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Mehdi Bagherzadehمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Mehdi Bagherzadeh is an Assistant Professor in the Department of Computer Science and Engineering at Oakland University. His professional profile shows consistent involvement in major software engineering conferences including ASE, SPLASH, and ICSE, where he has served in various leadership roles such as Workshops Co-Chair for SPLASH (2021-2023) and Late Breaking Results Co-Chair for ASE (2022). Dr. Bagherzadeh's research focuses on the correct construction of concurrent and big data software, situated at the intersection of Software Engineering and Programming Languages. His work particularly addresses challenges in deep learning program optimization, actor concurrency models, and automated refactoring techniques. His research trajectory shows a clear evolution from foundational work on concurrent programming models (Panini, Capsule) to current applications in deep learning systems. Analysis of his publication history reveals a strong focus on practical software engineering challenges in concurrent systems, with recent work concentrating on the migration and optimization of deep learning programs. His research combines empirical studies with formal methods, addressing both theoretical foundations and practical implementation concerns in modern software systems. Through his committee service across multiple top-tier conferences including ASE, SPLASH, ICSE, and ESEC/FSE, Dr. Bagherzadeh has established himself as an active contributor to the software engineering research community. His service roles have ranged from program committee membership to organizational leadership positions.

پروفایل رسمی دانشگاه

Pinjia Heمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.

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Jie Luمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Jie Lu is an Associate Professor at the Institute of Computing Technology of the Chinese Academy of Sciences (ICT, CAS), where he leads research in software security and program analysis. His work focuses on developing advanced program analysis techniques to improve software reliability and security, with applications in cloud systems, distributed environments, and modern web applications. Dr. Lu's research interests include: Software Security: Focusing on vulnerability detection and prevention in open-source software Program Analysis: Specializing in static/dynamic analysis techniques and context-sensitive pointer analysis Cloud Systems: Researching distributed system security, crash-recovery, and concurrency bug detection His recent publications demonstrate a strong focus on practical security solutions for real-world systems. The research spans Kubernetes ecosystems, PHP applications, Linux kernel security, Java web applications, and Windows IPC systems. A notable trend is the development of precise static analysis techniques that balance efficiency with accuracy, addressing the longstanding challenge in program analysis. His work often bridges theoretical advances with practical implementations that have been adopted by industry. Dr. Lu has received several prestigious awards: ACM SIGSOFT Distinguished Paper Award 2025 Best Paper Honorable Mention at CCS 2022 Chinese Academy of Sciences Outstanding Doctoral Dissertation 2021 Chinese Academy of Sciences President's Special Award 2020 ICT New Hundred Stars 2020 Dr. Lu actively mentors students and researchers, recruiting PhD candidates, Master students, and research interns interested in software security and program analysis. His research has been supported by the National Natural Science Foundation of China, CCF-Huawei Innovation Research Plan, and CCF-Ant Research Fund. The Program Analysis Group (ICT-PAG) at the National Key Laboratory of Processor has successfully identified numerous errors and vulnerabilities in popular open-source applications, with over 200 severe bugs confirmed by the open-source community and assigned more than 100 CVE numbers. His research group, the Program Analysis Group (ICT-PAG), is based in the National Key Laboratory of Processor at ICT, CAS. The group has achieved significant impact through both academic publications in top venues (SOSP, CCS, USENIX Security, NDSS, OOPSLA, ISSTA, FSE, ASE, TSE) and practical applications in leading IT companies and government organizations.

پروفایل رسمی دانشگاه

Lars Grunskeمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Lars Grunske is a Professor in the Department of Computer Science at Humboldt University of Berlin, Germany. His academic career spans multiple institutions across Germany, Australia, and internationally, with a strong focus on research and conference participation in software engineering. His educational background includes a PhD in computer science from the University of Potsdam (Hasso-Plattner-Institute for Software Systems Engineering) in 2004. Professor Grunske's research interests center on modeling and verification of systems and software, with particular emphasis on automated analysis techniques. His work primarily focuses on probabilistic and timed model checking and model-based dependability evaluation of complex software intensive systems. He has made significant contributions to software testing, program repair, formal methods, and the application of machine learning techniques to software engineering problems. His publication record shows consistent contributions to top software engineering conferences over the past decade, with recent work exploring the intersection of AI/ML with traditional software engineering challenges. His research demonstrates an evolution from foundational model checking techniques toward more practical applications in software testing and repair. Boeing Postdoctoral Research Fellow Professor Grunske actively mentors through conference activities including chairing mentoring circles at ICSE 2021. He serves on numerous program committees for major software engineering conferences including ASE, ICSE, ESEC/FSE, and others, demonstrating his standing in the academic community. His involvement spans multiple roles from committee member to track chair and award committee positions. He maintains an active research laboratory focused on software verification and testing, as indicated by his departmental affiliation and research website.

پروفایل رسمی دانشگاه

Andreas Zellerمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Andreas Zeller serves as Professor for Software Engineering at Saarland University and faculty at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. His dual appointments position him at the intersection of academic research and practical cybersecurity applications, contributing significantly to both institutions' research profiles. Professor Zeller's research spans multiple dimensions of software quality assurance, with particular expertise in automated debugging techniques, mining software repositories for insights, specification mining, and security testing methodologies. His work consistently bridges theoretical foundations with practical implementation, resulting in tools and frameworks adopted widely in both research and industry contexts. Analysis of his recent publications reveals an evolutionary trajectory from foundational debugging work toward increasingly sophisticated grammar-based testing approaches, with notable integration of machine learning techniques in recent years. His research demonstrates consistent focus on improving software reliability through automated analysis, with growing emphasis on security applications including XML injection testing, GNSS module security, and binary file format vulnerabilities. Recipient of two ERC Advanced Grants (including the S3 project) ACM Fellow ACM SIGSOFT Outstanding Research Award Professor Zeller has successfully secured substantial research funding through competitive mechanisms including ERC grants, enabling his team to pursue ambitious research agendas. His leadership extends to mentoring through his roles as doctoral symposium co-chair and active participation in new faculty development initiatives. He maintains strong engagement with the research community through numerous program committee memberships and conference organization roles. At CISPA Helmholtz Center for Information Security, Zeller contributes to the center's mission through research focused on software security testing and analysis. His work on grammar-based testing and fuzzing directly addresses critical security challenges in modern software systems, with practical applications for improving software resilience against attacks.

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Austin Mordahlمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Austin Mordahl is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research focuses on improving the usability and reliability of software quality assurance tools, particularly static analyses and fuzz testing. Dr. Mordahl actively seeks students interested in cutting-edge research in software engineering at UIC. Dr. Mordahl's research interests span multiple areas of software engineering with a particular emphasis on: Static Analysis and Taint Analysis for software security Automated Testing techniques including fuzz testing Improving the usability and reliability of software quality assurance tools Configurable static analysis tools and their behavior Applying machine learning to triage and configure static analysis tools Lifting static analysis to work on software product lines Improving evaluations of fuzz testing tools His recent publications demonstrate a strong focus on addressing challenges in configurable static analysis tools, with particular attention to nondeterministic behavior, configuration spaces, and automatic testing and debugging approaches. Mordahl's work bridges theoretical foundations with practical applications, aiming to make software quality assurance tools more accessible and effective for developers. Dr. Mordahl has received several prestigious awards for his work: NSF Graduate Research Fellowship Awardee (2020) Eugene McDermott Graduate Research Fellowship Awardee (2020) ICSE 2019 Student Research Competition Winner Dr. Mordahl is actively involved in mentoring and seeks students interested in software engineering research. He has served on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and PLDI. His service to the academic community extends to artifact evaluation committees, demonstrating his commitment to research reproducibility and rigor.

پروفایل رسمی دانشگاه

Dirk Roosمشاهده پروفایل

استاد

Hochschule Niederrhein University of Applied SciencesGermany

Prof. Dr.-Ing. Dirk Roos is a Professor for Computersimulation und Design Optimization at the Department of Mechanical Engineering and Process Engineering, Niederrhein University of Applied Sciences, where he has served since March 2011. He is also the Head of the Institute for Modeling and High Performance Computing (IMH) since December 2016. His academic career includes previous positions as Head of Robust Design Optimization at DYNARDO Dynamic Software and Engineering GmbH (2002-2011) and Technical Solutions Specialist at CADFEM GmbH (2000-2008). Prof. Roos specializes in machine learning, robust design optimization, stochastic analysis, and probabilistic modeling for virtual product development. His research focuses on developing mathematical methods for stochastic structural and fluid simulation, robustness and reliability analysis, multidisciplinary optimization, and software development for complex system development. He has established strong collaborations with academic institutions including RWTH Aachen, Ruhr-Universität Bochum, and industrial partners like Siemens AG and Robert Bosch GmbH. His recent work explores Probabilistic Intelligence, cyber-physical systems, digital twins, and Big Data Analysis, with applications spanning power plant flexibility, renewable energy, turbomachinery, automotive, aerospace, and medical technology. Prof. Roos has successfully secured numerous third-party funded research projects totaling over 1.8 million euros, including collaborations with Siemens AG, Robert Bosch GmbH, and various universities. optiSLang Award 2012 Weimar Optimization and Stochastic Days Gutachter BMBF im Programm FH-Kooperativ since 2019 Mitglied des Scientific Committee International Probabilistic Workshop since 2017 Mitgliedschaft im Graduierteninstitut für angewandte Forschung der Fachhochschule in NRW since 2017 Prof. Roos supervises multiple doctoral students and has successfully completed several cooperative PhD projects. His current research projects include AI-driven optimization for medical care, reinforcement learning for emergency room planning, and machine learning algorithms for power plant component lifetime prediction. The Institute for Modeling and High Performance Computing (IMH) under his leadership develops mathematical methods and software competence in machine learning and CAE-based robust design optimization for virtual product development.

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Jooyong Yiمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Jooyong Yi is an Associate Professor in the Department of Computer Science and Engineering at UNIST (Ulsan National Institute of Science and Technology). He leads the LOFT (Lab of Software), focusing on autonomous techniques for software reliability in AI-generated code environments. Research Interests: His work spans program analysis, automated repair, testing/debugging, and verification. Core themes include developing scalable methods for bug detection (via static/dynamic analysis), AI-compatible repair systems, and verification frameworks for safety-critical systems. Recent emphasis integrates fuzzing techniques with repair validation. Publication Trends: His 15 most recent works (2015-2025) show progression from foundational program repair techniques (e.g., Angelix, DirectFix) toward AI-era innovations: greybox fuzzing for efficiency, memory-leak repair for web frameworks, and deep-learning library testing. Over 50% of publications focus on optimizing repair validation and scalability. Awards: ACM Distinguished Paper Award at ASE 2023 Students & Grants: Currently advises 5 PhD, 1 MS/PhD, and 2 MSc students. Secured ₩20B+ in funding for projects including: MSIT Binary Micro-Security Patch Technology (2024-2026) Patch Validation for Automated Repair (2023-2026) AI-Powered Low-Code Platform (2023-2025) Memory-Safe Language Integration (2024-2027) Lab: LOFT lab develops verified repair tools (e.g., LeakPair, Verifix) and benchmarks (BUGSC++), prioritizing human oversight in AI-generated software.

پروفایل رسمی دانشگاه

Shane McIntoshمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Shane McIntosh is an Associate Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he leads the Software Repository Excavation and Build Engineering Labs (Software REBELs). His academic career focuses on empirical studies of software development processes with particular emphasis on release engineering and software quality. Dr. McIntosh's research centers on mining historical data generated during software development to derive practical insights for building more reliable systems. His work spans release engineering (assembling, verifying, and delivering software releases) and software quality (developing guidelines for reliable software). This research manifests in studies of continuous integration systems, build outcome prediction, defect prediction models, and code review practices. His publication record reveals a consistent focus on empirical software engineering with recent papers examining build system reliability, continuous integration practices, and defect prediction. The research demonstrates strong methodological rigor through replication studies, longitudinal analyses, and large-scale data mining of software repositories. His work bridges theoretical insights with practical applications for software development teams. Dr. McIntosh actively contributes to the software engineering community through substantial service roles including Proceedings Co-chair for ICSE 2022, General Chair for PROMISE 2021-2022, and committee positions across major conferences like ASE, ESEC/FSE, and MSR. His teaching portfolio includes foundational courses such as Introduction to Software Engineering, Software Analytics, and Software Delivery. He directs the Software REBELs lab, which provides a collaborative environment for investigating software development data. The lab's work focuses on extracting meaningful patterns from version control systems, issue trackers, and continuous integration pipelines to improve software engineering practices.

پروفایل رسمی دانشگاه

Wei Chenمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Wei Chen is a Research Fellow at the Institute of Software, Chinese Academy of Sciences, where he serves as a PhD and Master's supervisor. He leads the Software Engineering Technology R&D Center and maintains affiliations with the University of Chinese Academy of Sciences and its Nanjing College. Dr. Chen has established himself as a leading figure in intelligent software engineering research within China's academic community. His primary research focuses on four interconnected areas: intelligent code maintenance and quality assurance (particularly Python ecosystem compatibility based on domain knowledge), reliability assurance of complex IoT systems in human-machine-object convergence scenarios, cloud-native system development with emphasis on Function-as-a-Service optimization, and quality assurance of deep learning frameworks in resource-constrained environments. Dr. Chen's work consistently bridges theoretical advances with practical applications, maintaining strong industry collaborations with major Chinese technology companies. Analysis of Dr. Chen's recent publications reveals a strategic integration of AI techniques with traditional software engineering challenges. His research shows increasing emphasis on leveraging large language models for IoT component synthesis, sophisticated dependency management solutions for Python ecosystems, and innovative approaches to testing autonomous systems. The work demonstrates both theoretical depth and practical utility, with many publications leading to implemented tools and systems. Second Prize of Science and Technology Progress Award of China Institute of Electronics (2022) First Prize of Science and Technology Progress Award of China Institute of Electronics (2021) ACM SIGSOFT Distinguished Paper Award (2023) Special Prize of the 4th China Software Open Source Innovation Competition (2021) First Prize in the 4th China Software Open Source Innovation Competition (2021) OW2 Programming Contest First Prize (2016) Dr. Chen has mentored over ten graduate students who have achieved notable success in academic competitions and industry placements. His laboratory (TCSE, http://tcse.cn/) currently manages multiple significant research projects including 'Complex IoT System Reliability Assurance Key Technology Research' (2025-2028), 'Intelligent Development, Testing, and Maintenance of Cloud-native Software Ecosystems' (2024-2027), and 'Traffic Infrastructure Digital Industrial Software Architecture and Core Technology Standard System' (2021-2024). The lab maintains active collaborations with Huawei, Alibaba, Tencent, and other leading technology enterprises.

پروفایل رسمی دانشگاه

Huaming Chenمشاهده پروفایل

مدرس ارشد

Max Planck Institute for Security and PrivacyGermany

Dr. Huaming Chen is a Senior Lecturer in the School of Electrical and Computer Engineering at The University of Sydney, Australia. His work focuses on trustworthy machine learning systems, software engineering, and software security. With numerous publications in top-tier conferences and journals, Dr. Chen has established himself as a significant contributor to the fields of AI security and software engineering. Dr. Chen's primary research interests lie at the intersection of software engineering and artificial intelligence, with a strong emphasis on trustworthy AI systems. His work spans several key areas including: Software Security for AI-enabled systems Trustworthy and Responsible AI development Computational biology applications Industrial 4.0 implementations Federated learning and privacy-preserving techniques Large language model verification and uncertainty analysis His research addresses critical challenges in ensuring AI systems are secure, reliable, and ethically sound. Dr. Chen's recent publications demonstrate a strong trend toward addressing security and trustworthiness challenges in AI systems. His work spans multiple domains including software security (particularly for AI systems), trustworthy AI development, and applications in computational biology. A significant portion of his recent work focuses on large language models, examining their vulnerabilities, verification methods, and uncertainty analysis. He also maintains active research in federated learning, adversarial machine learning, and software security techniques. Dr. Chen has received several notable awards and recognitions: 2020 IEEE CIS Student Grant for IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2017 Student and Early Career Travel Fellowship for The 16th International Conference on Bioinformatics (InCoB 2017) 2017 Student Travel Award for 2017 IEEE World Congress on Services Dr. Chen actively supervises multiple research students working on cutting-edge projects related to trustworthy AI and software security. His current students are exploring topics ranging from blockchain-based governance frameworks to open-source AI security and digital twin platforms. He also serves in numerous committee roles at top conferences including area chair for ACM MM, and PC member for ACM CCS, IJCAI, KDD, and many others. His service as a Guest Editor for journals like Computers & Security and as a Grant Reviewer for UKRI demonstrates his standing in the research community. Dr. Chen organizes workshops focused on Trustworthy and Responsible AI, reflecting his commitment to advancing the field. His research group appears to focus on practical applications of AI security techniques, with projects spanning multiple domains including healthcare, finance, and industrial systems. He maintains active collaborations with researchers across multiple institutions, as evidenced by his co-authorship on diverse publications.

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Masud Rahmanمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

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Michael Hiltonمشاهده پروفایل

استاد آموزشی

Max Planck Institute for Security and PrivacyGermany

Michael Hilton is an Associate Teaching Professor at Carnegie Mellon University's School of Computer Science, where he serves as the Associate Department Head for Education in the Software and Societal Systems Department. He directs the Software Engineering Minor and the Software Engineering Concentration programs, and teaches various software engineering courses including Foundations of Software Engineering, Introduction to Software Construction, and Crafting Software. His research interests focus on improving the lives of software engineers, with particular emphasis on flaky tests , continuous integration , and structured editors . His work bridges both the research and educational aspects of software engineering, aiming to make software development processes more reliable and efficient while educating the next generation of software engineers. Analysis of his recent publications reveals a strong focus on flaky test detection and mitigation, with significant contributions to understanding test reliability in continuous integration environments. His research spans empirical studies of testing practices, machine learning approaches to flakiness prediction, and educational aspects of software engineering. A notable trend is his increasing focus on the intersection of software testing and education, particularly examining how students interact with modern development tools and practices. Professor Hilton is highly active in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and SIGCSE. He has contributed to numerous workshops and symposia focused on software engineering education and testing research. As an educator, he has developed and taught multiple software engineering courses covering object-oriented design, testing, concurrency, and software construction principles. He emphasizes practical, hands-on learning experiences that prepare students for real-world software development challenges. His educational research explores best practices for teaching software engineering and the impact of new technologies like generative AI on software engineering education. He maintains an active research lab focused on software engineering tools and education, collaborating with both academic and industry partners to address practical challenges in software development workflows. His current projects include investigations into flaky test patterns, continuous integration practices, and the integration of modern development tools into software engineering curricula.

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Sallam Abualhaijaمشاهده پروفایل

پژوهشگر

Max Planck Institute for Security and PrivacyGermany

Dr. Sallam Abualhaija serves as a Researcher at the Interdisciplinary Centre for Security, Reliability, and Trust (SnT) at the University of Luxembourg, where she applies cutting-edge AI technologies to software engineering challenges with emphasis on requirements engineering and regulatory compliance. Her work bridges academic research and industry applications through extensive collaboration with corporate partners. She earned her PhD in Computer Science from Hamburg University of Technology (Germany) in 2016, establishing her expertise in computational approaches to software requirements. Her research centers on leveraging natural language processing and large language models for regulatory compliance, particularly GDPR implementation in software systems. Key focus areas include automated compliance checking of privacy policies, data processing agreements, and financial regulations through techniques like question-answering systems and ambiguity resolution. This work directly addresses the critical need for trustworthy AI systems that adhere to legal frameworks while improving software development efficiency. Analysis of her recent publications reveals a strong trend toward practical tool development (e.g., CompAi) and multi-solution studies for GDPR compliance across mobile applications, financial services, and general software systems. Her research consistently integrates industry collaboration, with growing emphasis on LLM-driven approaches since 2023. Dr. Abualhaija actively contributes to the academic community through program committee roles at ASE, ICSE, and RE conferences, and as co-organizer of workshops including MO2RE (Multi-Disciplinary Requirements Engineering) and FinanSE. She has chaired tutorials on replication in NLP for requirements engineering and AI applications in regulatory compliance, demonstrating commitment to knowledge transfer and community building. As a core member of SnT, she contributes to Luxembourg's leading research hub for security, reliability, and trust in ICT systems. Her work aligns with SnT's mission through projects that develop verifiable, privacy-preserving technologies with real-world regulatory impact, particularly in European data protection contexts.

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Yutian Tangمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Dr. Yutian Tang serves as an Assistant Professor (UK Lecturer) and Principal Investigator at the School of Computing Science, University of Glasgow, where he supervises PhD students and leads research in AI-driven software engineering. His academic journey includes a PhD from The Hong Kong Polytechnic University's Department of Computing. His research spans AI+SE integration , particularly focusing on Large Language Models for program analysis, software testing, and Android security. Key areas include: LLM-assisted vulnerability detection and repair Empirical studies of real-world software systems Privacy protection mechanisms Configuration compatibility in mobile applications Smart contract security optimization His publication portfolio shows a clear trajectory toward AI-augmented software engineering , with recent work demonstrating how LLMs can enhance taint analysis, binary code similarity detection, and test generation. This evolution reflects the field's broader shift toward AI integration while maintaining rigorous empirical validation. Award highlights include: Best Industry Paper Award at ISSRE'18 Elevation to IEEE Senior Member (2024) Three Android OS defects confirmed by Google Security Team As an active researcher and community contributor, Tang serves on 40+ program committees including PLDI, ICSE, and FSE. His work receives funding from National Natural Science Foundation of China, Shanghai Science Commission, OpenAI, and Google. Current projects focus on automated bug localization and LLM-based testing frameworks, with recent grants from OpenAI Cybersecurity and Google Cloud programs. He leads research groups investigating Android security and AI-assisted program analysis, collaborating with institutions like Lund University.

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