Find Academic Supervisors

Omar I. Al-BatainehView profile

Researcher

• Automated Program Repair
• Formal Methods
• Software Engineering
• +3 more

Omar I. Al-Bataineh is a Research Scientist at Gran Sasso Science Institute (GSSI) in Italy, specializing in software engineering and formal methods. His work bridges theoretical foundations with practical applications in automated program repair and software verification. Education: Ph.D. in Computer Science, University of Western Australia Additional degrees from University of New South Wales and Jordan University of Science and Technology His research centers on three interconnected themes: (1) Multi-fault Automated Program Repair addressing complex bug interactions, (2) Formal Methods for Reliable Repair ensuring provable correctness, and (3) Termination-Aware Repair integrating performance considerations. He develops lightweight test oracles and context-sensitive repair techniques to overcome patch overfitting and scalability limitations in real-world systems. Recent publications reveal strong focus on multi-fault scenarios (60% of 2025 output), with growing emphasis on formal verification (30%) and performance-aware repair (10%). Key venues include ASE, ICSME, and SANER where he explores program slicing, oracle design, and fault interaction analysis. Awards: Best Paper Award at QRS 2022 for advancing automated program repair capabilities Prior to GSSI, he held research positions at Simula Research Laboratory, National University of Singapore, and Nanyang Technological University. His teaching experience includes Advanced Computer Security at UNSW and Java Programming at UWA, though current academic instruction isn't emphasized in recent activities. He maintains active contributions to workshops like APR@ICSE and FASE, focusing on practical tool development.

• Software Engineering
• Computer Security
• Machine Learning
• +5 more

Yun Lin is a researcher affiliated with Shanghai Jiao Tong University, with a former position at the National University of Singapore's School of Computing. Their work focuses on software engineering, computer security, and machine learning. Key research interests include formal methods for program verification, adversarial machine learning, phishing detection, and deep learning applications in security. Lin has collaborated extensively with institutions like NUS and the Institute of High Performance Computing, producing over 70 publications since 2014 across venues such as ICSE, USENIX Security, and NeurIPS. Research interests span security (e.g., phishing detection, smart contract vulnerabilities), program analysis (bug localization, delta debugging), and AI-driven software development (code editing recommendations, LLM integration). Their contributions address challenges in software reliability, adversarial robustness, and scalable security solutions. Prominent publications include works on automated program refinement with LLMs, phishing webpage identification via deep vision models, and conflict resolution in version control systems. Lin’s collaborations emphasize interdisciplinary approaches blending formal methods with machine learning. No awards or grants are explicitly listed in the provided text.

Dominic PetrakView profile

Researcher

• Natural Language Processing
• Conversational AI
• Machine Learning
• +4 more

Dominic Petrak is a Doctoral Researcher at the UKP Lab, Technical University of Darmstadt. His research focuses on conversational AI, particularly improving dialogue systems through implicit user feedback analysis and error prevention in open-world scenarios. He holds an M.Sc. in Computer Science from RheinMain University of Applied Sciences and has professional experience in software engineering with firms like Sopra Steria and DXC Technology, specializing in NLP, enterprise applications, and Jakarta EE. Research Interests: Natural Language Processing, Machine Learning, Conversational AI, Numeracy in Language Models, Dialogue Systems Design, Software Architecture, and Enterprise Application Development. Publications: His work spans dialogue systems (e.g., FEDI dataset), numeracy enhancement in LMs (Arithmetic-Based Pretraining), and citizen science for NLP. Recent contributions include analyzing social acceptance requirements for service robots and exploring free-text human feedback integration. Advisors & Labs: Supervised by Prof. Nafise Sadat Moosavi (University of Sheffield) and Prof. Iryna Gurevych. Affiliated with the UKP Lab, TU Darmstadt.

• Software Engineering
• Software Defect Prediction
• Machine Learning for Software Engineering
• +7 more

Akito Monden is a prolific Japanese software-engineering scholar with 171 publications recorded in dblp during 1995-2025. His recent work centres on defect prediction, online learning, bandit-based tool selection, and empirical studies of software quality and human factors. Research interests span software defect prediction, mining software repositories, effort estimation, clone detection, code generation, and the application of machine-learning techniques (notably bandit algorithms and ensemble methods) to practical software-engineering tasks. He also investigates requirements ambiguity, security-bug identification with large language models, and gaze-based human-computer interaction in programming education. Across 2022-25 articles Monden explores online learning to cope with concept drift in defect datasets, multi-armed bandit algorithms for dynamic selection of clone detectors, fault-localisation techniques and code generators, and LLM-based security-bug detection. These themes reflect a sustained focus on data-driven, adaptive approaches that improve software quality assurance processes.

• Search Based Software Engineering
• Software Testing
• Evolutionary Computation
• +7 more

Giovani Guizzo is a Software Engineer at Kii working as a Blockchain Engineer and Front-end developer with React, while maintaining an active research profile in Search-Based Software Engineering. He earned his PhD in Computer Science from the Department of Informatics at Federal University of Paraná (UFPR) in Brazil under Professor Silvia Regina Vergilio in 2018. His academic collaborations include significant affiliations with University College London where he has published as a researcher. His research program centers on applying evolutionary computation and search-based techniques to software engineering challenges, particularly in software testing and optimization. Guizzo's work bridges theoretical advances in multi-objective optimization with practical software engineering applications, resulting in innovative solutions for test model inference, mutant reduction strategies, and automated program repair. His approach often combines natural language processing with finite state machine generation to transform bug reports into actionable test models. Guizzo's publication record reveals a consistent trajectory of increasing impact in top-tier software engineering venues. His recent work demonstrates sophisticated integration of multi-objective evolutionary algorithms with practical software testing challenges, particularly in the areas of model inference from natural language bug reports and optimization of mutation testing processes. The evolution of his research shows progression from foundational work on design patterns in evolutionary algorithms toward more complex applications in automated software testing and repair. His achievements include: SSBSE 2021 Challenge Winner for innovative work on fitness functions in automated program repair Distinguished Artifact Award at ICSE 2021 recognizing reproducibility in genetic improvement research Microsoft Azure Research Award in 2017 supporting his work in cloud-based software engineering research Guizzo maintains active engagement with the research community through editorial roles for IET Software and Science of Computer Programming, membership on the SSBSE Steering Committee, and service on program committees for major conferences including ESEC/FSE, ASE, GECCO, and ICSE. His academic visits to University College London (3 months in 2017) and Queen Mary University (1 month in 2017) established lasting research collaborations with Dr. Jens Krinke, Dr. Federica Sarro, and Dr. John Drake that continue to produce high-impact publications. Through his interdisciplinary approach combining software engineering, evolutionary computation, and natural language processing, Guizzo has established himself as a significant contributor to the search-based software engineering community, with research that consistently addresses practical challenges while advancing theoretical foundations.

Lin ChenView profile

Associate Professor

• Software Engineering
• Programming Languages
• Artificial Intelligence for Software Engineering
• +4 more

Lin Chen is an Associate Professor in the Department of Computer Science and Technology at Nanjing University, China, specializing in software engineering and programming languages with applications to AI-integrated systems. His work bridges theoretical foundations and practical tools for software analysis, testing, and ecosystem studies. Education: Ph.D. in Computer Software and Theory, Southeast University (2009) B.S. in Computer Science and Technology, Southeast University (2001) Visiting Scholar at Purdue University (2015-2016) His research spans software testing, programming language design (particularly gradual typing), and AI-enhanced software engineering. Key contributions include empirical studies of Python's dynamic features, mutation testing frameworks for AI systems, and defect prediction models. He investigates how programming language semantics impact software quality and maintenance in open-source ecosystems. Recent publications (2023-2024) reveal strong trends in applying software engineering techniques to AI systems, analyzing Python's typing evolution, and developing practical testing tools. There is significant emphasis on empirical validation, with 60% of recent work focusing on Python ecosystem analysis and 30% on AI/software integration. Scientific Awards: FSE 2016 Distinguished Artifact Award First Prize of Hubei Science and Technology Award (2015) First Prize of Jiangsu Science and Technology Award (2012) First Prize of Jiangsu Science and Technology Award (2007) Professor Chen has advised 14+ graduate students including PhD candidates Hao Ren and Wanwangying Ma, and master's students like Fan Yang and Yuanlei Han. He actively recruits self-motivated PhD and undergraduate researchers for projects in software analysis, testing, and intelligent engineering. His group collaborates with industry partners on tool development for defect prediction and type system analysis. His research team at Nanjing University focuses on four pillars: (1) Software Analysis and Testing for dynamic languages, (2) Technical Debt and Refactoring in evolving systems, (3) AI-driven defect prediction, and (4) Gradual typing semantics for multilingual ecosystems. Current projects include large-model-based test generation and knowledge graph applications for QA system validation.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Programming Languages
• Program Reduction
• +5 more

Chengnian Sun is an Associate Professor in Software Engineering and Programming Languages at the University of Waterloo, Canada. His research spans program reduction, software testing, and compiler-related technologies, with a focus on developing practical tools for debugging and testing. Dr. Sun's research interests center on Software Engineering and Programming Languages , particularly in program reduction techniques, compiler testing, and software security. His work bridges theoretical foundations with practical applications, developing frameworks like Perses and Latra that have become influential in the software engineering community. His research demonstrates a consistent evolution from basic program reduction techniques to incorporating modern approaches like LLMs for compiler testing. His recent publications show a strong trend toward language-agnostic transformation frameworks and practical debugging tools , with increasing emphasis on security applications and integration of AI techniques. The research spans both theoretical foundations and practical implementations, with several tools developed becoming widely used in the software engineering community. Dr. Sun has served on program committees for major software engineering conferences including ASE, ICSE, ESEC/FSE, and ISSTA, demonstrating his standing in the research community. His extensive publication record shows consistent high-impact contributions across multiple venues, with particular emphasis on program reduction and compiler testing techniques. His work has led to the development of several influential tools including Perses (syntax-guided program reduction), Latra (template-based transformation framework), and AddressWatcher (memory leak localization). These tools have been widely adopted in both academic and industrial settings for debugging and testing purposes.

Tao ZhangView profile

Professor

• AI for Software Engineering
• Software Security
• Mining Software Repositories

Dr. Tao Zhang is a Full Professor at the School of Computer Science and Engineering, Macau University of Science and Technology (MUST), Macau SAR. He serves as an Associate Editor for IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Reliability (TRel), and the Journal of Systems and Software (JSS), and is an Editorial Board Member for Empirical Software Engineering (EMSE) and Science of Computer Programming (SCP). His educational background includes: Ph.D. in Computer Science from the University of Seoul B.S. in Automation and M.Eng in Software Engineering from Northeastern University, China Postdoctoral Research Fellow at Hong Kong Polytechnic University Dr. Zhang's research primarily focuses on three interconnected areas that represent the cutting edge of modern software engineering: AI for Software Engineering : Utilizing neural language models and large language models to create automated software engineering tools that help developers produce high-quality software. His work includes evaluating whether pretrained language models truly understand software engineering tasks and developing universal representations for bug reports. Software Security : Employing static analysis, AI technologies, and formal methods to detect malware, vulnerabilities, and privacy leaks in mobile apps and smart contracts. His research spans Android malware detection, smart contract vulnerability analysis, and state manipulation attacks in blockchain systems. Mining Software Repositories : Applying information retrieval and machine learning to extract meaningful insights from software artifacts to improve development efficiency. This includes work on app review analysis, change request localization, and code similarity metrics. His publications demonstrate significant impact across the software engineering community, with over 100 high-quality papers in top venues including ICSE, ESEC/FSE, ASE, TSE, TOSEM, EMSE, JSS, TIFS, and TDSC. Dr. Zhang has received numerous honors and recognitions: Distinguished Member, China Computer Federation (CCF), September 2025 Top Reviewer Award 2023, Journal of Systems and Software (JSS), April 2024 Distinguished Reviewer in 2023, ACM Transactions on Software Engineering and Methodology (TOSEM), February 2024 Senior Member of ACM (October 2020) and IEEE (February 2020) Best Paper Award, 16th Korea Conference on Software Engineering (KCSE), February 2014 As an academic leader, Dr. Zhang serves/served as General or Program Chair for numerous conferences including APSEC 2025, Internetware 2024, SANER 2023, and DSA 2021. He mentors a vibrant research group with multiple postdocs, PhD students, and master's students working on innovative projects in intelligent software engineering and security. His lab actively recruits highly motivated students interested in Data Mining, Artificial Intelligence, Software Security, and Software Engineering. Dr. Zhang leads the "Intelligent Software Data Analysis and Software Security" research team at MUST, which focuses on leveraging AI technologies to solve critical challenges in software development and security. The team maintains strong collaborations with international researchers and regularly publishes in top-tier venues.

Feifei NiuView profile

Research Fellow

• software quality assurance
• software testing
• requirements engineering

Feifei Niu is a Research Fellow at the University of Ottawa, Canada, specializing in software engineering research. She earned her Ph.D. from Nanjing University in 2023 and actively contributes to top-tier conferences including ASE, ICSE, and Mining Software Repositories. Education Ph.D., Nanjing University (2023) Her research centers on software quality assurance, testing methodologies, and requirements engineering, with recent emphasis on large language model applications for bug tracking and dataset curation. She develops refactoring-aware tools and conducts empirical studies on bug localization techniques. Publications from 2023-2025 reveal a strong focus on empirical validation of software engineering practices, particularly replication studies in bug localization and LLM-driven solutions for software maintenance challenges. Key themes include traceability modeling, dataset quality assessment, and tool development for code history analysis. She serves on program committees for ASE 2025 (Research Papers and Industry Showcase tracks) and ICSE 2026, and has chaired sessions at FORGE 2024-2025. Her GitHub profile shows 17 repositories covering replication studies, IoT protocols, and traceability models.

Reiner HähnleView profile

Professor

• Formal Verification
• Automated Deduction
• Software Product Lines
• +2 more

Reiner Hähnle is Professor in Software Engineering at TU Darmstadt, specializing in formal methods and verification technologies. His research develops automated approaches to software reliability through formal verification, abstract interpretation, and symbolic execution techniques. Key research areas include: Automated theorem proving for software correctness Verification of software product lines and system families Symbolic execution debuggers and analysis tools Resource-aware verification methods Hähnle contributes to foundational and applied aspects of formal software verification, with recent work focusing on efficient bug detection and product-line verification.

Marlena HerbertzView profile

Researcher

• Molecular Evolutionary Biology
• Biochemistry
• Insect Physiology
• +3 more

Dr. Marlena Herbertz is a postdoctoral researcher at the University of Hamburg, affiliated with the Department of Biology under the Faculty of Mathematics, Computer Science and Natural Sciences. She works in the Molecular Evolutionary Biology group led by Prof. Dr. Susanne Dobler at the Institute of Animal Cell and Systems Biology. PhD in Molecular Evolutionary Biology (University of Hamburg, 2017-2021) MSc in Biodiversity, Evolution, Ecology (University of Göttingen, 2014-2016) BSc in Biology (Free University of Berlin, 2011-2014) Her research focuses on molecular adaptations in insects, particularly the functional roles of Na,K-ATPase β-subunits in the cardenolide-resistant milkweed bug Oncopeltus fasciatus . Current work investigates tissue-specific α-β subunit combinations and their adaptive significance. Key publication trends include molecular evolution, insect physiology, and biochemical adaptations. Her 2022-2023 studies examine Na,K-ATPase function in tracheal development and nervous tissue localization. She has presented her work at international conferences including SIP 2024, ISCE 2021, and European Proteomics Association meetings. Contact: marlena.herbertz@uni-hamburg.de | Office: Room 312, Martin Luther King Jr. Place 3, Hamburg.

Frank EichingerView profile

Researcher

• Graph Mining
• Software Defect Localization
• Data Mining
• +4 more

Dr. Frank Eichinger is a researcher at the Institute of Program Structures and Data Organization (IPD) within the Faculty of Computer Science at Karlsruhe Institute of Technology (KIT), Germany. He works under the Chair of Prof. Klemens Böhm, focusing on advanced data mining applications in software engineering and energy systems, with significant contributions to graph-based analysis techniques. Education: PhD in Computer Science, Karlsruhe Institute of Technology (KIT), 2011 Research Interests: Eichinger's work centers on graph mining for software defect localization , particularly through call-graph analysis in multicore and multithreaded environments. His research extends to smart grid data compression and energy domain analytics , combining entropy-based methods with scalable algorithms. Key innovations include hierarchical mining of dynamic call graphs and weight-constrained subgraph analysis for improved fault detection in complex systems. Publication Trends: His 15 most recent publications (2008-2015) reveal a cohesive trajectory from foundational graph-mining algorithms to real-world energy applications. Early work focused on software bug localization via call graphs, evolving toward time-series compression for smart grids. The research demonstrates consistent methodological rigor in adapting graph mining to domain-specific challenges, with strong representation in top venues like VLDB Journal and ECML PKDD. Scientific Awards: No awards documented in source material Advising and Grants: While specific student supervision details are absent, his dissertation role and collaborative publications indicate mentorship activities. Project involvement in Graph Mining and LogoTakt suggests grant-funded research, though specific funding sources aren't disclosed. Labs and Teams: Eichinger operates within KIT's Information Management Systems research group (IPD Böhm), actively contributing to the Graph Mining project and LogoTakt initiative alongside collaborators J. Mülle and M. Bracht. His work integrates closely with KIT's energy informatics and software engineering research ecosystems.

Azalea RaadView profile

Associate Professor

• Programming Languages
• Formal Verification
• Weak Memory Models
• +4 more

Dr. Azalea Raad is an Associate Professor (Reader) in the Department of Computing at Imperial College London, leading the Veritas Lab. She is also co-director of the UK Research Institute on Verified, Trustworthy Software Systems (VeTSS). Her research focuses on programming languages, formal verification, and weak memory models, with contributions to persistent memory technology and concurrency. Raad has held visiting roles at Facebook and Bloomberg, and has been recognized with awards such as the UKRI Future Leaders Fellowship and the SIGPLAN Distinguished Paper Award. Her work bridges theoretical foundations with practical tools for ensuring software correctness. Education & Roles: PhD (Imperial College London, 2016) Reader/Associate Professor (Imperial College London, 2021–present) Co-Director, UK VeTSS Institute (2023–present) Research Interests: Her research explores formal methods for persistent programming, including non-volatile memory semantics, weak memory models, and concurrency. She develops tools like Incorrectness Separation Logic and frameworks for verifying binaries (e.g., IsaBIL). Her work emphasizes practical applications in industrial settings, such as Intel PMDK transactions and RDMA robustness. Grants & Awards: EPSRC Research Grant: Safe and Secure Remote Direct Memory Access (2023) UKRI Future Leaders Fellowship (2021) Meta Research Gift (2022) Imperial College President’s Award (2021) Professional Activities: Program Committee Member: ESOP (2024), CAV (2022), ICFP (2022) Keynote Speaker: MFPS (2022), PLDI (2021) Labs & Teams: Leads the Veritas Lab, focusing on verified software systems, and collaborates with industry partners like Intel and Bloomberg through applied research initiatives.

Michael UelschenView profile

Professor

• Software Engineering
• Embedded Systems
• Multicore/Manycore Computing
• +5 more

Prof. Dr.-Ing. Michael Uelschen is a Professor of Software Engineering for Technical Systems at Osnabrück University of Applied Sciences, part of the Faculty of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and has over a decade of industry experience in automotive software development. His research focuses on energy-efficient embedded systems, model-driven software engineering, multicore systems, and medical technology applications such as motion analysis for rehabilitation. He teaches courses in software engineering, embedded systems, and parallel algorithms at both bachelor's and master's levels. Research Interests : Software Engineering for Automotive Embedded Systems, Power Optimization, Energy-Aware Design Patterns, Medical Motion Analysis (gait stability), IoT Sustainability, and Process Improvement (CMMI). His work often bridges theoretical models with practical implementation in real-time systems. Key Projects : Developed a contactless monitoring system for elderly/patient care using commodity electronics, supported by Bayer Healthcare Grants4Apps. Also contributed to frameworks for energy-efficient embedded systems design patterns and algorithms for kinematic analysis in clinical settings. Teaching : Leads courses like 'Software Engineering for Parallel Systems', 'Embedded Systems', and 'Object-Oriented Design'. Supervises student projects and theses in automotive and medical software domains.

Jinhan KimView profile

Researcher

• Software Engineering
• Artificial Intelligence
• Testing of AI Systems
• +5 more

Jinhan Kim is a Postdoctoral Researcher at the Software Institute of USI University of Lugano, Switzerland, working in the TAU lab under the guidance of Prof. Paolo Tonella. He completed his Ph.D. in Software Engineering at KAIST, South Korea, under the supervision of Prof. Shin Yoo, where his research focused on mutation testing and the intersection of artificial intelligence and software engineering. His educational background includes: Ph.D. in Software Engineering, KAIST, South Korea (completed February 2023) Kim's research spans software engineering and artificial intelligence, with a focus on mutation testing, testing of deep learning systems, and security of AI models. He investigates techniques for improving the reliability and robustness of AI systems, particularly in safety-critical domains like autonomous driving. His work bridges traditional software engineering practices with modern AI systems, leading to novel approaches in fault localization, program repair, and adversarial testing. His recent publications reveal a strong trend toward testing and securing deep learning models in autonomous systems. He has developed taxonomies for attacks, frameworks for testing autonomous agents, and empirical studies on fault localization for neural networks. His work increasingly addresses securing AI systems against adversarial attacks and improving robustness of security detectors generated by large language models. Kim has received notable recognition including: Best Paper Award at the 18th International Workshop on Mutation Analysis (Mutation 2023) As an advisor, Kim supervises two PhD students: Masoud Jamshidiyan Tehrani and Samuele Pasini, working on security of deep learning models and robustness of security attack detectors. He actively serves the research community through program committees for ASE, ICSE, ISSTA, and ICST, and as organizer of DeepTest and SBFT workshops. His service includes being a Distinguished Reviewer for TOSEM. Kim is a core member of the TAU (Testing: Analysis and Understanding) lab at USI, which pioneers innovative approaches to software testing and analysis for modern AI-based systems.