جست‌وجوی استاد و پژوهشگر

• Computer Science
• Software Engineering
• Machine Learning
• +۶ مورد دیگر

Dario Di Nucci is a Researcher at Joint Academic Data Science (JADS), focusing on Data Analytics and Data Governance . His work intersects Computer Science , Software Engineering , and Machine Learning , with key contributions to DevOps, Infrastructure-as-Code, and Smart Contracts. Recent Publications include a 2025 review on Data Mesh architecture in ACM Computing Surveys , 2024 studies on vulnerability fix mapping in open-source repositories (ACM Transactions) and automated smart contract testing (Science of Computer Programming), and 2022 empirical analyses of linguistic inconsistencies in infrastructure-as-code (Empirical Software Engineering) and defect prediction via process metrics (IEEE Transactions on Software Engineering). These works collectively advance methodologies in code quality, security, and natural language processing. Collaborations span institutions like Vrije Universiteit Brussel and Tilburg University , with media coverage in 2023 highlighting his research in machine learning and cloud computing. He has supervised at least one PhD student (Supervised Work section) and contributed datasets like Detecting Code Smells Using Machine Learning Techniques (2018, Figshare).

Riccardo Scandariatoمشاهده پروفایل

استاد

• Software Security
• Cybersecurity
• Usable Security
• +۶ مورد دیگر

Riccardo Scandariato is a full professor and head of the Institute of Software Security at Hamburg University of Technology (TUHH), Germany. He coordinates the International Master in Information and Communication Systems and has held academic positions at institutions like the University of Virginia, Politecnico di Torino, KU Leuven, and the University of Gothenburg before joining TUHH in 2020. Education: PhD in Computer Science from Politecnico di Torino (2004). Career highlights include leading interdisciplinary research in software security, vulnerability repair, and AI-driven cybersecurity solutions. Research focuses on secure software design, automated vulnerability repair, and empirical methods for cybersecurity. Key interests include microservices, IoT, and cyber-physical systems, with emphasis on developer-centric security practices and usable privacy features. Notable projects include SEC4AI4Sec (EU Horizon Europe) and KiThreat (CREATUM GmbH), addressing AI-augmented systems and GenAI-based software risks. Former projects include CASUS (automotive security assurance cases) and CyReV (automotive cyber resilience). Research team includes senior researchers and PhD students working on topics like secure code generation (Catherine Tony), vulnerability localization (Cuong Bui Quang), and ML-driven threat analysis (Torge Hinrichs). Alumni have contributed to areas like automotive systems, privacy in smart homes, and architectural threat modeling. Grants: EU-funded AssureMOSS (2020–2023), H2020, and VINNOVA-backed CASUS (2017–2021). Committees: PC member of major conferences like ECSA, ARES, and SANER. Steering committee for ARES. Labs/Teams: Leads the Institute of Software Security at TUHH, focusing on interdisciplinary tools for secure application design. Collaborates with industry partners like Volvo and CREATUM GmbH.

JOSE MANUEL COTOS YAÑEZمشاهده پروفایل

استاد

• Remote Sensing
• Oceanography
• Machine Learning
• +۵ مورد دیگر

Prof. Jose Manuel Cotos Yáñez is a University Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Faculty of Physics. He leads the COGRADE (Computer Graphics and Data Engineering) research group and contributed to the LABSIS (Systems Laboratory). His PhD (1994) focused on oceanic structures and fisheries using remote sensing and knowledge engineering, supervised by Dr. Carlos Hernández Sande and Dr. Juan Enrique Arias Rodríguez. His research spans remote sensing, machine learning, geospatial technologies, and educational applications. Key projects include TRAFAIR (air quality data generation), SODA framework (spatial data analysis), and augmented reality tools for cultural heritage. His work integrates environmental monitoring, GIS systems, and interdisciplinary tech solutions. Research interests include oceanography, data engineering, and applying machine learning to environmental challenges. He has collaborated on projects like Retelab (geospatial grid portals) and frameworks for sensor data integration. His publications highlight innovations in distributed databases, educational gaming, and oil spill detection via satellite imagery. Key Projects: TRAFAIR, SODA, INOVA Framework, Retelab Labs/Teams: COGRADE, LABSIS Future Focus: Expanding AI applications in environmental and educational tech, sustainable data systems.

JOSE MANUEL COTOS YAÑEZمشاهده پروفایل

استاد

• Computer Graphics
• Data Engineering
• Remote Sensing
• +۷ مورد دیگر

Jose Manuel Cotos Yañez is a Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing within the Faculty of Physics. He holds a PhD from the same institution, completed in 1994 with a thesis focusing on oceanic structure dynamics for fisheries applications using remote sensing and knowledge engineering. His research is centered in the COGRADE (Computer Graphics and Data Engineering) group, with past involvement in the LABSIS (Systems Laboratory) . Research interests include computer graphics, data engineering, spatial data integration, remote sensing applications in oceanography and environmental monitoring, and the development of geospatial technologies. Notable work involves oil spill detection via SAR imagery, geospatial data frameworks (e.g., GeoDADIS, SODA), and educational technology tools like gesture-based math learning and augmented reality for archaeological education. Publications span topics from machine learning-driven environmental monitoring to GIS systems and distributed database optimization. He has contributed to projects such as TRAFAIR (air quality data systems) and INOVA (web technologies for consulting frameworks). His work often integrates AI techniques with geospatial and oceanographic data analysis.

Victor Kebandeمشاهده پروفایل

استادیار

• Cybersecurity
• Digital Forensics
• IoT/IIoT Security
• +۵ مورد دیگر

Dr. Victor Kebande is an Assistant Professor of IT Security in the Secure Distributed Systems (SDS) group at Blekinge Institute of Technology's Department of Computer Science (DIDA). He holds a PhD in Cyber and Information Security from 2018 and has held postdoctoral and visiting researcher roles at institutions like Luleå University of Technology and Colorado State University. His research focuses on cybersecurity, digital forensics, IoT/IIoT security, blockchain, and critical infrastructure protection. He is a member of IEEE, ACM, and IITPSA, and serves on the editorial board of Forensic Science International: Reports . Education: PhD in Computer Science (Cyber and Information Security & Digital Forensics) - 2018 Research Interests: Cybersecurity, digital forensics, IoT/IIoT security, cloud security, blockchain, threat modeling, privacy-preserving techniques, and AI integration with immersive realities. Projects: dAIEDGE: Horizon Europe-funded Network for trustworthy AI at the Edge DISS: Threat detection in IoT systems Symphony: Robust distributed service exposure Awards: None explicitly mentioned, but he holds editorial and reviewer roles in top journals. Grants & Advising: Supervised numerous theses on topics like IoT security, blockchain forensics, and cloud forensics. Active in funding projects like HINTS and Eclipse ArrowHead. Labs/Teams: Part of SDS group, ATLAS Institute (CU Boulder), and collaborations with industry partners like Axis Communications.

Hyunsook Doمشاهده پروفایل

دانشیار

• Software Engineering
• Software Quality
• Machine Learning Applications
• +۳ مورد دیگر

Hyunsook Do is an Associate Professor in Computer Science and Engineering at the University of North Texas, specializing in software quality, testing methodologies, and AI applications in software engineering. Her research develops innovative approaches for quality assurance in modern software systems. Research spans: Automated testing and debugging techniques AI/ML applications in software engineering Safety analysis for autonomous systems Quality metrics and bug detection Empirical software engineering studies Recent publications demonstrate strong focus on leveraging natural language processing, machine learning, and semantic analysis to improve software quality detection, bug triaging, and safety verification across diverse application domains including mobile apps and autonomous vehicles.

Wajdi Aljedaaniمشاهده پروفایل

استادیار بالینی

• Accessibility
• Software Engineering
• Human-Computer Interaction
• +۲ مورد دیگر

Wajdi Aljedaani serves as a Clinical Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT), with his office located in Discovery Park, room E235D. His research and academic work center on advancing accessibility solutions and software engineering practices through interdisciplinary approaches. His primary research domains include: Accessibility for users with visual, auditory, and neurological disabilities Integration of machine learning in accessibility compliance and software development Analysis of educational barriers in online learning for disabled students Software engineering challenges in refactoring, testing, and accessibility implementation Recent publications (2024-2025) demonstrate a concentrated focus on identifying accessibility barriers in social media, e-government platforms, and mobile applications through systematic literature reviews and empirical studies. His work uniquely bridges clinical perspectives with technical solutions, particularly examining seizure triggers in apps and accessibility sprints in software engineering education. A significant portion of his research leverages large language models to enhance accessibility features and address developer challenges with WCAG guidelines. His advising and research initiatives emphasize practical applications in educational settings, particularly through student software engineering projects that incorporate accessibility requirements. While specific grant information isn't detailed in available sources, his publication pattern indicates active engagement with real-world accessibility testing and implementation challenges across multiple platforms.

• Software Engineering
• Software Testing
• Software Design
• +۳ مورد دیگر

Benoit Baudry is a Full Professor of Computer Science at the University of Montreal's Faculty of Arts and Sciences, Department of Computer Science and Operations Research. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l’expérimentation dans les logiciels), focusing on software engineering challenges. His teaching responsibilities include courses like Software Quality Metrics (IFT-3913), Software Project Management (IFT-3150), and Advanced Topics in Software Engineering (IFT-6251). He is actively involved in strategic initiatives like IVADO and MITACS-funded projects. Research interests span software supply chain security, dependency management, automated software engineering, and AI-driven development tools. He leads projects such as 'Safe and Transparent Software Reuse' (2025–2031) funded by NSERC and 'CLIMB: LLM-driven MLOps Interface' (2025) supported by MITACS. His work addresses critical issues like detecting bloated dependencies, ensuring reproducible builds, and mitigating supply chain vulnerabilities. Recent funding includes NSERC Discovery Grants and Apogée Canada operational funds. He has pioneered techniques for generating mocks from production data, automating test generation via runtime analysis, and applying large language models to software repair. His interdisciplinary projects bridge software engineering with digital art, such as algorithmic art generation systems. He advises multiple graduate programs including Computer Science Bachelors, Masters, and PhD tracks. Key collaborations include KTH Royal Institute of Technology and industry partnerships like Co-operators. His research emphasizes practical solutions for real-world software systems' reliability and security.

• Healthcare Applications
• ECG Signal Processing
• Sleep Apnea Detection
• +۵ مورد دیگر

Nuno Gonçalo Coelho Costa Pombo is an active researcher and faculty member at the University of Beira Interior, Portugal, where he works in the School of Technology and Management within the Department of Computer Science. His academic career spans over a decade with consistent publication output from 2014 through 2025, demonstrating sustained research productivity and evolving expertise across multiple domains. Dr. Pombo's research interests center on the intersection of computer science and healthcare applications. His work prominently features ECG signal processing for medical diagnostics, with significant contributions to sleep apnea detection systems and clinical decision support systems . He has pioneered approaches in activities of daily living recognition using sensor data from mobile devices, which has important applications in elderly care and chronic disease management. His research methodology often combines machine learning techniques with Internet of Things (IoT) frameworks to create healthcare monitoring solutions that balance effectiveness with user privacy concerns. Recent work has expanded into educational technology applications, including augmented reality for programming instruction and futuristic thinking development in education. An analysis of Dr. Pombo's publication trends reveals a natural progression from foundational research in activity recognition to increasingly sophisticated healthcare applications. His early work focused on basic activity recognition frameworks, while more recent publications demonstrate advanced applications in specific medical conditions including diabetes management, mental health support, and cardiovascular diagnostics. The interdisciplinary nature of his research is evident in his extensive collaborations across engineering, medical, and social science domains, reflecting a holistic approach to healthcare technology development. Dr. Pombo maintains an active supervision role, with numerous publications featuring co-authors who appear to be students or early-career researchers. His work has been supported by research initiatives focused on healthcare technology innovation, though specific grant details are not evident from publication records. The consistent output across reputable venues including IEEE Access, Sensors, and international conferences demonstrates sustained research impact in his field.

Michael Godfreyمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution
• Empirical Studies
• +۵ مورد دیگر

Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.

Shane McIntoshمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Software Delivery
• Release Engineering
• +۲ مورد دیگر

Shane McIntosh is an Associate Professor and Associate Director of Graduate Studies at the Cheriton School of Computer Science, University of Waterloo. He holds a Ph.D. from Queen's University (2015) and specializes in empirical software engineering, software delivery processes, and software quality improvement through data mining techniques. His research focuses on analyzing historical and operational data from large-scale software systems to derive actionable insights. Key areas include release engineering, DevOps optimization, and software quality enhancement. McIntosh employs empirical methods to study build systems, continuous integration practices, and defect prediction. McIntosh's recent publications demonstrate a strong focus on improving software development workflows through empirical analysis of build systems, CI/CD pipelines, and code quality metrics. His work frequently addresses efficiency optimization in development operations and vulnerability detection in machine learning systems.

Tristram Wyattمشاهده پروفایل

عضو هیئت علمی

• Pheromones
• Animal Behavior
• Chemical Communication
• +۳ مورد دیگر

Dr. Tristram Wyatt is a Senior Research Fellow Emeritus at Kellogg College and former University Lecturer (equivalent to Associate Professor) in Biological Sciences at the University of Oxford's Department for Continuing Education. His research focuses on pheromones and animal behavior, particularly their evolutionary roles and chemical communication mechanisms. He authored award-winning books like Pheromones and Animal Behavior (2014) and Animal Behaviour: A Very Short Introduction (2017), which bridge chemistry and biology across vertebrates and invertebrates. His TEDx talk on human pheromones has garnered 1.4 million views globally. Wyatt's work emphasizes interdisciplinary approaches, addressing topics from lemur communication to the impact of ocean acidification on chemical signals. He has held leadership roles in online education, including Director of Distance and Online Learning at Oxford (2000–2005). His research has been recognized with the Royal Society of Biology's Best Postgraduate Textbook Award (2014). Key contributions include critiques of human pheromone myths, advocacy for reproducible research practices, and studies on semiochemical communication in diverse species. His writing extends to public engagement, including a Guardian blog debunking corporate pheromone marketing claims.

Yutian Tangمشاهده پروفایل

استادیار

• AI+SE (Large-language Model+SE)
• Program Analysis
• Empirical Software Engineering
• +۴ مورد دیگر

Dr. Yutian Tang is an Assistant Professor at the University of Glasgow's School of Computing Science. He holds a Ph.D. from The Hong Kong Polytechnic University and a B.Sc. from Jilin University. His research focuses on AI-driven software engineering, program analysis, and mobile app security, with over 40 publications in top venues like ESEC/FSE and IEEE TSE. He has received awards including the ISSRE'18 Best Industry Paper and Shanghai Pujiang Talent Program. Current research emphasizes automated testing, bug localization, and LLM applications in software engineering. Education: Ph.D., Department of Computing, The Hong Kong Polytechnic University B.Sc., Computer Science, Jilin University Research Interests: His work spans AI+SE (Large Language Models in Software Engineering), automated testing, privacy protection, and malware detection. Notable contributions include XDebloat (app debloating) and tools for analyzing Android vulnerabilities. He actively explores LLM applications for code generation and test suite optimization. Grants & Funding: EPSRC Peer Review College member National Natural Science Foundation of China (NSFC) grant (2023-2025) OpenAI API Researcher Access Program (2024) Google Cloud Research Credits (2024) Service & Leadership: Editorial Board Member of Frontiers in Computer Science , PC member of ICSE, ESEC/FSE, ASE, and other top conferences. Active in reviewing for IEEE Transactions and ACM journals. Future Directions: Tool support for automated bug detection, LLM integration in software lifecycle, and large-scale binary code analysis.

Tevfik Kosarمشاهده پروفایل

استاد

• Data Intensive Computing
• Distributed Systems
• Petascale Systems
• +۴ مورد دیگر

Prof. Tevfik Kosar is a Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), part of the School of Engineering and Applied Sciences. His research focuses on data-intensive computing, distributed systems, petascale storage, and energy-efficient data transfer optimization. He has taught numerous courses on operating systems, distributed systems, and green computing, including CSE 421/521 (Operating Systems), CSE 709 (Green Computing), and CSE 710 (Distributed File Systems). His achievements include the UB Exceptional Scholar Award (2020), SEAS Researcher of the Year (2018), and an NSF CAREER Award (2011). He leads the DIDCLAB , which develops innovative solutions for data-intensive distributed computing. Prof. Kosar holds a PhD from the University of Wisconsin-Madison (2005), MS from Rensselaer Polytechnic Institute (1999), and BS from Bogazici University (1997). His work spans academic contributions, industry collaboration, and sustainability-focused research. Teaching Highlights: Courses: Operating Systems (repeated annually since 2011), Green Computing (since 2013), and specialized seminars on distributed file systems. Grants & Projects: Collaborative NSF grants for GreenSW (2024), CloudScent (2023), and energy-aware data transfer optimization (2018–present). His research emphasizes minimizing energy consumption in cloud and HPC systems while maximizing data transfer efficiency. Recent projects include GreenABR+ for energy-aware video streaming and Greendataflow for zero-carbon data movement. He has also pioneered tools like FlowTracer for AI training cluster analysis and PhoneLab , a smartphone testbed for real-world network studies.

Ioannis Stefanakosمشاهده پروفایل

عضو هیئت علمی

• Formal Verification
• Robotics Safety
• Autonomous Systems
• +۳ مورد دیگر

Ioannis Stefanakos is a Research Associate at the Department of Computer Science, University of York, affiliated with the High Integrity Systems research group. His work focuses on formal verification of autonomous systems, safety-critical robotics, and software performance analysis. Current projects include assurance frameworks for drones, adaptive reinforcement learning in healthcare robotics, and probabilistic modeling of software performance. Recent research emphasizes interdisciplinary applications in UAV systems, medical decision support systems, and collaborative manufacturing robots. No academic awards are listed, though his work has been published in top-tier conferences. Advising and grants details are not explicitly stated, but his involvement in doctoral forum papers suggests potential supervision roles in software systems research.