جست‌وجوی استاد و پژوهشگر

Martin Robillardمشاهده پروفایل

استاد

• Software Engineering
• Information Privacy
• Human-Computer Interaction
• +۴ مورد دیگر

Martin Robillard is a Professor at McGill University's School of Computer Science, where he leads the McGill Software Technology Lab. He holds a Ph.D. from the University of British Columbia. His research focuses on software engineering, emphasizing human aspects of development, including documentation design, test suite quality, and information privacy. He has authored the textbook Introduction to Software Design with Java and developed the JetUML tool, recognized with the 2024 ACM SIGSOFT Influential Educator Award. He has co-chaired major conferences (FSE 2012, ICSE 2017) and is a Distinguished Member of the ACM. Research Interests: His work bridges software engineering, AI, and HCI, addressing challenges in API usability, automated documentation generation, and privacy-preserving software design. Recent projects include dynamic documentation tools like Casdoc and studies on developer documentation formats. Professional Activities: He serves on editorial boards for IEEE TSE and Empirical Software Engineering, and chairs workshops like DySDoc. His contributions include over 100 publications, six SIGSOFT Distinguished Paper Awards, and leadership in software engineering education. Teaching: Teaches courses on software design (COMP 303), information privacy (COMP 555), and software architecture. His courses emphasize practical skills and ethical considerations in software development. Labs & Collaborations: The McGill Software Technology Lab investigates knowledge discovery in software systems, with projects on API documentation, test quality, and privacy in health contexts. Collaborators include industry partners like ActiveEon and General Motors.

• Computer Science
• Software Engineering
• Algorithms
• +۳ مورد دیگر

Gonzague Yernaux is a Research Professor at the University of Namur's Faculty of Computer Science, affiliated with the Namur Digital Institute. He holds a Doctorate and a Master's degree in Computer Science from the same institution. His research focuses on programming paradigms, logic programming, algorithm optimization, and educational technology. Yernaux has contributed to projects like Algorithmic equivalence and developed tools such as Manim-DFA and Scrimmo. He has presented at international conferences and received awards including the Jean Fichefet Prize (2017) and the Student Encouragement Award (2022). Education : Doctorate, University of Namur (Year unspecified) Master of Computing (Computer Science), Faculty of Computer Science, University of Namur (2015–2017) Research Interests : His work spans logic programming, software engineering, and algorithm design. Key areas include predicate anti-unification, bipartite matching optimization, and semantic clone detection. He also explores educational applications, such as using escape games to teach computer science concepts. His research often combines theoretical frameworks with practical software tools. Grants & Activities : He leads the project Algorithmic equivalence by generalization-driven transformations of logic programs . Active in academic events, he has participated in the International Conference on Advances in Computing Research (2025), the Belgium-Netherlands Software Evolution Workshop (2024), and organized presentations on topics like predicate anti-unification and bipartite matching. Labs & Teams : Associated with the Namur Digital Institute, his work integrates interdisciplinary collaborations in computer science and software development.

Simon Connمشاهده پروفایل

استاد

• Molecular Biology
• Cancer Biology
• Circular RNA Research
• +۴ مورد دیگر

Professor Simon Conn is a Full Member of the College of Medicine and Public Health and the Flinders Health and Medical Research Institute at Flinders University. With expertise in molecular and cellular biology, he leads research on circular RNAs (circRNAs) and their implications in cellular development and cancer, with significant contributions published in top journals including Cell, Nature Biotechnology, and Nature Reviews Cancer. Dr. Conn completed his Bachelor of Biotechnology (Hons) at Flinders University in 2006 and pursued postdoctoral research at the European Molecular Biology Laboratory in France and the Centre for Cancer Biology in Australia. His academic journey demonstrates a transition from plant molecular biology to human cancer research, with a focus on RNA mechanisms. Professor Conn's research primarily investigates circular RNAs and their roles in gene regulation, with particular emphasis on cancer biology (brain cancer, prostate cancer), stem cell biology, and transcriptomics. His work has revealed how circRNAs impact cellular development pathways and contribute to oncogenesis, establishing him as a leader in this emerging field of RNA biology. His recent publications demonstrate a clear progression from fundamental circRNA biology to translational applications, with significant contributions to bioinformatics tools for circRNA analysis, understanding circRNA roles in cancer mechanisms, and developing circRNA-based diagnostic and therapeutic approaches. The research shows strong interdisciplinary integration between molecular biology, bioinformatics, and cancer therapeutics. Australian Research Council Future Fellowship NHMRC-project funding Over $2M in peer-reviewed research grants in the past 3 years h-index of 21 with over 80 citations per article Professor Conn has successfully supervised 12 graduate students across principal and associate supervisions, including Laura Gantley who received the University Medal in April 2023. His laboratory has secured substantial research funding and produced high-impact publications that have significantly advanced the understanding of RNA biology in disease contexts. Based at the Flinders Health and Medical Research Institute, Professor Conn leads a research team within the Alternative Splicing in Human Pathologies Laboratory, investigating RNA biology with direct implications for cancer diagnostics and therapeutics. His work contributes to UN Sustainable Development Goals related to good health and well-being through advancing precision medicine approaches.

Nata Stulovaمشاهده پروفایل

پژوهشگر

• Software Engineering
• Formal Methods
• Natural Language Processing
• +۳ مورد دیگر

Nata Stulova is a Researcher currently working as a staff research scientist at MacPaw's Technological R&D Center, focusing on software engineering research involving formal and informal program specifications. Formerly, she held academic roles including senior postdoctoral researcher at the University of Bern (SCG), researcher at EPFL's LARA Lab, and PhD work at the Technical University of Madrid (UPM). Her research spans software documentation consistency, NLP applications, runtime verification, and low-code tools. She has led multiple projects addressing code comment quality, empirical studies on documentation practices, and tool development for requirements engineering. Despite transitioning to industry in 2022, she maintains active collaboration with academia, publishing in top conferences like ICSE and IEEE Transactions. She holds a PhD in Computer Science from UPM and has authored numerous peer-reviewed articles and conference papers. Her work emphasizes bridging academic research with industry applications, particularly in software analysis and developer productivity. Education: PhD in Computer Science, Technical University of Madrid (2018) MSc in Artificial Intelligence, Technical University of Madrid (2013) BSc in Applied System Analysis, NTUU "KPI" (2012) Research Interests: Her work focuses on improving software quality through automated documentation tools, runtime verification techniques, and low-code solutions for requirements engineering. She explores NLP methods for detecting inconsistent or outdated comments and optimizing static analysis efficiency. Recent projects include SwiftEval (LLM-generated code evaluation) and RepliComment (cloned comment detection). She also emphasizes practical applications of formal methods in dynamic languages like Prolog and Python. Grants & Funding: ASA: Agile Software Assistance TRACES: Resource-Aware Software Tools N-GREENS: Energy-Efficient Software Labs/Teams: Core member of the Ciao system development team at IMDEA Software Institute, contributor to the Software Composition Group (SCG) at UniBe, and collaborator with MacPaw's TR&D team on industry-focused R&D.

Ying Zouمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Empirical Studies
• +۷ مورد دیگر

Dr. Ying Zou is a Professor and Canada Research Chair (Tier II) in Software Evolution at Queen’s University’s Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computing. She is a Visiting Faculty Fellow at IBM Centers for Advanced Studies (CAS) and a member of IEEE. Her research focuses on software engineering, maintenance, empirical studies, and service-oriented computing, with notable contributions to refactoring patterns, performance analysis, and developer tooling. Education: BEng from Beijing Polytechnic University (China), MEng from Chinese Academy of Space Technology (China), and PhD from the University of Waterloo (Canada). Research interests include software evolution dynamics, code clone analysis, and optimizing developer workflows. Notable achievements include the 2014 IBM CAS Research Faculty Fellow of the Year and two IBM Faculty Awards (2007, 2008). Her work bridges empirical software engineering with practical industrial applications. Awards: Canada Research Chair, IBM Faculty Awards, IBM CAS Fellow Grants: IBM CAS collaborations, cross-disciplinary research funding Labs/Teams: Active contributor to the Software Evolution and Analysis Lab (SEAL), focusing on empirical studies and tool development for software maintenance challenges.

• Software Engineering
• Machine Learning
• Precision Agriculture
• +۷ مورد دیگر

Önder Babur is an Assistant Professor in the Department of Information Technology at Eindhoven University of Technology. His research focuses on software engineering, machine learning applications, precision agriculture, and digital twin technologies. He has contributed to projects involving business process modeling, drone analytics, and energy market methodologies. Babur collaborates with institutions like Wageningen University & Research and has supervised PhD candidates in generative AI approaches and digital twin systems. His research interests span model analytics, clone detection, API usage analysis, and low-code platforms. Notable projects include an empirical study of business process models on GitHub and foundational work in digital twins for energy markets. Babur has co-developed datasets for drone imagery analysis and systematic reviews of food recommender systems. His work bridges theoretical software engineering advancements with practical applications in agriculture and energy systems. Advising PhD candidates include Gürkan Soykan (digital twins in energy markets) and Jeroen Doornbos (generative AI in drone analytics). Projects emphasize collaborative frameworks like SAMOS for model management and Apache Spark-based distributed analytics. Babur’s contributions span 45+ peer-reviewed publications and two active PhD supervisions.

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.

• Computer Security
• Machine Learning
• Binary Analysis
• +۳ مورد دیگر

Michael Pucher is an External Lecturer in the Department of Logic and Computation at Technische Universität Wien. His research focuses on applying machine learning techniques to identify obfuscated function clones in binary code, with notable work published in 2021. He teaches courses such as 'Attacks and Defenses in Computer Security' (Course ID: 192.111). Education details are not explicitly provided, but his diploma thesis indicates academic engagement in computer security. No scientific awards or grants are mentioned in the text. He is affiliated with the university's informatics department and maintains a TISS profile (ID: 281901).

• Service-Oriented Computing
• Edge Intelligence
• Blockchain
• +۶ مورد دیگر

Dr. Hai Dong is a Senior Lecturer at the School of Computing Technologies, RMIT University, Melbourne, Australia. He leads the Smart Sensing and Services Research Area and directs the GreenCryptoLab, a joint laboratory with CloudTech. He chairs the IEEE Task Force on Deep Edge Intelligence and has held roles including Research Fellow at Curtin University and RMIT. Education: PhD (Curtin University), BEng (Northeastern University, China), Graduate Certificate in Learning & Teaching (Distinction) His research focuses on Edge Intelligence, Blockchain, AI Security, and Cyber Security. He has published 150+ articles in top venues like TIFS, ICML, and ICSOC, securing over $5M in research funding from ARC, CRC, and industry partners. Recent work emphasizes Green Cryptocurrency systems and federated learning applications. His awards include the 2023 RMIT Industry Engagement Award and Best Paper recognitions in ICSOC and IEEE ICBC. Supervision: Active in guiding PhD/Master students in AI Security, Edge Computing, and Blockchain. Grants: Major projects include Green Bitcoin platforms and secure crypto payment systems. Labs: GreenCryptoLab collaborates on sustainable blockchain tech and secure edge systems.

• Machine Learning
• Software Testing
• Safety Critical Systems
• +۷ مورد دیگر

Karl Meinke is a Professor at KTH Royal Institute of Technology, where he serves as Head of the Computer Science Department and Head of the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning techniques to software testing, particularly for safety-critical systems like autonomous vehicles and embedded systems. His research interests span machine learning, software testing, safety critical systems, embedded systems, autonomous driving, digital pathology, and graph neural networks. Meinke has developed innovative approaches like Learning-Based Testing that combine machine learning with formal methods for system validation. His work bridges theoretical computer science with practical applications in automotive systems and medical diagnostics. His recent publications show a strong trend toward applying graph neural networks to diverse domains including program analysis, digital pathology, and autonomous vehicle testing. His research demonstrates a consistent focus on solving the test oracle problem and generating meaningful test cases for complex systems where traditional testing approaches fall short. Meinke actively collaborates with Karolinska Institutet (KI), indicating interdisciplinary work between computer science and medical research. He is responsible for Masters level education in software testing at KTH and serves as examiner for several advanced courses including Degree Projects in Computer Science and Software Reliability. His research group has developed tools like LBTest for learning-based testing of reactive systems, and he has secured funding for projects such as the ITEA3 Testomat Project focused on next-level test automation. His work has significant implications for validating autonomous systems where safety is paramount. Meinke leads research in using machine learning to address fundamental challenges in software testing, particularly for systems where traditional test oracles are unavailable or impractical. His approach of combining active learning with formal specifications has created new pathways for validating complex cyber-physical systems.

Jim Buckleyمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution
• Artificial Intelligence for Software Engineering
• +۵ مورد دیگر

Jim Buckley is a Professor in the Computer Science and Information Systems Department at the University of Limerick, Ireland, and a Principal Investigator in Lero. He leads the ARC research group focused on software evolution and legacy system modernization, with significant industry collaborations including Huawei, IBM, and Fidelity. Education: BSc in Biochemistry, University of Galway (1989) MSc in Computer Science, University of Limerick (1994) PhD in Computer Science, University of Limerick (2002) Research Focus: His work centers on AI-enhanced software engineering (AI4SE/SE4AI), featuring breakthroughs in clone detection, software architecture evaluation, and feature location. He has developed industry-adopted tools for software comprehension and evolution, with recent emphasis on explainable AI (XAI) and scalable neural network applications for industrial codebases. His research consistently bridges academic rigor with industrial implementation. Publication Trends: Recent articles (2022-2025) reveal a dominant shift toward AI-driven software engineering solutions, particularly in clone detection and architecture recovery. Key themes include industrial scalability, developer experience optimization, and responsible AI integration, with strong representation in top-tier venues like IEEE Transactions and ACM Computing Surveys. Scientific Awards: No awards specified in source material. Grants & Industry Impact: Leads the Huawei-funded TREES Programme and maintains active partnerships with 9+ companies. His research has yielded licensed tools (e.g., for legacy system evolution), two IP assignments from LLM-based clone detection work, and practical frameworks adopted by seven Irish enterprises. Research Infrastructure: Directs the ARC group within Lero, which combines academic researchers and industry practitioners to address real-world software maintenance challenges through empirical studies and tool prototyping.

Dejan Gjorgjevikjمشاهده پروفایل

استاد

• Pattern Recognition
• Machine Learning
• Software Engineering

Dejan Gjorgjevikj is a Full Professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje. He joined the Department of Computer Science and Engineering in 1992, progressing from Assistant Professor (2004) to Associate Professor (2009) before attaining full professorship in 2014. His international academic engagements include research visits to institutions in Austria, Bulgaria, the Czech Republic, and the UK. Education: Bachelor's and Master's degrees from the Faculty of Electrical Engineering, Skopje (1992, 1997); PhD from the same institution (2004). Research Focus: His primary research explores pattern recognition, machine learning, and software engineering. Recent work emphasizes applications in industrial diagnostics (fault detection in machinery), blockchain security (Ponzi scheme detection), environmental monitoring (air pollution prediction), and human-computer interaction (sensor-based activity recognition). Methodologies frequently involve deep learning architectures like autoencoders, LSTMs, and adversarial networks. Publication Trends: Gjorgjevikj has authored over 90 publications, with recent works demonstrating increased focus on neural network applications in cross-domain problems (mechanical engineering, finance, IoT) and NLP tasks like sarcasm detection. His articles frequently appear in IEEE, Springer, and Elsevier journals. Awards: AAIA’15 Data Mining Competition Award (2015) Projects & Service: Involved in 10+ international/domestic research projects IEEE member since 1991, ACM member since 1997 Program committee member for multiple international conferences

Felix Heideمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• Optics
• +۶ مورد دیگر

Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.

Kebin Pengمشاهده پروفایل

استادیار

• Computer Vision
• 3D Computer Vision
• Depth Estimation
• +۷ مورد دیگر

Dr. Kebin Peng is an Assistant Professor in the Department of Computer Science at East Carolina University's College of Engineering and Technology. He joined ECU in 2024 after working as a Senior Software Engineer at MathWorks (2023-2024) and completing his Ph.D. at the University of Texas at San Antonio (2019-2023). Dr. Peng's research focuses on Computer Vision and Machine Learning, with particular expertise in 3D Computer Vision, Depth Estimation, Point Cloud Detection/Segmentation, and 3D Reconstruction. His work often explores unsupervised learning approaches to solve complex vision problems. He teaches graduate and undergraduate courses including Artificial Intelligence, Software Engineering, and Principles of Programming Languages. His publication record shows consistent contributions to top computer vision venues, with recent papers on monocular depth estimation in dynamic scenes, multi-view 3D reconstruction using transformers, and code clone analysis in VR software. Dr. Peng actively contributes to the research community as a reviewer for major conferences including NeurIPS, CVPR, ICCV, and AAAI. Dr. Peng serves on program committees for multiple prestigious conferences and journals, demonstrating his growing recognition in the computer vision and AI research community. His professional service includes reviewing for Neural Information Processing Systems (NeurIPS) 2024, AAAI Conference on Artificial Intelligence 2024, and multiple CVPR conferences. With his blend of academic research experience from UT San Antonio and industry experience at MathWorks and Samsung Research America, Dr. Peng brings a practical perspective to his teaching and research, connecting theoretical concepts with real-world applications in computer vision and artificial intelligence.

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.