Concurrent program verification Professors and Supervisors in Germany

Achiya Elyasafمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Achiya Elyasaf is an Assistant Professor at the Software and Information Systems Engineering Department of Ben-Gurion University of the Negev, Israel. He received all his academic degrees in computer science from Ben-Gurion University and completed postdoctoral research in David Harel's group at the Weizmann Institute. In 2021, he co-founded Provengo Technologies, which develops software-testing solutions leveraging his expertise in software engineering and artificial intelligence. Dr. Elyasaf's research focuses on creating synergies between software engineering and artificial intelligence. He pioneered Context-Oriented Behavioral Programming, a paradigm for developing context-aware systems through natural and incremental behavior specification. His work on utilizing expert knowledge for evolving solvers to combinatorial games has been recognized as one of the most important achievements in AI gaming, mentioned alongside Deep Blue's victory over Kasparov and Watson's Jeopardy! win in Nick Bostrom's 2014 book 'Superintelligence: Paths, Dangers, Strategies'. His recent publications demonstrate strong activity in model-based testing, combinatorial sequence testing, and behavior composition. The articles reveal consistent focus on applying AI techniques to software engineering challenges, particularly in testing and verification domains, with practical implementations like the Provengo tool suite and EC-KitY framework. Dr. Elyasaf has received significant recognition for his work, including three HUMIE Awards from the Genetic and Evolutionary Computation community (Gold in 2013 and 2011, Bronze in 2009). He is actively recruiting MSc students, PhD students, and post-docs with funded positions, indicating an expanding research program at Ben-Gurion University. His GitHub profile shows active development of EC-KitY, a scikit-learn-compatible Python toolkit for evolutionary computation.

پروفایل رسمی دانشگاه

Bernd Fischerمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Bernd Fischer is a Professor and the current Head of Division in the Division of Computer Science at Stellenbosch University, South Africa. Previously, he held positions at TU Braunschweig, NASA Ames Research Center, and University of Southampton, establishing a strong international academic background in software engineering and formal methods. Professor Fischer's research focuses on automated software engineering, particularly logic-based techniques. His work spans specification-based component reuse, program synthesis, and program verification, with current emphasis on annotation inference, software model checking, and human-oriented presentation of verification results. His research bridges theoretical foundations with practical applications, particularly in concurrent program verification, grammar-based testing, and fault localization techniques. His work has significant implications for improving software reliability and developer productivity. Analysis of his recent publications reveals a strong focus on concurrent program verification through lazy sequentialization techniques, with substantial contributions to tools like CSeq and ESBMC. His research demonstrates consistent innovation in software verification, particularly in addressing the challenges of concurrency, bounded model checking, and fault localization. The interdisciplinary nature of his work connects theoretical computer science with practical software engineering challenges. ASE 2012 Most Influential Paper Award ACM Distinguished Paper Award Best Presentation Award Professor Fischer has successfully advised PhD students including Gillian Greene, who defended her thesis on "Concept-Based Exploration of Rich Semi-Structured Data Collections," and Geoff Birch, who completed work on "Fast, Fully-Automated, Model-Based Fault Localisation and Repair with Test Suites as Specification." His mentoring approach integrates theoretical rigor with practical tool development. His research has been supported through various academic grants enabling the development of multiple software verification tools. Professor Fischer leads development of several important software engineering tools including AutoBayes for statistical program synthesis, ConceptCloud for interactive visualization of software repositories, CSeq for concurrent program verification, and ESBMC for software model checking. These tools represent significant contributions to the software engineering research community and have been recognized in international verification competitions.

پروفایل رسمی دانشگاه

Xiang Gaoمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Xiang Gao is a Pre-tenure Associate Professor in the School of Software at Beihang University, China. His research focuses on applying program analysis, test generation, and formal methods to improve software quality through automated bug fixing and program synthesis. He has established significant collaborations with Fujitsu Laboratories of America, Microsoft Research, and other leading institutions in the software engineering field, demonstrating strong industry-academia connections. Dr. Gao received his Bachelor's degree in Computer Science (Elite Class) from Shandong University in 2016, followed by a Ph.D. from the School of Computing at the National University of Singapore, where he also served as a Postdoctoral Fellow until December 2021. His educational background spans both Chinese and Singaporean academic institutions, providing him with a global perspective on software engineering research. His primary research interests span multiple cutting-edge areas of software engineering: Program Analysis techniques for detecting and fixing software bugs with formal methods Software Security vulnerabilities with focus on automated repair methods Automated Program Repair systems that generate high-quality patches without overfitting Program Synthesis for creating transformation rules from examples Software Engineering for Artificial Intelligence (SE4AI) to improve AI model reliability and security Mobile Software Engineering with particular attention to UI testing and automation Deep Learning Security including model protection and obfuscation techniques Dr. Gao's recent publication trajectory shows a strategic evolution toward integrating large language models with traditional software engineering approaches, particularly in test generation and program repair. His work on DNN modularization (NeMo, CNNSpliter, SeaM) represents an innovative approach to enhancing model reusability and security in resource-constrained mobile environments, addressing critical challenges in deploying AI on edge devices. His scientific contributions have been recognized with multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award for "ProveNFix: Temporal Property guided Program Repair" at FSE'24 IEEE TCSE Distinguished Paper Award for "Investigating and Detecting Silent Bugs in PyTorch Programs" at SANER'24 ACM SIGSOFT Distinguished Paper Award for "Modularizing while Training: A New Paradigm for Modularizing DNN Models" at ICSE'24 Distinguished Artifact Award for "Automated Patch Backporting in Linux (Experience Paper)" at ISSTA'21 Dr. Gao actively mentors students at various levels, seeking "self-motivated Ph.D, master, undergraduate students and interns with strong programming skills" for his research projects. He serves on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and FSE, demonstrating his growing influence in the academic community. His research has been supported through collaborations with industry partners including Microsoft Research and Fujitsu Laboratories of America, translating theoretical advances into practical applications. His laboratory focuses on several key research projects including Automated Software Vulnerability Repair (with techniques like Fix2Fit, VulnFix, and ExtractFix that address the overfitting problem in program repair), Program Synthesis for Program Transformation (including Semi-supervised synthesis and FixMorph for automated patch backporting in Linux), and Software Engineering for Artificial Intelligence (with projects like CNNSpliter, SeaM, and Sensei that apply software engineering principles to improve AI model usability and robustness). These projects represent cutting-edge work at the intersection of traditional software engineering and modern AI techniques, addressing critical challenges in software reliability and security.

پروفایل رسمی دانشگاه

Hung Hoang Viet Nguyenمشاهده پروفایل

پژوهشگر

German Institute for Adult Education – Leibniz Centre for Lifelong LearningGermany

Hung Hoang Viet Nguyen is a Researcher at the Deutsches Institut für Erwachsenenbildung (DIE) in the department of Organization and program planning , where he has worked since 2024. Concurrently, he is completing his Doctorate in Political Science at the Bremen International Graduate School of Social Sciences (2019–2025). Education: Doctorate of Philosophy in Political Science, Bremen International Graduate School of Social Sciences (2019–2025) Master of Arts/Social Sciences in European Studies, University of Flensburg/University of Southern Denmark (2016–2018, double degree) Bachelor in International Relations, Ho Chi Minh City University of Social Sciences and Humanities (2011–2015) Research Focus: Nguyen specializes in computational approaches to political science, including text analysis, NLP, and machine learning. His work examines party systems, welfare policies, and methodological innovations in social science research. Key domains include: Quantitative analysis of political representation and social policies Machine learning applications for party positioning Cross-national studies of pandemic policy feedback Reproducibility frameworks for social sciences Publications: His recent articles demonstrate strong emphasis on computational reproducibility, political heuristics, and pandemic policy analysis, frequently employing advanced statistical modeling and cross-national datasets.

پروفایل رسمی دانشگاه

Danel Ahmanمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Danel Ahman is an Associate Professor of Programming Languages at the Institute of Computer Science, University of Tartu, Estonia. He is a member of the Chair of Programming Languages and the Laboratory for Software Science (SWS). His academic journey includes postdoctoral positions at the University of Ljubljana and Inria Paris, as well as research internships at Microsoft Research. Education: PhD in Theoretical Computer Science, University of Edinburgh (2012-2017), thesis: "Fibred Computational Effects" MPhil in Advanced Computer Science, University of Cambridge (2011-2012), thesis: "Computational effects, algebraic theories and normalization by evaluation" BSc in Informatics, Tallinn University of Technology (2007-2010), Cum Laude Danel Ahman's research focuses on programming languages, their design and semantics. He is particularly interested in dependent and refinement types, computational effects (especially algebraic effects, effect handlers, and runners), modal types for verifying temporal resource usage, and category-theoretic denotational and operational semantics. His work bridges theoretical foundations with practical applications in verified software development, exploring how programming language features can ensure correctness while maintaining expressiveness and efficiency. His recent publications demonstrate a consistent focus on computational effects, modal types, and verified programming. The trajectory shows increasing sophistication in handling temporal resources and asynchronous effects, with several papers published in top programming languages conferences (POPL, ICFP) and journals. His work often combines theoretical category-theoretic foundations with practical implementations, particularly using the F* verification language. Scientific Awards: 2025: Estonian Research Council grant on "Explainable Verification" (co-investigator) 2024: EuroProofNet COST Action Short-Term Scientific Missions to University of Ljubljana 2019: Horizon 2020 Marie Skłodowska-Curie Individual Fellowship 2018: 1st prize at the Estonian Ministry of Education and Research dissertations competition 2012: Google prize and Citrix prize for best research dissertation at University of Cambridge Ahman actively supervises graduate students and has served on numerous program committees for programming languages conferences. His grant portfolio includes significant funding for research on programming language foundations and verification. He collaborates extensively with researchers across Europe and the US, particularly in the F* verification ecosystem and effect handler communities. He is a key member of the Laboratory for Software Science at the University of Tartu, contributing to research on programming language theory and verified software development. His work intersects with several research groups focusing on formal methods, programming language design, and software verification.

پروفایل رسمی دانشگاه

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

Gary Tanمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.

پروفایل رسمی دانشگاه

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

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

Ajitha Rajanمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

پروفایل رسمی دانشگاه

Zhenbang Chenمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Zhenbang Chen is a Professor in the College of Computer at National University of Defense Technology (NUDT), China. His academic career spans over a decade with significant contributions to software engineering, particularly in program analysis and formal methods. He has served on program committees for major conferences including ASE, ICSE, and FSE, and has been actively involved in research that bridges theoretical formal methods with practical software engineering applications. Dr. Chen received his Ph.D. and Bachelor degrees in computer science from National University of Defense Technology (NUDT) in June 2009 and July 2002, respectively. His educational background from NUDT has provided a strong foundation for his research in software engineering and formal methods. Ph.D. in Computer Science, National University of Defense Technology (NUDT), 2009 Bachelor's Degree in Computer Science, National University of Defense Technology (NUDT), 2002 Zhenbang Chen's research primarily focuses on program analysis, with special emphasis on symbolic execution techniques. His work extends to formal methods and their practical applications in software engineering. He investigates constraint solving approaches to improve the efficiency of program analysis and explores program synthesis techniques to automate software development tasks. His research bridges theoretical foundations with practical software engineering challenges, particularly in the areas of software verification and testing. His recent work has increasingly focused on optimizing symbolic execution through novel constraint solving techniques and exploring multi-modal approaches to behavior tree synthesis. This demonstrates his commitment to advancing both the theoretical underpinnings and practical applications of software analysis techniques. Professor Chen's publication record shows a consistent focus on symbolic execution and constraint solving, with a clear progression toward more sophisticated optimization techniques. His recent work demonstrates a shift toward multi-objective optimization for floating-point constraints and multi-modal approaches to program synthesis. The research spans both theoretical foundations and practical implementations, with several tools developed from his research participating in international competitions. Dr. Chen's research excellence has been recognized through multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award for FSE 2025 paper "QSF: Multi-Objective Optimization based Efficient Solving for Floating-Point Constraints" ACM SIGSOFT Distinguished Paper Award for ISSTA 2021 paper "Type and interval aware array constraint solving for symbolic execution" ACM SIGSOFT Distinguished Paper Award for ICSE 2018 paper "Towards optimal concolic testing" Bronze Medal (3rd place) in Cover-Branches category at Test-COMP 2025 for the FDSE tool Professor Chen is actively involved in mentoring the next generation of researchers, currently seeking Ph.D. and M.Sc. students to work with him on cutting-edge research in program analysis and formal methods. His research group has developed several tools that have gained recognition in international competitions, including AISE which ranked 1st in SV-COMP 2025's ReachSafety-Loops category and FDSE which won Bronze Medal in Test-COMP 2025. His research has been supported by grants that enable participation in major international conferences and competitions, fostering collaborations with researchers worldwide. Dr. Chen leads a research group focused on program analysis and formal methods at NUDT. His team has developed several notable tools including AISE for program verification and FDSE for software testing, which have achieved top rankings in international competitions like SV-COMP and Test-COMP. The research group maintains active collaborations with other institutions and participates regularly in major software engineering conferences, contributing to both theoretical advancements and practical tool development in the field.

پروفایل رسمی دانشگاه