جست‌وجوی استاد و پژوهشگر

Ettore Merloمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Software Engineering
• +۵ مورد دیگر

Ettore Merlo is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads research in cybersecurity, artificial intelligence, and software systems. He holds an M.Sc. from the University of Turin and a Ph.D. from McGill University. His affiliations include membership in the Institute for Data Valorization (IVADO), focusing on data science and AI innovation. His research integrates software engineering with AI, emphasizing: Software artifact analysis (static/dynamic/symbolic) AI-driven security solutions for clone detection, malware analysis, and access control Fairness and robustness in machine learning systems Evolutionary analysis of software vulnerabilities Recent publications (2022-2025) demonstrate a strong focus on ethical AI, including bias mitigation in neural networks, automated anomaly detection, and certification of safety-critical ML systems. His work frequently applies graph neural networks, unsupervised learning, and formal verification methods to industrial and cybersecurity challenges. Professor Merlo has supervised 25 graduate students (10 PhD, 15 Master's), with projects ranging from avionics software to phishing kit analysis. While no scientific awards are listed, his extensive publication record includes 136 works spanning journals, conferences, and technical reports. Collaborations include partnerships with industrial telecommunication firms and international academia. No dedicated lab is specified, but his research aligns with Polytechnique Montréal's 'New Frontiers in Information and Communications Technologies' center.

Hüseyin Üzenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Medical Image Processing
• +۴ مورد دیگر

Assoc. Prof. Dr. Hüseyin Üzen serves as a faculty member in the Department of Computer Engineering at Bingöl University's Vocational School of Information Technologies. His research bridges artificial intelligence with practical applications in healthcare diagnostics and industrial automation, contributing to Bingöl University's mission of regional development through technological innovation. Education: PhD in Computer Engineering, İnönü University (2022) Master's in Computer Engineering, İnönü University (2018) Bachelor's in Computer Engineering, Süleyman Demirel University (2015) His research program centers on deep learning innovation for real-world problems, particularly in medical image analysis (retinal diseases, dental diagnostics, cancer detection) and industrial computer vision (surface defect detection, traffic monitoring). By developing specialized architectures like Swin-MFINet and DentifyNet, he addresses critical gaps in accuracy and efficiency for clinical decision support systems. Analysis of his 15 most recent publications reveals a dominant focus on hybrid neural network designs (73%), with 60% targeting medical applications and 40% industrial use cases. Key technical trends include attention mechanism integration (87% of papers), transformer-convolutional hybrids (73%), and multi-scale feature processing (67%). Research Funding: TÜBİTAK 1001 Project: Deep Learning-Based Lung Lesion Analysis in CT Images (Principal Investigator, 2025-2027) TÜBİTAK 1001 Project: Wilson's Disease Diagnosis from Brain MRI (Researcher, 2025-2027) Higher Education Council Project: Dental Image Analysis via Deep Learning (Researcher, 2024-2026) TÜBİTAK 1001 Project: SAR-Based Ship Detection (Researcher, 2023-2025) His research group operates at the intersection of computer vision and domain-specific applications, with current projects generating novel datasets in dental radiography, OCT imaging, and industrial defect cataloging. Students participate in end-to-end research from algorithm development to clinical/industrial validation, preparing them for careers in AI-driven healthcare technology and smart manufacturing systems.

• Embedded Systems
• Internet of Things
• Machine Learning
• +۴ مورد دیگر

Dr. Mairo Leier is a Senior Research Fellow at Tallinn University of Technology's School of Information Technologies, Department of Computer Systems, where he leads the Internet of Things Development Centre. His career spans industry roles at Ericsson Estonia and Elisa Estonia, alongside academic positions since 2010. He holds a PhD in Computer Systems (2016) and MSc (2010) from Tallinn Tech. Research interests center on embedded systems , IoT , and edge AI , with applications in smart infrastructure, autonomous vehicles, and biomedical sensing. Key focus areas include: Machine learning optimization for resource-constrained devices Real-time sensor data processing Hardware-software co-design Industrial IoT solutions His publications emphasize embedded AI efficiency, with recent work exploring model compression, hardware-aware neural networks, and edge deployment for computer vision. Awards and recognitions include: Tallinn Entrepreneurship Award (2017, 2018) 1st Prize in TTÜ Applied Research Competition (2017) Industry collaboration awards with Bosch Sensortec He actively advises students, with 14+ bachelor supervisees in embedded systems/IoT projects. Grants include applied research funding for industrial sensor algorithms and smart wearables. He directs the IoT Development Centre, fostering industry-academia partnerships and embedded systems innovation.

• Artificial intelligence in cyber/information Security
• IoT/IIoT vulnerability analysis and security
• Zero-trust architecture for IIoT
• +۷ مورد دیگر

Dr. Sadaf Hina is a Lecturer in Cybersecurity at the School of Science, Engineering and Environment (SSEE), University of Salford, Manchester, UK. She serves as the Programme Leader for the BSc Computer Science with Cybersecurity and holds significant research leadership roles as REF 2029 Co-Lead for People, Culture and Environment. Dr. Hina is an active member of the IoT Research and Innovation Lab (IRIL) and contributes to the Informatics Research Centre at the university. Her research interests span the critical intersection of artificial intelligence and cybersecurity, with specific expertise in IoT/IIoT security, zero-trust architectures, threat modeling in cyber-physical systems, and AI-aided security optimization. Over the past five years, her publication record shows consistent output in high-impact journals, with a notable increase in 2023-2025 focusing on cutting-edge applications of deep learning and vision transformers for security challenges. Her work demonstrates strong connections between theoretical security frameworks and practical industrial applications, particularly in critical infrastructure sectors. Dr. Hina has received significant professional recognition including: Fellow of the Higher Education Academy (FHEA, 2024) Advance HE Aurora Leadership Development Program certification (2025) REF Co-Lead roles for both People, Culture and Environment (2025) and Contributions to Knowledge and Understanding (2023) Editorial position for Applied Sciences journal (2024) Membership in professional organizations including IEEE (since 2014) and British Computer Society As a dedicated educator, Dr. Hina teaches Information Security, Information Security Management, Network Penetration Testing, Security and Privacy in IoT, and Fundamentals of Cybersecurity. She actively supervises BSc final year projects, MSc dissertations, and three PhD candidates working on AI-aided cybersecurity solutions. Her leadership extends to serving as Faculty Advisor for Women in Cybersecurity (WiCyS) and participation in cross-institutional mentoring programs, demonstrating commitment to developing the next generation of cybersecurity professionals.

• Wireless Communications
• Signal Processing
• Artificial Intelligence
• +۵ مورد دیگر

Berker Peköz serves as Assistant Professor in the Department of Electrical Engineering and Computer Science at Embry-Riddle Aeronautical University's College of Engineering. His expertise spans wireless communications, artificial intelligence security, and hardware systems, with significant contributions to 5G/6G technologies and aerospace applications. His educational foundation includes: B.S. in Electrical & Electronics Engineering M.S. in Electrical Engineering Ph.D. in Electrical Engineering Research focuses on cutting-edge intersections of communications and AI, particularly transformer model security, antenna-based authentication, and error-correction for emerging hardware. His work bridges theoretical innovation with practical aerospace implementations, evident in patents for antenna array geometries and publications addressing real-world challenges like contested IoT environments and spectral efficiency. Recent publications (2019-2025) show accelerating output with increasing emphasis on AI security frameworks. Scientific recognition includes: National Academy of Inventors membership (2019) Tau Beta Pi honor society induction (2018) Funding is evidenced through patent activity and consistent publications, though specific grants aren't detailed. He mentors students through undergraduate courses (EE 307 Avionics I, EE 327 Electrical Engineering Fundamentals) with likely graduate research supervision in communications security. His interdisciplinary approach connects electrical engineering fundamentals with next-generation AI and aerospace systems, positioning students for careers in secure communications and hardware innovation.

Carl Zhangمشاهده پروفایل

استادیار

• Social Network Privacy
• Data Science
• Machine Learning
• +۴ مورد دیگر

Carl Zhang serves as Assistant Professor of Computer Information Systems and Paul Engler Professor of Business Innovation at West Texas A&M University's Department of Computer Information and Decision Management within the Paul and Virginia Engler College of Business. Joining in 2020, he teaches programming, social network analysis, and data visualization courses while leading research in privacy and machine learning applications. His academic credentials include: Ph.D. in Computer Science, George Washington University (2020) M.S. in Computer Science, George Washington University (2017) B.S. in Information Management, Shandong Normal University (2015) Dr. Zhang's research centers on social network privacy vulnerabilities, data science methodologies, and machine learning implementations. His work bridges theoretical security frameworks with business applications, particularly in deanonymization resistance, malware detection systems, and consumer trust modeling in digital marketplaces. Current investigations focus on federated learning optimization and psychological factors in AI-driven e-commerce. Publication trends reveal increasing emphasis on practical security solutions, with recent work (2024-2025) addressing real-world challenges in data heterogeneity and malware classification. His output spans high-impact journals including IEEE Transactions and Journal of Computer Information Systems, demonstrating consistent contributions to both theoretical foundations and business applications. Key recognitions include: Paul Engler Professor of Business Innovation (2024) College of Business Teaching Excellence Award (2023) Dr. Zhang actively mentors 9-18 undergraduate students per term through structured projects like the ABET accreditation system and community initiatives. His $2,950 WTAMU Foundation grant (2022) supports IT knowledge dissemination via guest lectures and learning platforms. He leads student engagement through BuffTeks and Buff Analytics communities, fostering hands-on technical development. Though without a formal lab, he cultivates research opportunities through course projects, competition mentorship (e.g., USITCC winners), and software development initiatives like the Program Assessment Reporting System deployed on WT's network infrastructure.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

Shangwen Wangمشاهده پروفایل

استادیار

• Program repair
• Program comprehension
• Mining software repositories
• +۳ مورد دیگر

Shangwen Wang is an Assistant Professor in the School of Computer Science at National University of Defense Technology (NUDT) in Changsha, China. He earned his Bachelor's degree in June 2017, Master's degree in December 2019, and Ph.D. in December 2023, all from NUDT. During his graduate studies, he was supervised by Professor Xiaoguang Mao. From May 2022 to July 2023, he was a visiting student at Southern University of Science and Technology under Professor Yepang Liu. His educational background includes: Ph.D. in Software Engineering, NUDT (2020.3-2023.12), supervised by Prof. Xiaoguang Mao Visiting Scholar, SUSTech (2022.5-2023.7), supervised by Prof. Yepang Liu M.A. in Software Engineering, NUDT (2017.9-2019.12), supervised by Prof. Xiaoguang Mao B.A. in Software Engineering, NUDT (2013.9-2017.6) Wang's research focuses on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for Software Engineering. His work bridges traditional software engineering techniques with modern AI approaches, particularly leveraging large language models for various software engineering tasks. He has made significant contributions to automated program repair, fault localization, vulnerability detection, and code generation. His research demonstrates a strong emphasis on empirical validation and practical applicability to real-world software development challenges. His recent publications show a clear trend toward integrating large language models with traditional software engineering tasks. The 15 most recent articles reveal a focus on applying LLMs to program repair, fault localization, vulnerability detection, and code generation, while maintaining strong empirical foundations. His work spans both theoretical advancements and practical tool development, with applications in software security, testing, and maintenance. His notable achievements include: CCF Outstanding Doctoral Dissertation (CCF优博) 2024 Outstanding Doctoral Graduates, NUDT, 2023 Multiple distinguished paper awards including ACM SIGSOFT Distinguished Paper Award (ISSTA'24) and IEEE TCSE Distinguished Paper Awards (ICSME'22, SANER'22) Prestigious scholarships from NUDT throughout his academic career As an active member of the software engineering community, Wang serves on numerous program committees for top conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He has also contributed to teaching as a teaching assistant for courses such as Compiler, Python Programming, Discrete Mathematics, and C++ Programming. His research group appears to be actively mentoring students, as evidenced by his role as corresponding author on multiple student-led publications. Wang maintains an active research presence with collaborations across multiple institutions in China. His work demonstrates a clear trajectory from traditional program analysis techniques toward integrating cutting-edge AI approaches, particularly large language models, into software engineering practices.

Kijung Shinمشاهده پروفایل

دانشیار

• Data Mining
• Graph Algorithms
• Network Science
• +۴ مورد دیگر

Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.