جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Blockchain Technology
• Machine Learning
• +۷ مورد دیگر

Prof Jianming Yong is a Professor of Information Systems at the University of Southern Queensland's School of Business. His research focuses on AI-driven systems, blockchain technology, cybersecurity in telecommunications, and healthcare informatics. He holds an MEng from South East University and a PhD from Swinburne University of Technology. Research interests include: Artificial Intelligence applications in healthcare and finance Blockchain protocols for cross-industry solutions Data privacy in cloud computing and 5G networks Machine learning for medical diagnostics and treatment optimization Recent work explores causal learning with graph neural networks, UAV-enabled transportation systems, and ethical AI frameworks. His publications address challenges in decentralized finance (DeFi), multimodal logistics sustainability, and pandemic response systems integrating IoT/5G/AI. No scientific awards have been listed. His work spans theoretical advancements in cybersecurity and practical implementations in healthcare IT systems. Ongoing projects involve developing adaptive multi-agent systems for real-time healthcare interventions and secure blockchain applications in e-commerce.

• Computer Science
• Signal Processing
• Data Networking
• +۳ مورد دیگر

Majed Alhaisoni is a **Researcher** with significant contributions to computer science and engineering domains, particularly in medical imaging, signal processing, and peer-to-peer (P2P) networking. His work spans topics like smart healthcare systems , IoT-based communication frameworks , and biometric gait recognition . He has collaborated extensively with scholars including Muhammad Attique Khan, Antonio Liotta, and Usman Tariq, publishing in venues such as IEEE Access , Sensors , and Future Gener. Comput. Syst. . Research Interests include applying deep learning and optimization algorithms to solve challenges in medical diagnostics , IoT networks , and biometric security . His work on gait pattern recognition and medical image segmentation demonstrates interdisciplinary expertise. Publication Trends reveal a focus on computer vision , signal processing , and health informatics , with recent advancements in IoT device design , data protection , and multi-stream feature fusion .

Kun Sunمشاهده پروفایل

استاد

• Trustworthy Computing
• Network Security
• Artificial Intelligence Security
• +۳ مورد دیگر

Dr. Kun Sun is a tenured Professor in the Department of Information Sciences and Technology at George Mason University. He serves as Director of the Sun Security Laboratory and Associate Director of the Center for Secure Information Systems. His research spans network security, AI/ML security, software security, and IoT systems, with over 130 publications in these domains. Research Focus : Trustworthy computing environments, moving target defense, AI security, smartphone security, software-defined networking, and software security. Notable Contributions : Innovations in network attack detection (TCP/IP, Wi-Fi, SDN), secure AI frameworks (DNN watermarking, adversarial defense), and container/IoT security mechanisms. Awards & Recognition : George Mason University Presidential Award for Faculty Excellence in Research (2022). His recent work emphasizes graph-based anomaly detection (SentinelAgent), cryptographic chain integration for model security (ChainMarks), and novel frameworks for Android malware classification (FAMCF) and firmware updates (RapidPatch). He also investigates security challenges in Rust programming (TYPEPULSE) and Bluetooth networks (Blueswat).

• Cybersecurity
• Machine Learning
• Biometric Authentication
• +۴ مورد دیگر

Chan Yeob Yeun is a Professor at United Arab Emirates University in the College of Information Technology, with prior affiliation at Qatar University's Department of Computer Science. His research focuses on cybersecurity, machine learning, and biometric authentication systems, particularly in industrial IoT and digital twin environments. Current Affiliation: United Arab Emirates University, College of Information Technology Prior Affiliation: Qatar University, Department of Computer Science His work spans multiple domains including: Explainable AI for healthcare and security Biometric authentication using EEG and ECG signals Blockchain applications in UAV networks and industrial systems Federated learning security and data poisoning defense Digital twin threat modeling Recent publications demonstrate technical innovation in: YOLOv3-based safety monitoring Physics-informed neural networks Hybrid TESLA protocol security Shadow AI cybersecurity Metaverse threat intelligence

Prashan Premaratneمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Pattern Recognition
• Image Processing
• +۴ مورد دیگر

Prashan Premaratne is a Senior Lecturer at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering, where he has been affiliated since 2003. His academic qualifications include a Doctor of Philosophy in Image/Signal Processing from the National University of Singapore (1999-2001), a Bachelor of Engineering in Optical Communications and Signal Processing from the University of Melbourne (1994-1997), and a Graduate Certificate in Higher Education from the University of Wollongong (2008-2009). His research focuses on: Computer vision systems for gesture recognition and object detection Advanced image/video processing techniques including steganography Artificial intelligence applications in speech processing and sensor networks Energy system optimization through electromagnetic modeling His publications demonstrate strong focus on computer vision (45%), signal processing (30%), and AI applications (25%), with recent work emphasizing real-time systems optimization and deep learning architectures. Award recognition includes: Vice Chancellor's Award for Teaching Excellence (2020) IEEE Senior Member recognition (2008) Singapore Science Award (1999) He actively supervises graduate researchers in computer vision and AI, with 11 completed PhD/MPhil students. Grant funding includes internal university awards for robotics education and face detection technology. He serves as program chair for major AI conferences and reviews for national research councils.

• Cybersecurity
• Database Forensics
• Machine Learning Applications in Security
• +۴ مورد دیگر

Peter Frühwirt is an External Lecturer in the Department of Information Systems Engineering at Vienna University of Technology (E194). His research focuses on cybersecurity, database forensics, and usable security, with a particular emphasis on tamper-proof systems, QR code vulnerabilities, and forensic-aware database solutions. Education : Dipl.-Ing. Dr.techn., BSc BSc Research Interests : Cybersecurity, Database Forensics, Machine Learning Applications in Security, QR Code Security, Web Application Security, Traffic Classification His recent publications analyze forensic-aware database protocols, QR code threats, and machine learning applications for attacker profiling. He teaches courses including Advanced Software Engineering, Software Engineering Project, and Software Quality Assurance.

Yunsi Feiمشاهده پروفایل

استاد

• Hardware-oriented security
• Computer Architecture
• Embedded Systems
• +۷ مورد دیگر

Yunsi Fei is a Professor in the Electrical and Computer Engineering Department at Northeastern University, serving concurrently as Associate Dean of Faculty Affairs. She leads the Northeastern site of the NSF IUCRC Center for Hardware and Embedded System Security and Trust (CHEST). Her research focuses on hardware-oriented security, computer architecture, embedded systems, and IoT security, with significant contributions to mitigating side-channel and fault attacks on neural networks and hardware systems. Fei holds a PhD in Electrical Engineering from Princeton University (2004), and bachelor’s and master’s degrees in Electronic Engineering from Tsinghua University. She joined Northeastern in 2011 after faculty roles at the University of Connecticut. Her research interests span secure computer architecture, energy-efficient embedded systems, and underwater sensor networks. Notable projects include RINGS (a NSF-funded IoT resilience initiative) and secure RISC-V processor design. She has received the NSF CAREER Award and multiple best paper awards at top conferences. Fei’s work integrates hardware-software co-design to address vulnerabilities in AI accelerators and cryptographic systems. She leads the Energy-Efficient and Secure Systems (ENESS) Lab and collaborates with industry and academia through CHEST. Recent grants include $1.5M for cybersecurity in additive manufacturing and $1M for spectrum-agile IoT systems. Her awards include a 2023 Distinguished Paper Award (AsiaCCS) and 2022 Best Paper (Great Lake VLSI). She mentors students like Ruyi Ding, who joined LSU as faculty in 2025. Fei also chairs sessions on hardware security and is an affiliated faculty member with Northeastern’s Institute of Information Assurance.

Stephen Huangمشاهده پروفایل

استاد

• Cybersecurity
• Intrusion Detection
• Data Science
• +۳ مورد دیگر

Stephen Huang is Professor of Computer Science at the University of Houston's College of Natural Sciences and Mathematics. He directs the Center for Cybersecurity Research and has held administrative roles including Department Chairman and Director of Graduate Studies. He received his PhD in Computer Science from the University of Texas-Austin and served as National Research Council-NASA Senior Research Associate at NASA Goddard Space Flight Center. His 40+ year career encompasses cybersecurity research, algorithm design, and academic leadership. Research focuses on intrusion detection systems, malware analysis, network security, and applied algorithms. His work integrates machine learning with security applications, particularly in anomaly detection and behavioral analysis. Recent projects include NSA-funded research on automated intrusion detection through metadata traffic analysis. Publications demonstrate progression from foundational algorithms to advanced AI applications in security. Recent articles focus on VPN detection, Android malware analysis, and large language model interpretability, consistently applying computational theory to cybersecurity challenges. He leads the GAANN Fellowship Program in Cybersecurity and Research Experience for Undergraduates (REU) initiatives. His laboratory develops practical security solutions through collaborations with government agencies and industry partners.

Xavier de Carné de Carnavaletمشاهده پروفایل

استادیار

• Network Security
• Privacy
• Web Security
• +۴ مورد دیگر

Xavier de Carné de Carnavalet is an Assistant Professor at Radboud University's Digital Security group within the Faculty of Science. His research focuses on network security, privacy, web security, and applications of machine learning in cybersecurity. He holds a PhD from Concordia University (2019) and completed postdoctoral work at Carleton University's CCSL lab. Research Interests: Network Security & Privacy Passwords & Authentication Systems Embedded/Firmware Security Machine Learning for Cybersecurity Home Router Vulnerabilities TLS Interception Mechanisms Publications highlight trends in analyzing security configurations (home routers), detecting vulnerabilities (command injection in firmware), and evaluating TLS interception practices. His work often bridges technical security analysis with human factors influencing secure practices. He has contributed to major conferences like CHI, NDSS, and USENIX Security. Notable projects include analyzing password strength meters and verifying TrueCrypt's build integrity. Current research includes database breach analysis and cryptographic software hardening. Labs/Teams: Active contributor to the Digital Security group at Radboud University, collaborating with institutions like HK PolyU and Concordia's Madiba Security Research Group.

Wei-Shinn Kuمشاهده پروفایل

استاد

• Data management systems
• Data analytics
• Cybersecurity
• +۳ مورد دیگر

Wei-Shinn Ku is a Professor in the Department of Computer Science and Software Engineering at Auburn University . He earned his Ph.D. in Computer Science from the University of Southern California in 2007, following M.S. degrees in Computer Science (2003) and Electrical Engineering (2006), and a B.S. in Information and Computer Education from National Taiwan Normal University (1999). As a program director at the National Science Foundation (2019–2022), he shaped research directions in data science and cybersecurity. Dr. Ku leads the Data Science and Engineering Laboratory (DSE Lab) at Auburn University, focusing on data management systems , data analytics , cybersecurity , and mobile computing . His recent work includes privacy-preserving techniques for IoT networks, spatio-temporal modeling for traffic prediction, and federated learning frameworks for mobile social networks. He has secured multiple NSF grants for projects like Indoor Spatial Query Evaluation and Data Integrity in Clouds . Dr. Ku's 15 most recent articles (2024–2025) span domains including traffic prediction , air quality modeling , secure cloud computing , and IoT security , with subfields like graph neural networks, quadtree-based optimization, and homomorphic encryption. His research has been recognized with awards such as the Auburn Alumni Engineering Council Research Award (2018, 2012) and the AFRL Summer Research Associate (2016). He has advised 18 current and graduated students in Ph.D. and M.S. programs, including Wenlu Wang, Ting Shen, and Kazuya Sakai. His professional service includes chair roles at IEEE/ACM conferences (ICDE, CIC, ICLR) and editorial positions at journals like IEEE Transactions on Knowledge and Data Engineering .

• Cybersecurity
• Intrusion Detection
• Anomaly Detection
• +۵ مورد دیگر

Grzegorz Kołaczek is a faculty member at the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, affiliated with the Department of Computer Science and Systems Engineering . His research focuses on cybersecurity , intrusion detection , and security modeling with specific interests in Trust-based security frameworks IoT cybersecurity Anonymity protocols Fraud detection in digital systems . His recent publications emphasize the intersection of cybersecurity and emerging technologies , particularly in Internet of Things environments and e-commerce platforms. Key research trends include collaborative intrusion detection , adaptive user interfaces , and machine learning for threat analysis . Contact: grzegorz.kolaczek@pwr.edu.pl

• Computer Vision
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

Shajina Anandمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Cloud Computing
• +۵ مورد دیگر

Shajina Anand is an Assistant Professor in the Department of Mathematics and Computer Science at Seton Hall University . She established the university's advanced Cybersecurity Lab, focusing on research areas such as cyberbullying prevention in academic institutions, green initiatives in agriculture, microgrid security, and sentiment analysis of women in tech. Her work bridges academia and industry through collaborations with entities like the US Secret Service and PSEG, and international partnerships in India. Her professional interests include leading cybersecurity research, mentoring students on real-world projects, and curricula development aligned with industry needs. She actively organizes events like the NJ Cybersecurity Conference and Microsoft Hackathon. She is a recipient of the OMIC Grant and contributes to interdisciplinary efforts via the Academy of Applied Analytics and Technology (A3T). Research trends in her publications span cybersecurity for autonomous systems, 5G networks, microgrids, and social engineering. Her work emphasizes AI-driven solutions for critical infrastructure protection and ethical hacking practices. Awards: OMIC Grant Industry & Academic Collaboration: US Secret Service PSEG, Lenovo, Capgemini International Internship Programs in India Labs & Teams: Directs Seton Hall's Cybersecurity Lab, fostering international student participation in cutting-edge research.

Maher Salemمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Blockchain Technology
• Machine Learning
• +۴ مورد دیگر

Dr. Maher Salem is a Senior Lecturer (Assistant Professor) in Cybersecurity at King's College London, affiliated with the Department of Informatics within the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in Security Engineering from Kassel University (2014) and is a HEA Fellow. His research focuses on intrusion detection systems, cyber threat intelligence, machine learning-based security solutions, and blockchain applications in cybersecurity and finance. He has 15+ years of industrial experience with firms like AUDI AG and E-Plus, and has secured funding from the German Federal Ministry of Education and Research (BMBF). Dr. Salem leads the Computing Education Research Centre (CERC) at King's, advancing cybersecurity education through pedagogical tools and PhD supervision. His work bridges academia and industry, addressing challenges in online learning equity, cloud security, and automotive/IoT security via blockchain. Notable contributions include frameworks for threat intelligence detection, privacy-preserving cloud search, and decentralized transportation authentication systems. Education: PhD in Security Engineering (Kassel University, 2014) Affiliations: Former roles at Higher Colleges of Technology (UAE) and Fulda University of Applied Sciences (Germany) Labs/Teams: Security Hub (King's College), CERC His awards include the Emirates Skills First Rank Award (2017) and HEA Fellowship (2019). Research outputs span 25+ peer-reviewed articles, focusing on AI-driven penetration testing, blockchain applications, and cybersecurity education innovations. He collaborates globally on projects like dynamic cloud congestion management and APT detection frameworks.

• Cybersecurity
• Cloud Computing
• Information Security
• +۶ مورد دیگر

Mehmet Tahir Sandıkkaya is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He holds a Ph.D. in Computer Sciences from ITU and has been actively contributing to academia since 2002, progressing from Research Assistant to his current faculty position. He also served as a Doctoral Teaching Staff member at Institut National Polytechnique de Grenoble (Drakkar Lab) from 2018 to 2019. His educational background includes: B.Sc. in Electrical Engineering, Istanbul Technical University (1998–2002) M.Sc. in Computer Sciences (Thesis), Istanbul Technical University (2002–2006) Ph.D. in Computer Sciences, Istanbul Technical University (2006–2016) Dr. Sandıkkaya's research is centered on cybersecurity , cloud computing , information security , and cryptography . His work extends into Internet of Things (IoT) security , malicious behavior detection , and privacy-preserving systems . He employs machine learning techniques for intrusion detection, particularly in web sessions and avionic platforms. His research also addresses security challenges in e-commerce, SMEs, and national critical infrastructures. The analysis of his recent publications reveals a strong focus on practical security solutions, including lightweight authentication for IoT, DDoS detection using network features, and secure cloud architectures. His work increasingly integrates AI and cyber-physical systems for environmental monitoring and sustainable resource management. His scientific recognition includes: IEEE Member since 2001 Principal Investigator on multiple TTO and TÜBİTAK-funded research projects h-index of 8 with over 215 citations (Scopus) Dr. Sandıkkaya actively supervises students and leads significant research initiatives. He has served as Program Chair at ITU in 2018 and is the Principal Investigator (PI) on nine research projects, including "National Critical Infrastructure Cyber Attack System (SİNERJİ SALDIRI)" and "AI-Based Sustainable Basin Management via IoT" . His grants span cybersecurity, cloud design, and embedded systems, reflecting a robust research portfolio with real-world applications. He is affiliated with the Drakkar Research Lab at Institut National Polytechnique de Grenoble and leads cybersecurity research teams at ITU focusing on cloud security, IoT authentication, and avionic system protection. His lab integrates machine learning, formal methods, and system-level security engineering.