جست‌وجوی استاد و پژوهشگر

Yuan Tianمشاهده پروفایل

استادیار

• Software Engineering
• Applied Machine Learning
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Canada. Her research focuses on applying artificial intelligence and machine learning techniques to solve software engineering challenges, particularly in the areas of code analysis, technical debt management, and developer productivity enhancement. Dr. Tian received her Ph.D. in Information Systems from Singapore Management University in May 2017 under the supervision of Prof. David Lo (IEEE/ACM fellow). Prior to joining Queen's University, she worked as a data scientist at the Living Analytics Research Centre (LARC) in Singapore. She has also conducted research visits at Carnegie Mellon University in 2015 and Inria Paris in 2013. Dr. Tian's research spans several key areas in software engineering with AI: Automatic technical debt, bug, and code change management LLM applications for code transformation and generation Human-AI collaboration in software development Analysis of developer interactions with AI tools like ChatGPT Mining software repositories for insights into development practices Her recent work has increasingly focused on leveraging Large Language Models to address software engineering challenges, with publications examining code translation, technical debt identification, and the dynamics of developer-AI interactions. Her research demonstrates a strong empirical approach, often analyzing large datasets from GitHub and other software development platforms. Dr. Tian has received recognition for her work, including the Best Research Paper Award at AI Foundation Models and Software Engineering (Forge), 2024 for her paper "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation." Dr. Tian leads the RISE research lab at Queen's University, which currently includes 5 PhD students, 2 MSc students, and 2 undergraduate research assistants. She has successfully supervised several graduate students to completion, with alumni now working at institutions including Duke University and Veeva Systems. Her research is supported by funding including an NSERC Alliance-Mitacs project on "Pragmatic Automated Code Transformation Leveraging Large Language Models" in collaboration with industry partner Ross Video. The RISE lab (Goodwin 621) is dedicated to developing reliable and intelligent support for software engineering. The lab's current research focuses on three main thrusts: automatic technical debt/bug/code change management, LLM for code transformation, and human-AI collaboration in software development.

Yakun Zhangمشاهده پروفایل

دانشیار

• Intelligent Software Engineering
• Multi-Agent Systems
• Trustworthy Large Language Models
• +۱ مورد دیگر

Yakun Zhang is an Associate Professor at the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). She holds a Ph.D. in Software Engineering from Peking University (2021-2025) and was a visiting scholar at the National University of Singapore. Her research integrates AI technologies like large language models to advance software engineering automation. Education Ph.D. in Software Engineering, Peking University (2021-2025) M.S. in Software Engineering, University of Chinese Academy of Sciences (2018-2021) B.S. in Computer Science, Wuhan University (2014-2018) Research Focus Her work spans: Intelligent Software Engineering (LLM-powered code generation/testing), Multi-Agent Systems (collaborative AI agents), and Trustworthy LLMs (addressing hallucinations/security). Recent publications demonstrate strong emphasis on GUI testing automation and dynamic analysis frameworks. Awards & Honors CCF System Software Outstanding PhD Dissertation Nomination (2025) Tencent Qingyun Talent Program & ByteDance Soaring Star Talent (2025) Outstanding Graduate of Beijing Municipality & Peking University (2025) National Scholarship & President's Awards (Peking University/CAS) Academic Service PC member for ASE 2025, ICSE 2026, and MSR 2025. Reviewer for ACM TOSEM and IEEE TSE. Actively recruits PhD/Master's students and collaborates with NUS, Microsoft, and Tencent.

Fatemeh Hendijani Fardمشاهده پروفایل

استادیار

• Code Intelligence
• Transfer Learning
• AI4SE
• +۵ مورد دیگر

Fatemeh Hendijani Fard is an Assistant Professor in the Department of Computer Science at the University of British Columbia's Okanagan campus. She serves as a graduate student supervisor and teaches courses in Computer Science and Data Science. Dr. Fard is a member of the CITECH program and MMRI, part of the Killam family of scholars, and an active member of both IEEE and ACM. Her research focuses on the intersection of Natural Language Processing and Software Engineering, with particular emphasis on developing code intelligence models for low-resource programming languages like R. She conducts empirical studies and develops techniques to improve the computational efficiency of code-language models while making them accessible to communities with restricted GPU access. Her work strongly advocates for Diversity and Inclusion in STEM, particularly for underrepresented females. Analysis of Dr. Fard's recent publications reveals a strong research trajectory in adapting Large Language Models for code intelligence with a focus on efficiency and accessibility. Her work spans multiple dimensions including code summarization, method name prediction, code search, code clone detection, and program repair, with special attention to low-resource programming languages. A notable trend is her exploration of adapter-based approaches for knowledge transfer that reduce computational requirements while maintaining performance. Izaak Walton Killam Memorial Scholarship Alberta Innovates Technology Futures (AITF) NSERC Discovery NSERC CREATE Mitacs Accelerate UBC Start-up Fund Dr. Fard has secured significant research funding including NSERC Discovery, NSERC CREATE, Mitacs Accelerate, and UBC Start-up funds to support her work on code intelligence for low-resource programming languages. She actively serves as a graduate student supervisor, guiding research in areas related to code representation learning and mining software repositories. Her service to the academic community is extensive, having served on program committees for major conferences including FSE, MSR, ASE, SANER, and ICSME across multiple years. Dr. Fard leads research initiatives focused on making code intelligence accessible to communities working with understudied programming languages. Her team conducts empirical studies and develops new techniques specifically designed for low-resource languages, with particular attention to the R programming language. This work addresses diversity and inclusion in AI tools by ensuring developers with limited computational resources can benefit from advances in neural networks and automated tools.

• Software Engineering
• Software Quality Assurance
• Data-Intensive Methods
• +۳ مورد دیگر

Morgan Ericsson is a professor at Linnaeus University's Faculty of Technology, Department of Computer Science and Media Technology. He has coordinated the Linnaeus University Centre for Data Intensive Sciences and Applications and leads the research group Data Intensive Software Technologies and Applications (DISTA). His work spans software engineering, quality assessment, and machine learning applications in system design. Research coordinator, DISA Faculty member, Linnaeus University His research focuses on defining and measuring software quality through data-driven approaches, including code metrics, documentation evaluation, and machine learning. He develops methods to balance performance, safety, and functionality in complex systems. Recent publications involve generative adversarial networks for log end identification, copula-based metrics aggregation, and graph convolution networks for code-architecture mapping. Key themes include automated classification, density analysis, and educational software tools. Ericsson has led projects like "HPC for SME" and "In-line Visual Inspection Using Unsupervised Learning," while contributing to software infrastructure for quality assessment and mobile learning frameworks.

Arnoud Visserمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Robotics
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Arnoud Visser is a Senior Lecturer at the Informatics Institute within the Faculty of Science at the University of Amsterdam . He holds a PhD in Computer Science (2007) and an MSc in Experimental Physics (1987). His research focuses on artificial intelligence and robotics , particularly cooperative robot teams in real-world environments. He has led projects like Meaningful Control of Autonomous Systems (2020) and developed simulation frameworks for RoboCup Rescue challenges. His 15 most recent publications (2022-2025) span topics including generative AI for military scene understanding , GAN-based nighttime re-identification , soft actor-critic locomotion , and ROS2 simulation environments . These works integrate machine learning with robot navigation and multi-agent coordination . Scientific Awards include: 1th Prize in RoboCup Rescue Simulation League (2018, 2014, 2012) Best Demo Award (Benelux Conference on AI 2016) IEEE RAS Most Active Technical Committee Award (2018) USARsim Development Prize (2010) As a thesis committee member for 27 PhD and 68 Master's theses (2000-2025), he has mentored students in areas like spiking neural networks , soft robotics , and autonomous navigation . He has also served on program committees for major robotics conferences including ICRA, IROS, and RoboCup Symposia (2011-2025).

Jim Buckleyمشاهده پروفایل

استاد

• Software Reengineering
• Empirical Software Engineering
• Information Seeking in Software Engineering
• +۷ مورد دیگر

Jim Buckley is a Professor in the Department of Computer Science & Information Systems at the University of Limerick, and a Principal Investigator at Lero – the Irish Software Research Centre. He holds a BSc in Biochemistry from the National University of Ireland, Galway (1989), followed by an MSc (1994) and PhD in Computer Science (2002) from the University of Limerick. His research focuses on software reengineering, empirical software engineering, and source code analysis, with notable contributions to clone detection, software architecture recovery, and AI-driven code generation. He led the TREES Centre, a €6M industry-funded initiative, and currently oversees three research teams at Lero investigating large-scale clone detection, deep learning architecture recovery, and AI-generated source code. His work intersects technical innovation with societal impact, such as ethical software engineering and pandemic response technologies like contact tracing apps. Key research themes include software architecture consistency, feature location, and developer experience optimization. He has published over 118 peer-reviewed articles and received a Research Impact award from Lero. His contributions span open-source tool development, empirical studies in software practices, and interdisciplinary projects addressing global challenges like public health and cryptocurrency equity. Education: BSc Biochemistry, National University of Ireland, Galway (1989) MSc Computer Science, University of Limerick (1994) PhD Computer Science, University of Limerick (2002) Awards: Research Impact Award, Lero (2021) Labs/Teams: TREES Centre (Trustworthy, Responsible, Efficient Engineering of Software) Three core Lero teams: Clone Detection, Architecture Recovery, and AI-Code Generation

Jean-Guy Schneiderمشاهده پروفایل

استاد

• Software Engineering
• Quantum Computing
• Cloud Computing
• +۴ مورد دیگر

Prof. Jean-Guy Schneider is a Professor and Associate Dean (Education) in the Faculty of IT at Monash University. He holds a PhD in Computer Science from the University of Bern, Switzerland, and has over 20 years of experience in higher education. His research focuses on reliable software technologies, including component-based systems, cloud/mobile computing, quantum software, and agile methodologies. He actively contributes to software engineering education and industry-relevant research. His key research areas include energy-efficient IoT architectures, machine learning model security (e.g., MLGuard project), and quantum computing abstractions. Recent work emphasizes optimizing workflows for energy/performance trade-offs and addressing challenges in open-source ML projects. Prof. Schneider has published extensively on topics like service virtualization, Docker container monitoring, and anomaly detection in cloud systems. He supervises PhD candidates in software engineering for machine learning and quantum computing.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Alexander Ilin is a Visiting Professor and part-time teacher in the Department of Computer Science at Aalto University, specializing in Artificial Intelligence and Machine Learning. He holds roles in both the Computer Science - Artificial Intelligence and Machine Learning (AIML) research area and the Professors of Practice group. His research focuses on Machine Learning, Reinforcement Learning, and Deep Learning applications, with contributions to areas like neural networks, generative models, and healthcare informatics. Dr. Ilin earned a Doctoral degree in Engineering and Technology from Helsinki University of Technology in 2006. His work aligns with UN Sustainable Development Goals, particularly in advancing education and innovation. Key projects include the Finnish Center for Artificial Intelligence (FCAI) and the APPIA project on privacy-aware AI applications. His research explores cutting-edge topics such as diffusion models for dynamical systems, reinforcement learning for robotics, and self-supervised forecasting in healthcare. He has led projects like APPIA (2020–2021) and FCAI (2020–2026), securing grants from the Academy of Finland and Business Finland. His work bridges theory and practice, with applications in autonomous systems, nanotechnology, and medical diagnostics. Notable activities include visiting research at the UK Met Office Hadley Centre and presentations at top conferences like NeurIPS and AAMAS. His lab focuses on scalable AI solutions for real-world challenges, emphasizing ethical AI and privacy-preserving techniques.

Christof Ferreira Torresمشاهده پروفایل

استادیار

• Blockchain Security
• Smart Contract Analysis
• Decentralized Finance (DeFi)
• +۴ مورد دیگر

Christof Ferreira Torres is an Assistant Professor at the Department of Computer Science and Engineering (DEI) at Instituto Superior Técnico (IST), University of Lisbon, and a researcher at INESC-ID in the Distributed, Parallel and Secure Systems (DPSS) group. His research focuses on program analysis, software security, and blockchain systems, particularly addressing vulnerabilities in smart contracts and decentralized finance (DeFi). He holds a Ph.D. from the University of Luxembourg and Technical University of Munich under Professors Radu State and Claudia Eckert. Education: Ph.D. in Computer Science, 2022 (University of Luxembourg & TU Munich) Postdoctoral Fellow at ETH Zurich (2022–2023) His research interests include blockchain security, MEV (Maximal Extractable Value) analysis, cross-chain interoperability, and privacy in Web3. Notable contributions include frameworks like Horus for attack detection in smart contracts and Elysium for automatic vulnerability patching. He actively participates in academic service, serving on program committees for major conferences like S&P, CCS, and USENIX. Recent work highlights cross-chain arbitrage dynamics, privacy leaks in web wallets, and centralized risks in blockchain infrastructure. His findings emphasize the need for decentralized solutions to counteract threats to blockchain liveness and finality. Key Awards: TLDR Research Fellowship (2024) Excellent Doctoral Thesis Award (2022) UBRI Impact Award (2022) He teaches courses on information security, dependable systems, and computer science foundations at IST and ETH Zurich. His work bridges theoretical computer science with practical blockchain security challenges, addressing both academic and industry needs.

John-Paul Oreمشاهده پروفایل

استادیار

• Robot Software Reliability & Testing
• Program Analysis for Cyber-Physical Systems
• Flying Robots for Environmental Monitoring
• +۶ مورد دیگر

John-Paul Ore is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His research bridges software engineering and field robotics, with a focus on program analysis, system testing, and high-resolution physical simulators for robotics systems, particularly those built with the Robot Operating System (ROS). He develops software engineering methods that improve the dependability of robotics systems through techniques for dimensional analysis without developer annotations, open-source tools like PHYS, and public datasets documenting dimensional inconsistencies in real-world systems. His educational background includes a Ph.D. from the University of Nebraska-Lincoln (2019) and a B.A. in Philosophy from the University of Chicago. His interdisciplinary background informs his approach to combining software engineering with robotics to address challenges in reasoning about full-system behavior across multiple layers of abstraction. Ore's research interests center on applying program analysis techniques to software that controls robots and interacts with the physical world. His work includes abstract type inference of physical unit types (like 'meters-per-second'), probabilistic techniques for combining semantic information in identifiers with code flow inference, and empirical measurements of how developers make decisions about robotic software. He focuses on program analysis and software testing that enhances system safety and reliability while remaining practical and economically efficient. His research has significant applications in environmental monitoring, addressing climate challenges, food production, and liberating people from dangerous, dirty, and dull work. His publication record shows a clear trajectory from foundational work on dimensional analysis in robotics software to increasingly complex applications in environmental monitoring and autonomous systems. The most recent publications demonstrate expansion into Large Language Models for code analysis while maintaining focus on practical robotics applications. His research consistently addresses the critical gap between theoretical program analysis and practical robotics system development. Best Tool Demonstration Award, ISSTA'17 for Phriky-Units ACM SIGSOFT Travel Award ($300) Othmer Fellowship 2014-2018 ($8K/year) UNL CSE Outstanding Master's Thesis Award 2015 RSS 2013 Travel Grant ($500) Ore actively mentors students, having served as research mentor for undergraduates Becca Horzewski (2016-17) and Lambros Karkazis (2018). His research is supported by significant grants including FARM BILL: NRI: INT ($1,018,596 from NSF), SHF: SMALL ($499,994 from NSF), and North Carolina Space Grant ($5,000 from NASA). He is currently recruiting PhD students for his lab focused on robotics and software engineering. His laboratory work combines robotics, software engineering, and environmental monitoring, with projects including autonomous aerial water sampling systems, UAV-based environmental sensing, and tools for improving robotics software reliability. His team develops both theoretical approaches and practical implementations, often creating open-source tools that bridge the gap between academic research and industry applications.

Safwat Hassanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Mobile Computing
• Software Engineering
• +۴ مورد دیگر

Safwat Hassan is an Assistant Professor at the University of Toronto's Faculty of Information . He holds a PhD from Queen’s University and has over a decade of industry experience in software engineering roles at companies like the Egyptian Space Agency, Hewlett Packard, Vodafone, and Etisalat. His research focuses on analyzing mobile apps, user-developer interactions, and improving app store quality through techniques like review analysis and release engineering. Education: PhD (Software Analysis and Intelligence Lab, Queen’s University), MSc & BSc (Helwan University). Certifications include Sun Certified Java Programmer and OMG-Certified UML Professional. Research interests span Artificial Intelligence , Mobile Computing , Software Engineering , and User-Centric Design . Recent work explores Large Language Model (LLM) applications in mobile apps, performance bug prediction, and competitive analysis of app features. Current teaching includes INF1341H: System Analysis and Process Innovation. Supervises graduate students Sara Ibrahim Al Hajj Ibrahim and Buthayna AlMulla. Active in open-source Android analysis and CI/CD configuration studies.

Önder Baburمشاهده پروفایل

پژوهشگر

• Clone Detection
• Model-Driven Engineering
• Analytic Model
• +۵ مورد دیگر

Önder Babur is a University Researcher at Eindhoven University of Technology in the Department of Mathematics and Computer Science, specializing in Software Engineering and Technology. His research focuses on software analytics, model-driven engineering, and machine learning applications in software development. Research interests span clone detection, analytical modeling, and information retrieval systems. His work integrates deep learning techniques to advance software development processes and source code analysis. Publications demonstrate strong focus on empirical software engineering, with recent trends showing applications in energy systems, digital twins, and cyber-physical systems. Research consistently incorporates machine learning methodologies across domains. Research Output (2023-2025): 8 journal articles on software analytics and ML applications 7 conference contributions on model-driven engineering 3 datasets related to computational modeling

Mahdi Saeedi Nikooمشاهده پروفایل

پژوهشگر

• Software Engineering
• Business Process Modeling
• Service Composition
• +۵ مورد دیگر

Mahdi Saeedi Nikoo is a researcher affiliated with the Eindhoven University of Technology under the Mathematics and Computer Science school in the Software Engineering and Technology department. His research focuses on Software Engineering , Business Process Modeling , Service Composition , Clone Detection , and Industrial Internet of Things . The titles of his recent publications reflect a strong emphasis on empirical studies in software engineering, domain-specific languages, and architectural frameworks for IoT. Key trends in his work include the application of variability modeling in software systems, integration of cloud platforms, and analysis of open-source repositories like GitHub. Mahdi's contributions span datasets, conference papers, and journal articles, with citations in areas such as Industrial Internet of Things and Service Composition . He has no listed scientific awards, and his supervised work includes datasets and collaborative research outputs.

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.

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.