جست‌وجوی استاد و پژوهشگر

• Recommendation Systems
• Machine Learning
• Medical Informatics
• +۴ مورد دیگر

Zhaopeng Qiu is an active researcher in computer science with a strong publication record spanning from 2012 to 2025. His work primarily focuses on recommendation systems, machine learning, and their applications in healthcare and online services. He frequently collaborates with researchers including Xian Wu, Zhi Zheng, Hengshu Zhu, and Hui Xiong on projects related to large language models, medical informatics, and job recommendation systems. Dr. Qiu's research interests include Recommendation Systems, Machine Learning, Medical Informatics, Natural Language Processing, Artificial Intelligence, Data Mining, and Healthcare AI. His work demonstrates a clear evolution from earlier research in mobile robotics (2012-2015) to current cutting-edge work applying large language models to recommendation problems across various domains. Analysis of his recent publications shows a strong trend toward leveraging large language models for recommendation tasks, with significant contributions in medication recommendation, job matching, and fairness-aware systems. His work bridges theoretical AI advancements with practical applications, particularly in healthcare contexts where AI can have significant real-world impact. While specific scientific awards aren't documented in the available publications, his work appears in top-tier venues including WWW, AAAI, KDD, and IEEE/ACM transactions journals, indicating recognition within the research community. Dr. Qiu's research has practical implications for online platforms, healthcare systems, and labor market technologies. His recent focus on large language models for recommendation suggests he's at the forefront of integrating emerging AI capabilities with traditional recommendation paradigms.

• Wireless Networking
• Mobile Computing
• Visible Light Communication
• +۷ مورد دیگر

Chenren Xu is an Associate Professor in the Department of Computer Science at Peking University's School of Electronics Engineering and Computer Science. With a prolific publication record spanning from 2016 to 2025, Xu has established themselves as a leading researcher in wireless networking, mobile systems, and novel communication technologies. Their work frequently appears in top-tier conferences including SIGCOMM, MobiCom, NSDI, and SenSys, as well as leading journals such as IEEE Transactions on Mobile Computing and IEEE Journal on Selected Areas in Communications. Xu's research focuses on cutting-edge problems in wireless communications, with particular emphasis on visible light communication, backscatter networking, high-speed rail networking, and satellite-terrestrial integrated systems. Their work bridges theoretical innovation with practical implementation, often developing novel systems that address real-world networking challenges. Recent publications demonstrate growing leadership in the field, with Xu increasingly serving as first author on significant contributions. The research portfolio shows consistent evolution from foundational mobile networking concepts toward more specialized areas including acoustic sensing, visible light communication, and space-air-ground integrated networks. Xu's work demonstrates strong interdisciplinary connections between networking, systems, and hardware design, with practical applications spanning transportation systems, healthcare monitoring, and next-generation wireless infrastructure. Xu actively collaborates with researchers across institutions, maintaining particularly strong ties with Peking University colleagues including Daqing Zhang, Xuanzhe Liu, and Lingyang Song. Their research has practical implications for 5G/6G deployment, IoT systems, and future mobile networking architectures.

• Computer Vision
• Machine Learning
• Rail Transportation Systems
• +۵ مورد دیگر

Yu Qian is a Professor in the Department of Electrical and Computer Engineering at the University of Southern California's Viterbi School of Engineering, with extensive research contributions spanning computer vision, machine learning, and transportation systems. Their work bridges theoretical advances with practical applications, particularly in railroad safety and ferroelectric computing architectures. Research interests focus on computer vision applications for transportation safety, including railroad inspection systems and pedestrian behavior analysis at grade crossings. Additional expertise includes ferroelectric computing-in-memory architectures, micro-expression recognition, and multimodal sentiment analysis. The research program demonstrates strong interdisciplinary collaboration across engineering disciplines. Recent publications (2023-2025) show a clear trend toward hardware-algorithm co-design for specialized computing, particularly using ferroelectric devices for optimization problems, alongside continued work in transportation safety applications. The research spans both theoretical contributions to neural network architectures and practical implementations for real-world problems. While specific awards aren't documented in the provided records, the extensive publication record in high-impact venues demonstrates significant scholarly contribution to the field. The research program appears well-funded, supporting numerous collaborative projects across multiple institutions.

• Computer Vision
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Computer Science
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Hao Tian is a professor at Georgia State University's Department of Computer Science, with affiliations at institutions including Hubei University of Economics, University of Alberta, and Shanghai Key Laboratory of Trustworthy Computing. His research spans interdisciplinary domains at the intersection of computer science, applied mathematics, and engineering. Key research areas include: Machine learning architectures for image and signal processing Graph-theoretical methods for pattern recognition Federated learning and edge computing frameworks Fuzzy systems for industrial process control Numerical methods in computational mechanics Recent publications demonstrate trends in: Higher-order network representation and learning Physics-informed neural networks for financial modeling Peridynamic models for crack propagation analysis Transformer-based approaches in point cloud processing His work has been applied to municipal solid waste incineration, biomedical imaging, and smart city infrastructure, reflecting a strong engineering focus.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Huan Zhou is a prolific researcher affiliated with multiple institutions including the University of Oldenburg , China Three Gorges University , and Wuhan University . His work spans Computer Science , Mathematics , and Engineering disciplines. Key affiliations: Signal Processing Group (Oldenburg), Computer and Information Technology College (China Three Gorges University), Geodesy and Geomatics (Wuhan University) Research interests include: Graph Theory : Resistance spectra, planar graph properties, and spectral analysis Optimization Algorithms : Manta ray foraging, vortex search, and swarm intelligence applications Computer Vision : Road segmentation, stereo matching, and remote sensing image analysis Telecommunications : RIS-assisted wireless systems and beamforming techniques Machine Learning : Anomaly detection, continual learning, and transformer architectures Recent publications demonstrate cross-domain impact in bioinformatics (TriGCN for medicine), network systems (Plover distributed logging), and computational pathology (glioma diagnosis with transformers).

• Wireless Sensor Networks
• Machine Learning
• Internet of Things (IoT)
• +۳ مورد دیگر

Ali Nauman is a researcher affiliated with Yeungnam University in Gyeongsan, South Korea, specializing in wireless communications, machine learning, and IoT systems. His work focuses on cutting-edge technologies for 5G/6G networks, UAVs, and biomedical applications. Co-author in 61+ academic publications (2019–2025) Collaborates extensively with scholars from King Saud University, University of Turku, and Yeungnam University Research Interests span Wireless Sensor Networks , Machine Learning for Communications , and AI-Driven Network Optimization . Key areas include electromagnetic emission management, underwater network topologies, and secure biometric systems. Recent publications highlight AI integration in 6G networks , UAV communication frameworks , and privacy-preserving biometrics . His work bridges theoretical advancements with practical implementations in smart cities and industrial IoT.

• Wireless Communications
• Deep Learning
• Signal Processing
• +۷ مورد دیگر

Thien Huynh-The is a Professor in the Department of Electrical Engineering at Chungnam National University's College of Engineering, where they lead research in wireless communications, deep learning applications, and IoT systems. With over 150 publications spanning from 2014 to 2025, their work demonstrates sustained academic productivity with significant contributions to 5G/6G networks, spectrum sensing, and federated learning architectures. Research interests focus on the intersection of deep learning and wireless communications, particularly in automatic modulation classification, semantic segmentation for remote sensing, and energy-efficient communication protocols. Their innovative approaches include developing specialized CNN architectures like SRNet for spectrum sensing and CosPoint Transformer for 3D semantic segmentation, addressing critical challenges in 5G/6G systems and metaverse infrastructure. Recent work explores STAR-RIS-aided networks, waveform classification for integrated radar-communication systems, and privacy-preserving federated learning for healthcare applications. Analysis of publication trends reveals increasing focus on metaverse technologies and 6G communications since 2022, with significant contributions to IEEE Communications Surveys & Tutorials and IEEE Internet of Things Journal. Key research areas include federated learning optimization, channel estimation techniques using attention networks, and semantic communication frameworks for edge-assisted metaverse applications. Collaborative research spans multiple institutions with frequent co-authorship with Dong-Seong Kim, Quoc-Viet Pham, and Won-Joo Hwang. Current projects address critical challenges in wireless power transfer, spectrum efficiency, and computational resource allocation in next-generation networks.

• Cybersecurity
• Machine Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Qusay H. Mahmoud is affiliated with the University of Ontario Institute of Technology, Canada, and previously held roles at the University of Guelph (2002–2013) and completed his PhD at Middlesex University, London (2002). His research focuses on cybersecurity, machine learning, IoT systems, blockchain, and smart policing. He has authored over 200 peer-reviewed publications in journals like IEEE Access, Sensors, and Future Internet, and conferences such as Canadian AI, IEEE CCWC, and SMC. His work spans anomaly detection in IoT networks, adversarial machine learning, cryptocurrency fraud analysis, and reinforcement learning applications. Research Interests: Dr. Mahmoud’s expertise includes developing AI-driven solutions for cybersecurity challenges, IoT security frameworks, blockchain applications in healthcare and finance, and enhancing smart policing through machine learning. He also explores adversarial training for robust deep learning models and reinforcement learning for bridging virtual-physical environment gaps. His contributions emphasize real-world applications, such as phishing detection, fault-injection attack mitigation, and cryptocurrency market analysis. Articles Trends: Recent work highlights the use of large language models (LLMs) in crime prediction, adversarial robustness in GANs, and reinforcement learning for robotics. His publications often intersect machine learning with cybersecurity, IoT, and blockchain, addressing practical challenges like anomaly detection, fraud prevention, and system reliability. Grants & Advising: While specific grants are not detailed, his extensive publication record suggests sustained research activity. No formal student advisees are listed, though collaborations with co-authors like Akramul Azim and Michael Lescisin indicate active teamwork. Labs/Teams: Engaged in interdisciplinary projects at the University of Ontario Institute of Technology, focusing on AI, cybersecurity, and IoT systems. Collaborations extend to institutions globally, addressing emerging tech challenges like blockchain in healthcare and edge computing security.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Michel Kulhandjian is a researcher specializing in Wireless Communication and Machine Learning applications. His work spans NOMA Systems , RF Fingerprinting , and Drone-Assisted Sensing across academic institutions. Key collaborations with Carleton University , Carleton University , and University of Ottawa researchers Active in 5G/6G technologies and IoT Security since 2018 His recent articles focus on: 2024 : Pedestrian detection, drone-based tree health monitoring, and industrial IoT security 2025 : AI-powered agricultural robotics Scientific contributions include: Code design for OTFS-NOMA systems Low-complexity detection algorithms 3D CNN frameworks for signal analysis RF Fingerprinting under impaired channels

• Machine Learning
• Healthcare Informatics
• Explainable AI
• +۶ مورد دیگر

Kai-Chih Pai is an Associate Professor at China Medical University's College of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning over 14 years. His work bridges computer science and healthcare, focusing on practical AI applications that address critical medical challenges. Dr. Pai's research interests center around Machine Learning applications in healthcare , Explainable AI systems , Natural Language Processing for Chinese language , and Educational Technology . His work demonstrates a strong commitment to developing AI solutions that are not only technically sophisticated but also interpretable and clinically useful. His research trajectory shows a clear evolution from educational technology applications to increasingly sophisticated healthcare AI systems. His publication record reveals significant contributions to medical decision support systems , particularly in acute kidney injury prediction , pneumonia diagnosis , and mortality prediction in critical care settings. More recently, he has been exploring cutting-edge applications of large language models for industrial knowledge management. His work consistently emphasizes the importance of model interpretability in medical contexts. Federated machine learning approaches for multi-institutional medical research Privacy-preserving healthcare technologies using homomorphic encryption Adaptive learning systems for Chinese language education Predictive analytics for critical care medicine Dr. Pai has established a productive research program with consistent publication output in high-impact venues, demonstrating expertise that spans both theoretical AI development and practical healthcare applications. His collaborative work with medical professionals across Taiwan highlights the interdisciplinary nature of his research and its real-world impact.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Kaushik Roy is an academic researcher affiliated with multiple institutions including North Carolina State University, Purdue University, and North Carolina A&T State University. His primary focus is on computer science, cybersecurity, and AI-driven solutions for real-world challenges. He has extensive experience in machine learning applications for intrusion detection, deepfake detection, biometrics, and federated learning frameworks. His work often bridges theoretical advancements with practical implementations in domains like IoT security, healthcare analytics, and cyber-physical systems. Affiliations: North Carolina State University, Purdue University, North Carolina A&T State University, and others Research Interests: Cybersecurity, Deep Learning, Biometric Authentication, Federated Learning, and AI Ethics He has co-authored over 105 publications, focusing on topics like anomaly-based intrusion detection, malware traffic classification, and privacy-preserving techniques. His recent work emphasizes federated learning architectures for healthcare and transportation IoT systems. Roy collaborates with industry partners to translate research into deployable solutions, particularly in edge computing and sensor networks.

• Cybersecurity
• Machine Learning
• Federated Learning
• +۵ مورد دیگر

Jin-Hee Cho is a prominent researcher actively contributing to computer science domains since 2018, with over 35 publications in 2024-2025 alone. She focuses on cybersecurity, machine learning, and interdisciplinary applications across autonomous systems, smart farms, and social media safety. Coauthor network includes: Terrence J. Moore (63), Feng Chen (31), Dong Seong Kim (28), Frederica Nelson (24), Hyuk Lim (24) Publishes in venues: IEEE Transactions, ACM, MILCOM, DSN, KDD, ICWSM Research Trends (2024-2025): Uncertainty quantification in deep learning systems Federated learning for sustainable agriculture monitoring Bayesian network modeling for autonomous vehicle security Combating cybergrooming through dialogue generation Her work bridges technical cybersecurity with social dimensions, as seen in publications about social capital-based defense mechanisms and disinformation resilience frameworks.

• Bioinformatics
• Machine Learning
• Network Science
• +۴ مورد دیگر

Anne Hartebrodt is a Postdoctoral Researcher at the Biomedical Network Science Lab in the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany. She has been working at FAU since March 2023, focusing on network-based embeddings and carbon-aware computing for bioinformatics following her PhD completion at the University of Southern Denmark. Her research spans multiple cutting-edge domains at the intersection of AI and biomedical science: Bioinformatics and computational biology with emphasis on network science Federated learning and privacy-preserving computation for biomedical applications Single-cell omics analysis and gene regulatory network inference Carbon-aware computing for sustainable bioinformatics research Machine learning applications in proteomics and cancer research Dr. Hartebrodt's publication record demonstrates expertise in developing computational methods that bridge theoretical machine learning with practical biomedical challenges, particularly in maintaining data privacy while enabling collaborative research across institutions. Her work on federated learning approaches has been published in high-impact journals including Nature Computational Science and Bioinformatics. Her research leadership includes: Principal investigator for the 'Dimensionality reduction for molecular data based on explanatory power of differential regulatory networks' project (NetMap) funded by the Bundesministerium für Forschung, Technologie und Raumfahrt (2023-2026) Development of FedProt for privacy-preserving protein analysis Creation of Decoil for reconstructing extrachromosomal DNA structures Advancements in federated singular value decomposition methods Dr. Hartebrodt actively supervises multiple graduate students working on gene regulatory networks, differential expression analysis, and carbon-aware optimization. Her laboratory work in the Biomedical Network Science Lab focuses on developing innovative approaches to analyze complex biological networks while addressing computational sustainability through carbon-aware computing methods.