Software Correctness and Reliability Professors and Supervisors

Antonio Salmerón Cerdánمشاهده پروفایل

استاد

University of AlmeríaSpain

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

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John Wickersonمشاهده پروفایل

مدرس ارشد

IMDEA Software InstituteSpain

concurrency semantics
FPGAs
GPUs
+۵ مورد دیگر

John Wickerson is a Senior Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London. His research spans formal methods, concurrency, and hardware/software synthesis. Academic Rank: Senior Lecturer Affiliation: Imperial College London His research interests include concurrency semantics, weak memory models, transactional memory, GPU and FPGA programming, and high-level synthesis for hardware accelerators. These areas intersect formal verification, programming language design, and hardware-software interface optimization. The publications of John Wickerson reflect trends in formalizing memory models, improving hardware synthesis reliability, and testing concurrency frameworks. His work addresses challenges in quantum compiler validation, GPU workgroup progress, and weak memory persistency across Intel, ARM, and C++ architectures. He actively contributes to academic communities as a Publicity Co-Chair and Session Chair in conferences like POPL and as a Committee Member in SPLASH and PLDI. His GitHub repository activity and X (Twitter) presence further demonstrate his engagement in technical dissemination.

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Danni Novakovicمشاهده پروفایل

مدرس ارشد

Anglia Ruskin UniversityUnited Kingdom

Dr. Danni Novakovic serves as a part-time Senior Lecturer Practitioner at Anglia Ruskin University within the Faculty of Science and Engineering, specializing in Computing and Information Science. She joined ARU in 2019 after holding leadership roles including Head of School of Computer Science at the University of Bedfordshire and Head of Computing at the University of West London, with an academic career spanning since 1994. Her educational background comprises: PhD in Mathematics PgCert in Teaching and Learning in Higher Education BSc in Computer Science and Mathematics Research focuses on cryptography-combinatorics intersections for security applications, formal methods, and computational theory. Recent expansions include computer music generation via genetic algorithms and self-configuring security frameworks for mobile devices, reflecting an evolution from theoretical mathematics to applied interdisciplinary security solutions. Publication trends demonstrate foundational work in permutation mathematics (1995-2000), system dynamics for institutional management (2009-2010), and contemporary security applications including EEG biometrics and smartphone anomaly detection (2014-2018), showcasing consistent contributions to cryptographic theory and practical security engineering. Professional recognition includes: Fellow of the British Computer Society Fellow of Higher Education Academy She actively supervises postgraduate research across mathematics, cryptography, and cybersecurity domains while contributing to academic leadership through program committee roles at major conferences including IEEE CIT and WEBIST, alongside extensive curriculum development and quality assurance experience.

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Ranjit Jhalaمشاهده پروفایل

استاد

Southern Illinois University EdwardsvilleUnited States

Ranjit Jhala is a Professor of Computer Science Engineering at UC San Diego's Jacobs School of Engineering, where he leads the Programming Systems Group. His research spans Programming Languages and Software Engineering, focusing on building reliable systems through Type Systems, Model Checking, Program Analysis, and Automated Deduction. His current projects include Flux for Rust verification, Liquid Haskell refinement types, and techniques for analyzing timing channels. Professor Jhala teaches courses on Programming Languages (CSE 130) and Graduate Programming Languages (CSE 230), with extensive experience teaching compilers and verification topics. Professor Jhala advises several students including Alexander Bakst, Ben Cosman, and Marc Andrysco. Notable former students include Niki Vazou (Postdoc at Maryland), Ravi Chugh (University of Chicago), and Patrick Rondon (Google).

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Shing-Chi Cheungمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

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.

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Yintong Huoمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Yintong Huo is an Assistant Professor at the School of Computing & Information Systems, Singapore Management University (SMU), where he leads research in intelligent software engineering. He received his PhD from The Chinese University of Hong Kong (CUHK) in 2024 under Prof. Michael R. Lyu and holds a Bachelor's degree from the University of Electronic Science and Technology of China. His research focuses on empowering AI models (particularly LLMs) for software development, testing, and operations, with two flagship projects: LogPAI (open-source AI platform for automated log analysis) and WebPAI (multimodal intelligence for automatic webpage development). His work spans log analysis, code intelligence, UI generation from prototypes, and configuration diagnostics. Huo's publication record shows strong trends in leveraging multimodal LLMs for practical software engineering challenges, with recent work on interactive webpage generation (Interaction2Code), configuration logging (ConfLogger), and log parsing (LILAC). His research bridges theoretical AI advancements with real-world system reliability needs. ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022) ACM SIGSOFT CAPS Travel Grants National Scholarship (2019) Huo actively supervises PhD students (including Shi Ying Chang and Dan Huang) and research engineers. His lab has secured funding for multiple projects including WebPAI and LogPAI. He serves on program committees for major conferences (ASE, ICSE, FSE) and reviews for top journals. Current projects include dynamic webpage generation and configuration diagnostics, with ongoing work on small language models for logging systems. Huo leads the LogPAI and WebPAI research groups, developing open-source tools for automated log analysis and multimodal UI code generation. The LogPAI project has garnered over 3,000 GitHub stars and 70,000 downloads. His team collaborates with industry partners on AIOps challenges and is expanding into configuration diagnostics through the ConfLogger project.

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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.

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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.

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