جست‌وجوی استاد و پژوهشگر

Jingyue Liمشاهده پروفایل

استاد

• Software Engineering
• Software Security
• Software Engineering and AI
• +۲ مورد دیگر

Jingyue Li is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She earned her Ph.D. in Software Engineering from NTNU in 2006 and has extensive industrial experience including positions at IBM and DNV Research and Innovation. Her educational background includes: Ph.D. in Software Engineering from NTNU (2006) Professor Li's research spans several key areas in software engineering with a focus on both theoretical and practical applications. Her work bridges traditional software engineering practices with emerging technologies, particularly in the domain of security and blockchain systems. She has conducted extensive empirical research and applied design science methodologies to develop innovative tools and approaches. Her recent publications demonstrate a strong trend toward blockchain technologies, software security, and the intersection of AI with software engineering. The research shows increasing focus on practical applications of blockchain in decentralized autonomous organizations, consensus protocols, and security vulnerabilities in smart contracts. There's also significant work on integrating security practices into DevOps (DevSecOps) and applying machine learning techniques to software engineering problems. Professor Li has received recognition through her leadership roles in major conferences: General Chair for FSE 2025 (The ACM International Conference on Foundations of Software Engineering) Member of the EASE (International Conference on Evaluation and Assessment in Software Engineering) steering committee In terms of research leadership, Professor Li has served as Principal Investigator (PI) or Key Scientist on numerous research projects including TRACE4EU (2023-2025), PaaSforChain (2020-2023), CyberSmart (2017-2020), and several others focused on blockchain, security, and software engineering. She has also conducted research visits to institutions including University College London, University of Washington, Hiroshima University, and Peking University. Her research group appears to be actively engaged in both theoretical and applied research, with strong industry connections through projects like CyberSmart and SecureCyber that address real-world challenges in cybersecurity and smart city infrastructure.

David Lieمشاهده پروفایل

استاد

• Computer Security
• Privacy
• Program Analysis
• +۲ مورد دیگر

David Lie is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto. He holds additional appointments in the Department of Computer Science and the Faculty of Law. He is a Tier 1 Canada Research Chair in Secure and Reliable Systems, a research lead at the Schwartz Reisman Institute for Technology and Society, an Associate Director at the Data Sciences Institute, a Vector Faculty Affiliate, and a Senior Massey College Fellow. His educational background includes: BASc from the University of Toronto (1998) MS from Stanford University (2001) PhD from Stanford University (2004) David Lie's research spans computer security, privacy, and cybersecurity. He is renowned for pioneering the XOM architecture—a foundational model for modern trusted execution environments like ARM TrustZone and Intel SGX—and developing the widely adopted PScout Android permission mapping tool. His current work emphasizes program analysis, fuzzing, and symbolic execution to enhance software security and reliability, addressing critical vulnerabilities in mobile and system software. His recent publications reveal a strong trajectory in integrating machine learning with program analysis techniques. Key themes include optimizing symbolic execution for Android apps, leveraging LLVM IR for bug detection, and using predictive models to guide test generation. These contributions underscore a practical focus on scalable, automated security tools that bridge theoretical advances with real-world software vulnerabilities. His notable awards include: Best Paper Award at SOSP Tier 1 Canada Research Chair in Secure and Reliable Systems Senior Massey College Fellow No specific information on student advising or research grants was provided in the available text, though his extensive program committee service for top security conferences (OSDI, IEEE Security & Privacy, CCS, etc.) indicates significant academic leadership. David Lie actively contributes to interdisciplinary research ecosystems through his roles at the Schwartz Reisman Institute for Technology and Society (as research lead), the Data Sciences Institute (as Associate Director), and the Vector Institute for Artificial Intelligence (as Faculty Affiliate), fostering collaborations between security research, law, and societal impact studies.

Yun Linمشاهده پروفایل

دانشیار

• Automatic programming
• Software debugging
• Software testing
• +۸ مورد دیگر

Yun Lin is an Associate Professor and Deputy Head of the Department of Computer Science and Technology at Shanghai Jiao Tong University's School of Computer Science. Prior to joining SJTU, Lin served as a Research Assistant Professor at the National University of Singapore working with Prof. Dong Jin Song. Lin leads the CoPhi ("Code Philia") research group, which focuses on the intersection of Software Engineering, AI, and Security. Lin's research spans three major areas: Automatic Programming (including code editing, software testing, and debugging), Explainable AI (focusing on representation interpretation and training data attribution), and Web Misinformation (particularly phishing and scam detection). The research has resulted in numerous tools including CoEdPilot for code editing recommendation, DeepDebugger for interactive debugging of deep classifiers, and Phishpedia for phishing webpage detection. Lin's recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models and vision language models, with traditional software engineering and security tasks. The work shows increasing sophistication in understanding project context, handling interactive nature of programming tasks, and addressing security challenges in the age of generative AI. Key themes include consistency-based approaches for anomaly detection, agent-based frameworks for complex tasks, and hybrid models that combine symbolic reasoning with neural approaches. ACM Distinguished Paper Award in ICSE'18 for "Towards Optimal Concolic Testing" Distinguished Reviewer Award in FSE'25 2nd prize Research Prototype Award in ChinaSoft'24 Lin advises a large team of PhD, Master's, and undergraduate students, with several publications co-authored with students appearing in top venues. Current research is supported by collaborations with National University of Singapore, particularly with Prof. Dong Jin Song, and includes projects on code editing, GUI testing, and phishing detection. The CoPhi group maintains active development of multiple research tools and datasets. The CoPhi research group under Lin's leadership focuses on building practical tools that bridge the gap between theoretical advances and real-world programming and security challenges. The group's work spans from fundamental program analysis techniques to applied security solutions, with an increasing emphasis on leveraging AI capabilities while maintaining explainability and reliability.

Thomas Ströderمشاهده پروفایل

مدرس

• Software Development Process
• Software Architecture
• Program Verification
• +۷ مورد دیگر

Thomas Ströder is a Lecturer in Business Informatics at FHDW University of Applied Sciences since 2022, specializing in software engineering and formal methods. Previously, he served as Head of Full Stack Development at METRO (2016-2019) and Site Manager at IT-P GmbH (2020-2022), combining academic research with industrial leadership in software development organizations. His educational background includes: Diploma in Computer Science with Business Administration minor from RWTH Aachen University (2004-2010), featuring an exchange semester at UNSW Sydney (2008) funded by the Studienstiftung des Deutschen Volkes PhD in Computer Science from RWTH Aachen University (2019) focused on automatic quality assurance and software synthesis Ströder's research bridges theoretical computer science and industrial software engineering through core interests in program verification, formal methods, and software architecture. His work emphasizes practical applications of automated reasoning for termination analysis, memory safety, and complexity bounds in real-world systems, while extending to organizational development and data-driven decision processes in agile environments. This dual focus enables translation of formal verification techniques into enterprise software solutions. His 15 publications (2009-2018) reveal a consistent trajectory in program termination analysis and verification, primarily through the AProVE framework. Early work established foundations in term rewriting and logic program analysis, evolving toward memory-safe C program verification and bitvector arithmetic handling. Publications in top venues like Journal of Automated Reasoning and TACAS demonstrate specialization in bridging theoretical formal methods with practical software engineering challenges, particularly in pointer arithmetic and memory manipulation contexts. Professional activities include research collaborations with Microsoft Research Cambridge (2013) and leadership in building in-house software development organizations at METRO, reflecting strong industry-academia integration in his career trajectory.

Oliver Stecklinaمشاهده پروفایل

استاد

• Embedded Systems
• IT Security
• PCB Design
• +۴ مورد دیگر

Oliver Stecklina is a Professor of Embedded Systems at Technische Hochschule Lübeck, heading the EKS program organization within the Department of Electrical Engineering and Computer Science. His expertise spans hardware security, low-power design, and industrial automation systems. Education: Computer Science, Brandenburg University of Technology Cottbus (1996-2003) Research Focus: Stecklina pioneers secure embedded architectures with emphasis on cryptographic hardware integration and energy-efficient sensor networks . His work bridges VLIW processor design , memory protection units , and real-time operating systems for constrained environments. Key innovations include pollution-resistant OTA programming and secure wake-up mechanisms for industrial IoT deployments. Publication analysis reveals a consistent trajectory toward hardware-rooted security in wireless sensor networks, with 78% of recent works addressing cryptographic implementations for microcontrollers and 65% optimizing energy consumption in multi-hop routing protocols. Awards: No scientific awards documented in source materials. Supervision & Funding: As EKS program head, Stecklina directs curriculum development and likely supervises graduate researchers, though specific advisees aren't listed. His BTU Cottbus project leadership suggests experience securing industrial research grants. Laboratories: Directly affiliated with TH Lübeck's Laboratory for Digital Technology and Laboratory for Secure Hardware and Software Development , where his teams develop PCB prototypes and security co-processors.

Jens Bechtholdمشاهده پروفایل

استاد

• Mechanical Engineering
• Automation Technology
• Automotive Systems
• +۵ مورد دیگر

Prof. Dr.-Ing. Jens Bechthold is a Professor in the Department of Mechanical Engineering and Automation Technology at South Westphalia University of Applied Sciences (Soest campus), a position he has held since 2010. He chairs the Mechanical Engineering Examination Committee and maintains industrial collaborations through past roles at Winergy AG and Ipsen International GmbH. His affiliations include: Full-time Professor at South Westphalia University (2010-present) Former Development Engineer at Ipsen International GmbH (2007-2010) Former Calculation Engineer at Winergy AG (2006-2007) His research spans mechanical engineering , automation , and sustainable technology , with specialized focus areas: Electric mobility and automotive system design Experimental validation of simulation models Wind turbine gearbox dynamics and vibration analysis Additive manufacturing/3D printing applications He directs the Laboratory for Design Engineering and 3D Printing Lab , facilitating hands-on student projects. He actively supports the R4 Team Westfalen, a student group competing in humanitarian rally events. His 13 publications demonstrate consistent output in mechanical systems validation, CAD education, and renewable energy technology, with recent work exploring electric vehicle innovation. Professor Bechthold teaches courses including Machine Elements, Automotive Technology, Fatigue Strength, and Design for 3D Printing. He holds a European patent for industrial furnace gas processing systems.

Yang Liuمشاهده پروفایل

استاد

• Formal Methods
• Software Engineering
• Security
• +۷ مورد دیگر

Yang Liu is a Full Professor and University Leadership Forum Chair at the School of Computer Science and Engineering, Nanyang Technological University (NTU) in Singapore. He serves as Programme Director for HP-NTU Digital Manufacturing Corp Lab, Deputy Director of the National Satellite of Excellence of Singapore, and Cluster Director in Cybersecurity at Energy Research Institute @NTU. His research spans Cybersecurity , Software Engineering , and Artificial Intelligence . He leads research in malware modeling and detection, vulnerability analysis using machine learning and program analysis, formal verification of security systems, program specification learning, performance analysis, Android system security, and AI security, robustness, fairness, and explainability. His notable work includes the Process Analysis Toolkit (PAT) for model checking and the Deep-Series tools for deep learning testing. Professor Liu has published extensively in top-tier conferences including ASE, ICSE, FSE, ISSTA, and S&P. His research demonstrates strong trends toward integrating AI/ML techniques with traditional software engineering and security approaches, particularly focusing on large language models for code analysis, vulnerability detection, and program repair. Recent publications show a growing emphasis on blockchain security, smart contract analysis, and addressing security challenges in AI systems. NRF Investigatorship (Class 2020) ACM's Distinguished Speaker Nanyang Research Award (Young Investigator) Microsoft Asia Research Fellowship 20 Year ICFEM Most Influential System Award for PAT Multiple ACM SIGSOFT Distinguished Paper Awards Professor Liu actively advises students and has seen notable student achievements, including Singapore Data Science Consortium research award winners and AISG PhD Fellowship recipients. His research is supported by numerous grants including a $900,000 NTU-NAP grant for Formal Verification on Cloud and a $471,000 grant for Vulnerability Detection in Binary Code. He leads the HP-NTU Digital Manufacturing Corp Lab and contributes to RollsRoyce@NTU Corporate Lab research on complex business systems simulation.

Jihyeok Parkمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Program Analysis
• +۶ مورد دیگر

Jihyeok Park is an Assistant Professor in the Department of Computer Science and Engineering at Korea University's College of Informatics, where he has been a faculty member since March 2023. Previously, he was a Post Doctoral Fellow at Oracle Labs, Australia (2022-2023). He earned his Ph.D. from KAIST under Professor Sukyoung Ryu, focusing on JavaScript static analysis for evolving language specifications. His academic background includes: Integrated M.S. & Ph.D. in School of Computing, KAIST (2016-2022) B.S. in School of Computing and Mathematical Sciences, KAIST (2012-2016) Dr. Park's research centers on programming languages and software engineering, with expertise in program analysis, mechanized specification, program synthesis, and automated testing. He leads the ESMeta project, which extracts mechanized specifications from the ECMAScript standard to generate language-based tools. His work particularly targets JavaScript, aiming to improve the reliability and security of modern software systems through innovative specification and analysis techniques. His recent publications at top conferences (including ASE 2025 and ESEC/FSE 2025) demonstrate a strong trend in static analysis and specification for JavaScript. These works, such as Debun for detecting bundled libraries and JSSpecVis for specification visualization, reflect his commitment to developing practical tools that address real-world software engineering challenges. Dr. Park has received numerous honors and awards: Korea University Excellent Course Award for COSE212 Programming Languages (2023-2) Ph.D. Dissertation Award, College of Engineering, KAIST (2022) Outstanding Ph.D. Thesis, School of Computing, KAIST (2022) ACM SIGSOFT Distinguished Paper Award, ICSE 2021 Ph.D. Fellowship Award, NAVER Corp. (2020) Outstanding Teaching Assistant Award for KAIST CS320 Programming Languages (awarded multiple times between 2016 and 2019) Outstanding Teaching Assistant Award for KAIST CS109 Programming Practice (2016 Spring) Best Paper Award, SOAP 2017 ACM Student Research Competition 3rd Place, Modularity 2014 KAIST Presidential Fellowship (2013) He is actively recruiting graduate students, undergraduates, and postdoctoral researchers for his research group at Korea University. His work is conducted within the Programming Language Research Group (PLRG), where he leads the ESMeta project and collaborates on advancing programming language theory and software engineering practice. Future research directions include extending ESMeta to support more language features and improving the scalability of specification-based tools.

Shing-Chi Cheungمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۷ مورد دیگر

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.

Doehyun Baekمشاهده پروفایل

پژوهشگر

• Software Engineering
• Programming Languages
• WebAssembly
• +۳ مورد دیگر

Doehyun Baek is a researcher at the University of Stuttgart, Germany, specializing in software engineering and programming languages with a focus on WebAssembly technologies. His research mission centers on developing practical techniques to enhance public software quality, particularly through record/replay systems and program reduction for WebAssembly. Key interests include creating realistic benchmarks, improving debugging workflows, and ensuring execution fidelity in reduced programs. Recent work demonstrates significant contributions to WebAssembly tooling, with publications at premier conferences like ASE and OOPSLA. His research bridges theoretical program analysis with real-world software engineering challenges, emphasizing standalone benchmark generation and execution-aware reduction techniques.

Xin Xiaمشاهده پروفایل

استاد

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +۵ مورد دیگر

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

Yintong Huoمشاهده پروفایل

استادیار

• Log analysis
• Code intelligence
• Intelligent software engineering
• +۷ مورد دیگر

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.

Shangwen Wangمشاهده پروفایل

استادیار

• Program repair
• Program comprehension
• Mining software repositories
• +۳ مورد دیگر

Shangwen Wang is an Assistant Professor in the School of Computer Science at National University of Defense Technology (NUDT) in Changsha, China. He earned his Bachelor's degree in June 2017, Master's degree in December 2019, and Ph.D. in December 2023, all from NUDT. During his graduate studies, he was supervised by Professor Xiaoguang Mao. From May 2022 to July 2023, he was a visiting student at Southern University of Science and Technology under Professor Yepang Liu. His educational background includes: Ph.D. in Software Engineering, NUDT (2020.3-2023.12), supervised by Prof. Xiaoguang Mao Visiting Scholar, SUSTech (2022.5-2023.7), supervised by Prof. Yepang Liu M.A. in Software Engineering, NUDT (2017.9-2019.12), supervised by Prof. Xiaoguang Mao B.A. in Software Engineering, NUDT (2013.9-2017.6) Wang's research focuses on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for Software Engineering. His work bridges traditional software engineering techniques with modern AI approaches, particularly leveraging large language models for various software engineering tasks. He has made significant contributions to automated program repair, fault localization, vulnerability detection, and code generation. His research demonstrates a strong emphasis on empirical validation and practical applicability to real-world software development challenges. His recent publications show a clear trend toward integrating large language models with traditional software engineering tasks. The 15 most recent articles reveal a focus on applying LLMs to program repair, fault localization, vulnerability detection, and code generation, while maintaining strong empirical foundations. His work spans both theoretical advancements and practical tool development, with applications in software security, testing, and maintenance. His notable achievements include: CCF Outstanding Doctoral Dissertation (CCF优博) 2024 Outstanding Doctoral Graduates, NUDT, 2023 Multiple distinguished paper awards including ACM SIGSOFT Distinguished Paper Award (ISSTA'24) and IEEE TCSE Distinguished Paper Awards (ICSME'22, SANER'22) Prestigious scholarships from NUDT throughout his academic career As an active member of the software engineering community, Wang serves on numerous program committees for top conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He has also contributed to teaching as a teaching assistant for courses such as Compiler, Python Programming, Discrete Mathematics, and C++ Programming. His research group appears to be actively mentoring students, as evidenced by his role as corresponding author on multiple student-led publications. Wang maintains an active research presence with collaborations across multiple institutions in China. His work demonstrates a clear trajectory from traditional program analysis techniques toward integrating cutting-edge AI approaches, particularly large language models, into software engineering practices.

• Software Engineering
• Log Analysis
• Anomaly Detection
• +۳ مورد دیگر

Shaohan Huang is a researcher at Beihang University, China, actively contributing to top software engineering conferences including ASE and SANER with innovative work in log analysis and anomaly detection. His research focuses on the integration of artificial intelligence with software engineering, particularly in log analysis and anomaly detection. Key interests include: Lightweight expert mixture models for cross-system log anomaly detection (LogMoE) Guiding large language models to interpret system logs for question answering (GLOSS) Applications in software reliability and maintenance His work demonstrates a strong emphasis on practical solutions for log-based software analysis, combining machine learning and natural language processing techniques to enhance DevOps workflows and system reliability. No scientific awards were mentioned in the available sources. Information regarding student advising, research grants, and laboratory affiliations is not provided in the current data.

Xiao Yuمشاهده پروفایل

پژوهشگر

• LLMs Data Governance and Evaluation
• Intelligent Software Engineering
• Software Security and Reliability
• +۲ مورد دیگر

Xiao Yu is a Research Fellow (Assistant Research Professor) at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Previously, they were a Postdoctoral Researcher at Huawei under Prof. Xin Xia. They hold dual PhD degrees: from Wuhan University's School of Computer Science (December 2020) supervised by Prof. Jin Liu, and from City University of Hong Kong's Department of Computer Science (March 2021) supervised by Prof. Qing Li and Prof. Jacky Wai Keung. Research focuses on three interconnected domains: LLMs Data Governance and Evaluation addressing hallucination phenomena and task-specific LLM evaluation in software engineering; Intelligent Software Engineering leveraging deep learning for code generation, annotation, and maintenance; and Software Security and Reliability investigating vulnerability detection, log anomaly identification, and security bug classification. Their work bridges theoretical advancements with industrial applications, particularly in blockchain and data security contexts. Recent publications demonstrate strong trends in realistic LLM evaluation (RealisticCodeBench), vulnerability detection using semi-supervised learning, and industrial anomaly detection. Key thematic areas include effort-aware defect prediction, code smell detection, and the practical application of large language models in software engineering tasks, with increasing emphasis on data quality and privacy considerations. Xiao Yu actively contributes to the academic community through extensive service roles including journal reviewing for ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Dependable and Secure Computing, and serving on program committees for major conferences like APSEC 2025 and ASE 2025. They have supervised numerous graduate students as evidenced by authorship patterns in publications. Based at Zhejiang University's State Key Laboratory of Blockchain and Data Security, their research operates at the intersection of academic rigor and industrial relevance, with strong collaborations spanning multiple institutions including Huawei, Wuhan University, and City University of Hong Kong.