea M. Tonelloمشاهده پروفایل

استاد

• Power Line Communications
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Andrea M. Tonello is a distinguished academic researcher specializing in Power Line Communications (PLC), Machine Learning, and Signal Processing. His work bridges theoretical advancements with practical applications in communication systems, robotics, and smart grid technologies. He has authored over 200 publications across leading conferences and journals, with a focus on optimizing communication protocols, enhancing system robustness through noise mitigation, and applying AI techniques to solve challenges in wired/wireless networks. Research interests include: 1) Developing advanced PLC frameworks for smart grids and IoT deployments, 2) Designing machine learning algorithms for classification and signal processing under noisy conditions, 3) Investigating control systems for underactuated robotic mechanisms, and 4) Exploring full-duplex communication techniques for improved bandwidth utilization. Recent works emphasize energy-efficient sensor networks for agriculture and topology-aware learning models for PLC quality prediction. His articles consistently address cutting-edge topics like reinforcement learning for system stability, f-divergence based classification methods, and channel modeling innovations. Notable contributions include pioneering work on in-band full-duplex PLC systems and robust neural decoding techniques leveraging mutual information principles. Though no specific grants or advising information is listed here, his prolific collaboration network (evident from co-authorship patterns) indicates active participation in collaborative research initiatives. Laboratory activities focus on experimental validation of communication protocols and algorithmic performance evaluation in real-world scenarios.

• Robotics
• Navigation Systems
• Sensor Fusion
• +۵ مورد دیگر

Chan Gook Park is a prominent researcher in robotics, navigation systems, and sensor fusion with an extensive publication record spanning over two decades. His work primarily focuses on visual-inertial odometry, LiDAR-based navigation, indoor positioning systems, and applications in both terrestrial and space environments. Dr. Park's research interests encompass robotics, sensor fusion, computer vision, and navigation systems. His work demonstrates a consistent focus on developing robust algorithms for visual-inertial odometry under challenging conditions, including low-light environments and indoor settings. He has made significant contributions to pedestrian navigation using wearable devices, lunar landing navigation systems, and infrared target detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward deep learning integration in traditional navigation systems, with increasing focus on event-based vision sensors, multi-sensor fusion techniques, and applications in wearable technology. His work spans both theoretical algorithm development and practical implementations for real-world navigation challenges. Dr. Park has mentored numerous researchers who frequently appear as first authors on his publications, including Jae Hong Lee, Hanyeol Lee, and In Ho Lee. His collaborative network extends across various Korean institutions, with frequent co-authorship patterns suggesting a well-established research laboratory focused on navigation and sensor systems. His research group appears to maintain strong connections with the aerospace and robotics communities, regularly publishing in top-tier IEEE conferences and journals including IEEE Transactions on Robotics, IEEE Access, and proceedings from ICRA and PLANS conferences. The laboratory seems particularly focused on practical implementations of navigation systems for both terrestrial and space applications.

• Computer Vision
• Computational Imaging
• Non-Line-of-Sight Imaging
• +۷ مورد دیگر

Suren Jayasuriya is a highly active faculty-level researcher in computer vision and computational imaging, with 79 refereed publications (2014-2025) in leading venues such as CVPR, ICCV, ECCV, NeurIPS, IEEE TPAMI and ACM TOG. His work integrates physics-based models with modern machine learning to tackle problems like non-line-of-sight imaging, atmospheric turbulence removal, neural 3D reconstruction, and speech enhancement, while also advancing STEM education through AI. Education & Affiliation: No explicit institutional details are contained in the supplied DBLP extract; however, the sustained publication record and extensive student mentoring indicate a tenured or tenure-track professorship within an engineering or computing department. Research Interests: Jayasuriya’s interests sit at the intersection of computer vision, computational photography, and physics-based machine learning . He develops algorithms that exploit optical and acoustic phenomena for tasks such as seeing around corners, correcting atmospheric distortion, and reconstructing 3D scenes from sparse sonar or radar data. Additional threads include energy-efficient tracking, robust speech processing, and AI-supported pedagogical innovation. Publication Trends: Across the 15 most recent works (2022-2025) his papers exhibit a clear thematic arc: neural representations for multimodal fusion (camera-sonar, radar-vision), unsupervised video restoration under atmospheric turbulence, attention-driven non-line-of-sight tracking, and evaluation of large multimodal models for perceptual reasoning. These contributions simultaneously advance core vision methodologies and demonstrate interdisciplinary applications spanning robotics, environmental monitoring, underwater perception, and education. Students & Mentoring: Jayasuriya has advised an active cohort of graduate researchers who appear repeatedly as co-authors, including Sreenithy Chandran, Ripon Kumar Saha, Albert W. Reed, Joshua D. Rego, Dehao Qin, Jianwei Zhang, Odrika Iqbal, Sameeksha Katoch, Md. Farhan Tasnim Oshim, and Shenbagaraj Kannapiran, among others. Scientific Awards: No awards are listed in the supplied extract; the awards field is left empty. Labs & Teams: While specific laboratory names are not disclosed, the collaborative scope—encompassing hardware-software co-design, field deployments, and educational outreach—suggests he leads or co-leads a research group with access to specialized imaging and robotics facilities.

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

Hong Liu is a Professor at Shandong University's School of Computer Science and Technology, with an extensive publication record spanning multiple disciplines within computer science and engineering. His research spans computer vision, machine learning, medical imaging, robotics, and signal processing, with over 500 publications listed in the dblp database from 1993 to 2025. Dr. Liu's research interests focus on the intersection of artificial intelligence and practical applications across various domains. His recent work demonstrates expertise in deep learning architectures, medical image analysis, robotics navigation, and computer vision systems. He has made significant contributions to medical imaging techniques, particularly in segmentation methods using quaternion mathematics and self-attention mechanisms, as well as applications in liver disease diagnosis and pediatric fracture detection. The publication trends show increasing activity in recent years, with substantial contributions in 2023-2025 across IEEE journals and conferences. His work frequently appears in high-impact venues including IEEE Transactions on Medical Imaging, IEEE Access, and Pattern Recognition, demonstrating both theoretical rigor and practical applications. His research bridges theoretical computer science with real-world engineering and medical challenges, particularly in China's healthcare and technology sectors. Dr. Liu has collaborated extensively with researchers across Chinese institutions including Shandong University, Harbin Institute of Technology, and Huazhong University of Science and Technology, as well as international partners. His work shows strong interdisciplinary connections between computer science, electrical engineering, and medical applications.

• Biomedical Engineering
• Machine Learning
• Signal Processing
• +۱۲ مورد دیگر

Abbas Z. Kouzani is a Professor at Deakin University's School of Engineering, specializing in Electrical and Computer Engineering. His research spans interdisciplinary domains including biomedical engineering, artificial intelligence, sensor technology, and embedded systems. Key areas: Machine Learning, Neurotechnology, Sustainable Robotics, Mental Health Analytics Recent work focuses on AI-driven solar defect detection, low-resource FPGA implementations, and 4D-printed robotic joints with reinforcement learning. Collaborations include Sui Yang Khoo, Scott D. Adams, and M. A. Parvez Mahmud.

• Wireless Communications
• Quantum Machine Learning
• Digital Twin Technology
• +۷ مورد دیگر

Trung Quang Duong is a leading professor in wireless communications and quantum technologies, with a focus on 6G networks, digital twins, and the metaverse. His research spans secure data sharing, UAV-assisted communication, and AI-driven network optimization. He contributes extensively to IEEE journals and conferences as an author and editor. Dr. Duong's work integrates quantum machine learning with digital twin systems to enhance vehicular networks, IoT, and satellite-terrestrial communication. He explores counterfactual quantum protocols, blockchain-based security, and energy-efficient edge computing, positioning himself at the forefront of next-generation wireless standards. His 2025 publications highlight advancements in quantum-enhanced reinforcement learning, UAV swarm efficiency, and digital twin applications for healthcare and environmental monitoring. Collaborations with researchers like Octavia Dobre and Hyundong Shin reflect his role in shaping 6G research agendas through federated learning and semantic communication frameworks.

• Machine Learning
• Computer Vision
• Human Activity Recognition
• +۳ مورد دیگر

Fadi Al Machot is an active researcher in artificial intelligence and machine learning, focusing on applications in human activity recognition, emotion detection, and sensor-based systems. His work often explores zero-shot learning, deep learning frameworks, and the integration of symbolic knowledge into neural networks. Collaborations with co-authors such as Kyandoghere Kyamakya highlight interdisciplinary research in complex systems and adaptive technologies. Key research areas include: Human Activity Recognition (HAR), Emotion Recognition, Sensor Networks, Zero-Shot Learning, and Explainable AI. Recent contributions emphasize noise-resilient time series forecasting and transparent AI system development using ontologies and logical reasoning. Publications span prestigious journals like Sensors, IEEE Access, and Symmetry, with notable work in conferences such as WACV, COINS, and AVSS. His research bridges theoretical advancements with practical implementations in healthcare, transportation, and manufacturing domains.