Gilles Grimaud serves as Teacher Team Leader for the 2XS research group and Elected Member of the Unit Council at University of Lille's CRIStAL laboratory, located at Science City campus (Building M3 Extension, Office 209). His research spans critical domains in systems security: Formal verification of operating system kernels Memory isolation for IoT ecosystems Embedded systems security Cloud and wireless network intrusion detection Energy-constrained software development Distributed software verification from cloud to IoT Supervising over 14 doctoral theses through 2023, his students have pioneered verified hypervisors, Android security with eBPF, and DDoS detection frameworks. Current work focuses on formally proven security architectures for resource-limited devices. As core member of the 2XS team within CRIStAL (UMR 9189), he bridges theoretical formal methods with practical system implementations across constrained environments.
Sjouke Mauw is a full professor in "Security and Trust of Software Systems" at the University of Luxembourg , leading the Department of Computer Science and the SaToSS research group. His work bridges formal methods with real-world security and trust challenges. Current affiliation: University of Luxembourg (Faculty of Science, Technology and Communication) Previous roles: Associate Professor at Eindhoven University of Technology, Senior Researcher at CWI Research Focus Security protocols and their formal verification Attack trees and defense trees for structured threat analysis Privacy in voting systems and digital exchange Trust management frameworks and risk assessment RFID and e-voting security Cyber-physical socio-technical systems Recent Article Trends (2023-2025) include applications of deep learning to privacy risks, formal verification of distance-bounding protocols, and anomaly detection in resource-constrained environments like cubesats. These works span machine learning, cryptography, and aerospace engineering. Academic Contributions include over 50 refereed publications, supervision of 15+ PhD students, and leadership in European research projects (e.g., Meteor, Concur). He has developed formal methods for security protocols, attack-defense trees, and GNSS signal integrity frameworks.
Lina Gong is an Associate Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. She holds a Ph.D. in Computer Software and Theory from China University of Mining and Technology (2020) and completed a research visit at Queen's University's Software Analysis and Intelligence Lab (SAIL) under Prof. Ahmed Hassan (2019-2020). Her research focuses on leveraging machine learning to extract insights from software repositories, with emphasis on: ML-enabled defect prediction techniques Code pre-trained models for vulnerability detection Identifier normalization and issue classification Empirical studies of software quality attributes Her recent publications (2023-2025) demonstrate strong trends in applying transformer architectures to code analysis, with increasing focus on supply chain security and cross-platform UI translation. Key venues include IEEE TSE, ACM TOSEM, and ASE. Scientific recognition includes: National Natural Science Foundation of China (2022-2025) Natural Science Foundation of Jiangsu Province (2022-2025) Key National Laboratory Foundation (2022-2023) Excellent Ph.D. Student Award (CUMT) She actively mentors graduate students (14 advisees: 1 doctoral, 13 master's) and serves on program committees for ASE, APSEC, and SANER. Her research is supported by multiple competitive grants focusing on ML applications in software engineering.
Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.
Minxue Pan is a Professor and PhD supervisor at the Software Institute, State Key Laboratory for Novel Software Technology, Nanjing University, China. His research focuses on the dependability of complex software systems, with expertise spanning software modeling and verification, software analysis and testing, cyber-physical systems, mobile computing, and intelligent software engineering. Ph.D. in Computer Science and Technology from Nanjing University (2014), supervised by Prof. Xuandong Li B.Sc. from Nanjing University Studied at UC Berkeley's Department of Electrical Engineering and Computer Sciences (2009-2010) under Prof. Edward A. Lee Professor Pan's research interests center on improving software dependability through innovative approaches to modeling, analysis, and testing. His work spans traditional software systems, mobile applications (particularly Android), cyber-physical systems, and the application of AI techniques to software engineering problems. He has made significant contributions to GUI testing, vulnerability detection, and deep learning applications in software engineering. His recent publications (2024-2025) demonstrate a strong focus on applying advanced machine learning techniques to software testing and security challenges. Key trends include leveraging large language models for test migration, enhancing fault localization with graph learning and contrastive learning, developing specialized frameworks for Android security analysis, and improving test efficacy through GUI and functional equivalence. His work consistently targets real-world industrial settings and addresses practical challenges in mobile and complex software systems. ISSTA 2020 Distinguished Paper Award ICSE 2025 Best Artifact Award Professor Pan actively advises graduate students and has developed several notable tools including Q-testing (reinforcement-learning based Android testing), ISDChecker (model checking for interrupt-driven systems), PREFEST (preference-wise testing for Android), Sketchoid (GUI code search), and PI-REC (hand-drawn draft conversion). His research is supported by extensive publication records in top-tier software engineering venues including ASE, ICSE, FSE, ISSTA, and TOSEM. He teaches undergraduate courses in Advanced Programming with C++, Software System Design, and Software Construction, as well as graduate courses in Advanced Software Design. His laboratory work focuses on developing practical solutions for real-world software dependability challenges through the State Key Laboratory for Novel Software Technology.
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.
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.
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.
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.
Lian Li is a Professor in the Institute of Computing Technology at the Chinese Academy of Sciences, where he leads the program analysis research group. He holds a PhD from the University of New South Wales, Australia, and a Bachelor's degree from Tsinghua University in Engineering Physics. His research focuses on developing innovative program analysis techniques and tools to enhance software reliability and security. His educational background includes a PhD in Computer Science from the University of New South Wales (2003-2007) with a thesis on "ScratchPad Management for Static Data Aggregates" under Professor Jinling Xue, and a Bachelor's degree in Engineering Physics from Tsinghua University (1993-1998). Lian Li's research primarily centers on program analysis techniques, particularly static analysis methods for software security and reliability. His group developed Wukong, a static analysis and detection system capable of identifying deep security vulnerabilities across functions, components, and complex dependencies in C/C++, Java, and Android applications. This tool has discovered thousands of errors in popular open-source software including Google Chromium, Bash, sed, and Hadoop, with hundreds confirmed by developers and over 50 CVEs assigned. His publication record shows a strong focus on program analysis, particularly context-sensitive pointer analysis, taint analysis, and vulnerability detection. His recent work (2021-2024) demonstrates continued innovation in context-free language reachability, efficient IFDS algorithms, and specialized analysis for generics and authorization vulnerabilities. His research spans cybersecurity, programming languages, and software engineering domains, with emphasis on practical applications for real-world software systems. ASE 2019 Distinguished Paper Award CCS 2022 Best Paper Honorable Mention Lian Li has guided numerous PhD and Master's students in computer system architecture and software theory. His research group maintains active collaborations across various software analysis domains, with funding supporting their work on tools like Wukong. They have developed significant intellectual property including multiple patents related to program analysis techniques. The program analysis research group he leads focuses on developing practical tools for software reliability and security. Their work bridges theoretical program analysis with real-world applications, particularly through the Wukong analysis system which has been successfully applied to major open-source projects.
Dr. Donald Ekong serves as Associate Professor of Computer Engineering at Mercer University's School of Engineering in Macon, GA. A registered Professional Engineer in Georgia and Saskatchewan with Senior Member status in IEEE, he joined Mercer in 2002 after industry roles at Ciena Corp, Motorola, and Valmet Automation. His expertise spans computer architecture, networks, security, and mobile systems development. His educational credentials include: PhD in Electrical Engineering, University of Saskatchewan (1997) MS in Electrical Engineering, University of Saskatchewan (1991) BE in Electrical Engineering, University of Port Harcourt (1986) Research centers on mobile technology applications for healthcare improvement in vulnerable populations and STEM education initiatives. Key projects involve diabetic emergency response systems, telepathology solutions using Raspberry Pi, and interventions to increase female participation in STEM fields through mobile app development. Recent publications (2014-2024) reveal consistent focus on cybersecurity curriculum development, mobile health diagnostics, and gender-inclusive STEM education. His interdisciplinary approach bridges computer engineering with biomedical applications and educational innovation, often targeting underserved communities through service-learning frameworks. Professional recognitions include: ORAU Travel Grant (2019) NIH/NLM Visiting Scientist appointment (2016) Mentorship is demonstrated through student co-authorship in publications and conference presentations on speech impairment assistive technology and STEM career affinity research. His NSF review service and ASEE leadership (1999-2021) reflect commitment to advancing engineering education standards and research funding processes.
