جست‌وجوی استاد و پژوهشگر

Nazareno Aguirreمشاهده پروفایل

استاد

• Software Engineering
• Formal Methods for Software Development
• Formal Analyses of Software
• +۷ مورد دیگر

Nazareno Aguirre is a Full Professor at the Departamento de Computación, Facultad de Ciencias Exactas, Físico-Químicas y Naturales (FCEFQyN), Universidad Nacional de Río Cuarto, Argentina. He is also an Independent Researcher at Argentina's National Council for Scientific and Technical Research (CONICET) and a Visiting Professor at Guangdong Technion - Israel Institute of Technology in Shantou, China. Aguirre was promoted to Full Professor at the University of Rio Cuarto in November 2024, having previously served as an Associate Professor. His research interests focus on Software Engineering, particularly Formal Methods for Software Development. Aguirre specializes in formal analyses of software based on SAT solving, including specification inference, automated testing, bounded verification, and program repair. His work aims to build reliable quality software through formal underpinnings and automated techniques. He has made significant contributions to the analysis of Alloy specifications, test generation, and program verification. Analysis of Aguirre's recent publications (2023-2025) reveals a strong focus on advancing formal methods for software verification and testing. His work spans multiple dimensions including specification inference (SpecFuzzer, EvoSpex), test generation (BEAPI, combinatorial test generation for Alloy), program repair (ACoRe), and verification techniques (LTL solvers, Alloy model analysis). A common thread through his research is the application of formal methods and SAT solving to practical software engineering challenges, with emphasis on automation and scalability. Nazareno Aguirre serves in several important academic roles: Associate Editor of IEEE Transactions on Software Engineering Coordinator of the Informatics, Electronics and Communications Commission at Argentina's National Agency for the Promotion of Research Program Committee member for major conferences including ICSE 2025, ISSTA 2025, ASE 2025, and ICST 2025 His research group at Universidad Nacional de Río Cuarto collaborates extensively with international researchers, particularly with Marcelo Frias and colleagues at the same institution, as well as with researchers from institutions worldwide including the University of Bergamo in Italy. Aguirre's work is characterized by a strong theoretical foundation combined with practical tool development, as evidenced by numerous tool demonstration papers at top software engineering conferences.

Michele Pasquaمشاهده پروفایل

استادیار

• Static Analysis
• Abstract Interpretation
• Software Security
• +۵ مورد دیگر

Michele Pasqua is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Verona, Italy, where he completed his entire academic training. His career demonstrates deep institutional continuity at this Italian university. His educational pathway includes: PhD in Computer Science (2019), University of Verona MSc in Computer Science and Engineering with Security specialization (2015), University of Verona BSc in Computer Science (2013), University of Verona Pasqua's research bridges theoretical formal methods and practical security applications. He pioneered abstract interpretation techniques for hyperproperties verification in his doctoral work, later expanding to REST API security testing through novel approaches combining deep reinforcement learning and natural language processing. His scholarship consistently addresses critical vulnerabilities in emerging technologies like blockchain smart contracts and web services, demonstrating both theoretical rigor and practical impact in software security. Analysis of his publication timeline reveals a strategic evolution from foundational formal methods (2020-2023) toward empirical security testing frameworks (2023-2025), with increasing emphasis on tool development and evaluation infrastructure for REST API security. This trajectory shows growing industry relevance while maintaining strong theoretical underpinnings in program analysis. No scientific awards are documented in the available materials. His advising activities and grant funding remain unspecified, though his leadership roles in workshops like NSAD and Lipari Summer School suggest active research group supervision. Similarly, no dedicated laboratory facilities are mentioned in the provided texts.

Martin Kelloggمشاهده پروفایل

استادیار

• Practical Program Verification
• Software Engineering
• Programming Languages
• +۲ مورد دیگر

Martin Kellogg is an Assistant Professor in the Department of Computer Science at the New Jersey Institute of Technology. His primary academic focus is on making software verification practical for everyday developers, aiming to integrate verification techniques as standard tools in the developer's toolkit similar to unit testing and code review. Dr. Kellogg's research spans several key areas in programming languages and software engineering, with a particular emphasis on practical program verification. His work focuses on two main approaches: improving the expressivity of simple verification technologies to prove more facts about programs within developer constraints, and convincing developers of verification benefits through deployment in new domains and improved usability. His expertise includes pluggable type systems, abstract interpretation, and resource leak verification, with applications across various programming language paradigms and software development contexts. Analysis of Dr. Kellogg's publication record reveals a consistent focus on making verification more accessible and practical for developers. His work spans foundational verification techniques like accumulation analysis for resource leaks and initialization, to practical applications in compliance verification. The research shows progression from theoretical foundations to practical implementations, with increasing emphasis on developer experience and integration into standard development workflows. His publications appear consistently in top-tier software engineering and programming languages conferences including ASE, ICSE, ESEC/FSE, and ISSTA. Dr. Kellogg is actively involved in the academic community as a program committee member for numerous conferences including ASE, ESEC/FSE, PLDI, and ISSTA. He has served in various organizational roles such as Proceedings Chair for STATIC 2026 and Sponsorship Co-Chair for multiple conferences. His GitHub profile shows active contributions to open-source verification tools, particularly related to the Checker Framework for Java, demonstrating his commitment to translating research into practical tools that developers can use. Through his work on repositories like the divide-by-zero checker and other verification tools, Dr. Kellogg maintains an active research lab focused on practical program analysis. His team develops and maintains specialized type checkers that address specific verification challenges, contributing to the broader ecosystem of static analysis tools for software developers.

Mohamed Wiem Mkaouerمشاهده پروفایل

دانشیار

• Software Engineering
• Refactoring
• Artificial Intelligence
• +۷ مورد دیگر

Mohamed Wiem Mkaouer is an Associate Professor of Software Engineering in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the principal investigator of the SMILE (Software Maintenance and Intelligent Evolution) Research Lab, which focuses on designing intelligent algorithms to tackle software engineering problems. Dr. Mkaouer has published over 130 peer-reviewed papers in top venues including TSE, TOSEM, EMSE, ICSE, FSE, CHI, and ASE, and has secured $1.5 million in externally funded projects as PI/Co-PI. PhD in Computer and Information Science, 2016, University of Michigan MS in Computer Science, 2010, University of Geneva (Switzerland) BS in Computer Science, 2007, University of Tunis (Tunisia) Dr. Mkaouer's research spans the intersection of Software Engineering and Artificial Intelligence, with particular focus on software quality assurance, systems refactoring, and the application of search-based techniques. His work increasingly explores the integration of large language models in software development processes, examining how AI tools like ChatGPT impact developer workflows, code quality, and refactoring practices. His research often employs empirical methods, mining software repositories, and conducting large-scale studies of developer behavior. Analysis of Dr. Mkaouer's recent publications reveals a strong trend toward investigating AI-assisted software development, particularly focusing on how developers interact with large language models for code refactoring and quality improvement. His work bridges traditional software engineering concerns with emerging AI capabilities, examining both infrastructure-as-code quality and mobile application development challenges. His research methodology typically combines empirical studies of developer practices with the development of intelligent tools to support software maintenance. Recipient of 5 best-paper/presentation awards RIT GCCIS Best Emerging Researcher Award (2020) Dr. Mkaouer actively mentors a diverse group of students at multiple levels, from undergraduate to PhD candidates, through the SMILE Research Lab. His externally funded projects demonstrate the practical relevance of his research to industry needs. He also contributes significantly to the software engineering community through service on program committees for major conferences including ASE, ICSE, and ICSME, where he frequently serves in leadership roles for tracks related to software maintenance, refactoring, and AI in software engineering. The SMILE Research Lab, under Dr. Mkaouer's leadership, maintains strong industry connections and collaborates with researchers worldwide. The lab's work has practical implications for improving software quality, reducing technical debt, and enhancing developer productivity through intelligent tooling and evidence-based practices.

Yanyan Jiangمشاهده پروفایل

دانشیار

• Software Engineering
• Software Testing
• Software Verification
• +۲ مورد دیگر

Dr. Yanyan Jiang is an Associate Professor at Nanjing University, China, specializing in software systems with a focus on validation, verification, and synthesis. His research spans software testing, analysis, and program synthesis, with significant contributions to the field of software engineering. Dr. Jiang's research interests include: Software Testing - particularly code coverage validation and Android application testing Software Verification - with focus on compiler validation and system correctness Program Synthesis - especially for dynamic software updates and state transformation Operating Systems - including educational approaches and system design principles His recent work shows a strong trend toward practical applications of software engineering research, with focus on Android systems, compiler validation, and self-adaptive software. His publications demonstrate expertise in both theoretical approaches to software validation and practical tools for real-world systems, particularly in the areas of code coverage validation, dynamic software updating, and compiler verification. Dr. Jiang has received numerous prestigious awards: SOSP'23 Best Paper Award ACM SIGSOFT Distinguished Paper Awards (ICSE'21 and ASE'18) ACM Europe Council Best Paper Award Microsoft Research Asia Fellowship Award CCF Outstanding Doctoral Dissertation Award Dr. Jiang actively contributes to the software engineering community through program committee memberships at major conferences including ASE, ICSE, and ESEC/FSE. He also serves on the Steering Committee for regional Computing Olympiad contests and as a Technical Committee member for the Asia-Pacific Informatics Olympiad (APIO) 2022-2024, demonstrating his commitment to both research excellence and educational outreach in computer science.

Eunsuk Kangمشاهده پروفایل

دانشیار

• Software design
• Software architecture
• Formal methods
• +۵ مورد دیگر

Eunsuk Kang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. He leads the Software Design and Analysis (SoDA) Group, focusing on the intersection of software engineering and formal methods to create safe, secure, and reliable software systems. His research spans several key areas including software design, requirements engineering, modeling, specification and verification, with particular emphasis on system safety, security, and cyber-physical systems. Kang's work addresses fundamental challenges in designing robust software systems that can withstand environmental deviations and uncertainties. Kang's publication record shows a strong trajectory of research contributions, with recent work focusing on specification engineering, robustness in software design, automated reasoning techniques, and safety in cyber-physical systems. His publications appear consistently in top-tier software engineering conferences including ICSE, ASE, FSE, and ESEC/FSE. As an educator, Kang teaches advanced courses including Designing Large-Scale Software Systems, Formal Methods, and Software Engineering for AI-enabled Systems, reflecting his expertise at the cutting edge of software engineering research and practice. He actively contributes to the academic community as a Program Co-Chair for SEAMS 2026, co-organizer of the Dagstuhl Seminar on Specification Engineering, and serves on program committees for numerous prestigious conferences including ICSE, ASE, and OOPSLA. Kang is also an Associate Editor for IEEE Transactions on Software Engineering (TSE).

Yijun Yuمشاهده پروفایل

استاد

• Requirements Engineering
• Automated Software Engineering
• Software Maintenance and Evolution
• +۳ مورد دیگر

Yijun Yu is a Professor of Software Engineering at The Open University, UK, where he leads research on automated techniques to enhance software engineering productivity and software quality. He serves as Associate Editor for the Software Quality Journal and Chair of the BCS Specialist Group on Requirements Engineering, while actively contributing to program committees of premier conferences including ICSE, FSE, RE, ICSME, and SEAMS. His research spans Requirements Engineering, Automated Software Engineering, and Software Maintenance and Evolution, with emphasis on developing methods for software adaptation, security enhancement, and resilience engineering. Key contributions include techniques for requirements-driven adaptation, Rust-based safety mechanisms, and socio-technical resilience frameworks that bridge theoretical advances with practical industry applications. Analysis of his 2018-2024 publications reveals a strong trajectory in programming language safety (particularly Rust transpilation), neural code representation learning, and adaptive security systems. His work consistently addresses real-world challenges in memory safety, vulnerability prediction, and autonomous system behavior through innovative static analysis, machine learning, and formal methods approaches. His scientific achievements include: 10 Year Most Influential Paper award (CASCON’16) 6 Best Paper awards (SEAMS’18, iRENIC’16, TrustCom’14, EICS’13, VMPDP’01) 3 Distinguished Paper awards (RE’11, BCS’08, ASE’07) Best Tool Demo Paper Award (RE’13) Best Student Paper Award (PDCS’02) As Principal Investigator, Professor Yu has managed knowledge transfer projects with industry leaders: NATS (air traffic management) Huawei (telecommunications) IBM (enterprise software) CA Technologies (IT management) RealTelekom (network solutions) These collaborations focus on translating academic research into practical solutions for requirements engineering, adaptation techniques, and security challenges in industrial software systems.

Liliana Pasqualeمشاهده پروفایل

دانشیار

• Requirements Engineering
• Self-Adaptive Systems
• Security
• +۵ مورد دیگر

Liliana Pasquale is an Associate Professor at University College Dublin's School of Computer Science and a funded investigator at Lero - the Irish Software Research Centre. She also serves as the Director of the UCD MSc in Cybersecurity program, demonstrating her leadership in both academic and research domains. Her educational background isn't explicitly detailed in the provided text, but her extensive publication record and academic positions indicate strong academic foundations in computer science and software engineering. Pasquale's research focuses on requirements engineering, self-adaptive systems, security, and digital forensics, with particular emphasis on using runtime models to engineer complex cyber-physical systems that satisfy security requirements while remaining forensic-ready and compliant with data protection regulations. Her work bridges theoretical software engineering with practical security applications across various domains including smart homes, industrial control systems, and transportation networks. Analyzing her recent publications reveals a consistent focus on adaptive security mechanisms, forensic readiness for cyber-physical systems, and GDPR compliance through software engineering approaches. Her research shows a progression from foundational work in adaptive security to more specialized applications in specific domains like industrial control systems and ransomware detection. Lero Research Award (2024) Best Paper Award at SEAMS 2014 for 'User-centric Adaptation of Multi-tenant Services: Preference-based Analysis for Service Reconfiguration' Best Student Paper Award at ICWS 2009 Pasquale actively advises PhD students on topics spanning secure software engineering, forensic-ready systems, and adaptive security. Her current supervision includes research on securing AI-based coding assistants, engineering adaptive authentication, ransomware detection, sustainable adaptive security, and cyber information sharing. She has successfully guided multiple PhD students to completion, with graduates now working as R&D engineers, post-doctoral researchers, and assistant professors. Beyond direct student supervision, she serves on numerous program committees for major software engineering conferences including ICSE, ASE, and SEAMS, demonstrating her significant contribution to the academic community. She is part of the SPARE research group at University College Dublin, which focuses on security and privacy aspects of adaptive and reliable software engineering. Her work often involves collaboration with researchers across Europe and internationally, particularly with Prof. Bashar Nuseibeh and other members of the Lero research center.

Tianyi Zhangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +۵ مورد دیگر

Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.

Wensheng Douمشاهده پروفایل

پژوهشگر

• software analysis and testing
• distributed systems
• database systems
• +۱ مورد دیگر

Wensheng Dou is a Researcher at the Institute of Software, Chinese Academy of Sciences . His work bridges software engineering, distributed systems, and database technologies, with a strong emphasis on improving system reliability through advanced testing methodologies. Research Focus: Dou's interests span: Software Testing & Analysis : Differential testing, fault injection, and bug detection in databases and distributed systems. Systems Reliability : Cloud systems, graph databases (Gremlin/RDF), and transaction processing. Emerging Platforms : IoT, mobile apps (WeChat), and LLM-assisted bug fixing. Publication Trends: His recent work (2022–2025) demonstrates a consistent focus on: Automated testing tools for databases (e.g., FaultFuzz ) and distributed systems. Bug characterization in real-world systems (cloud, IoT, mini-apps). Innovations in differential testing and coverage-guided fault injection.

NIANYU LIمشاهده پروفایل

پژوهشگر

• Self-adaptive Systems
• Cyber-Physical Systems
• Formal Verification
• +۴ مورد دیگر

NIANYU LI is an active researcher at ZGC Lab, China specializing in human-involved self-adaptive systems. Their work integrates rigorous modeling techniques to ensure software safety, security, and reliability in dynamic environments, with significant contributions to cyber-physical systems and formal verification methodologies. They earned a Ph.D. in Computer Software and Theory from Peking University in 2021 under Prof. Zhi Jin and Prof. Wenpin Jiao, with additional research experience at the National Institute of Informatics (NII) and Carnegie Mellon University (CMU) under Prof. Zhenjiang Hu and Prof. David Garlan respectively. Research focuses on human-in-the-loop adaptation mechanisms, with expertise spanning requirements modeling, system verification, and safety-critical cyber-physical implementations. Their methodology combines formal control paradigms with practical human factors considerations to address environmental uncertainty in adaptive systems. Publication trends reveal consistent contributions to ASE, ICSE, and specialized conferences like SASMS, with recent work exploring LLM applications in adaptation, federated learning robustness, and SBOM generation. Key themes include verification-driven design, human-system coordination, and empirical validation of adaptive mechanisms in real-world scenarios. As an active community contributor, they serve on program committees for ASE, ACSOS, and ICSE events while maintaining leadership roles in artifact evaluation. Their work bridges theoretical formal methods with practical implementation challenges in adaptive systems. NIANYU LI maintains research continuity through collaborations with Peking University and international institutions, directing focus toward explainable adaptation mechanisms and safety assurance in increasingly complex human-software ecosystems.

Fiorella Zampettiمشاهده پروفایل

پژوهشگر

• Experimental Software Engineering
• Continuous Integration and Delivery
• Static Analysis Tools
• +۴ مورد دیگر

Fiorella Zampetti is a researcher at the University of Sannio, Italy, specializing in empirical software engineering with a focus on continuous integration and delivery, static analysis tools, mining software repositories, and software maintenance and evolution. Her work bridges theoretical research with practical applications in real-world software development environments. Her research interests span multiple critical areas of modern software engineering, with particular emphasis on Experimental methodologies for evaluating software engineering practices Technical debt identification and management, especially in emerging domains like deep learning systems Continuous integration and delivery pipelines, including common anti-patterns and solutions Mining software repositories to extract valuable insights about development processes Software quality assessment through static analysis and empirical studies Analysis of her recent publications reveals an evolving research trajectory that has increasingly incorporated artificial intelligence and large language models into traditional software engineering concerns. Her 2023-2025 work shows a significant focus on how AI-generated code impacts software development practices, licensing concerns, and educational contexts. She has also maintained a strong thread of research on technical debt, particularly examining self-admitted technical debt across different software domains. Dr. Zampetti has been actively involved in the software engineering research community as a program committee member for major conferences including ASE, ICSE, ESEC/FSE, and ICSME. Her service on these committees demonstrates recognition by her peers as a subject matter expert in her research domains. Her research has practical implications for software development teams looking to improve their CI/CD practices, manage technical debt effectively, and understand the emerging challenges posed by AI-assisted development tools. She has conducted numerous empirical studies that provide evidence-based insights into software engineering practices across both open-source and industrial contexts.

Guowei Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Machine Learning Systems
• Programming Languages
• +۵ مورد دیگر

Guowei Yang is a Senior Lecturer (equivalent to U.S. Associate Professor) at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland. His research bridges software engineering, machine learning, and programming languages, focusing on enhancing software and ML system reliability/security. Dr. Yang leads projects in symbolic execution, fuzzing techniques, Android app testing, and LLM-based tool development. Research Interests: Core interests include software testing automation, Android ecosystem analysis, ML system robustness, and symbolic execution optimization. His work emphasizes practical tools for API compatibility detection, security patch generation, and fuzzing efficiency. Recent projects leverage large language models for document-guided testing and dynamic vulnerability detection. Professional Service: Dr. Yang actively contributes to top-tier conferences as a program committee member (ICSE, FSE, ASE, ISSTA) and organizer (DSN 2024 Local Chair, TAIC PART Co-Chair). He mentors through Google Summer of Code and NSF Research Experiences for Undergraduates programs, and reviews for journals including IEEE TSE and ACM TOSEM.

Günter Neumannمشاهده پروفایل

استاد

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

Georg Rehmمشاهده پروفایل

استاد مدعو

• Language Technology
• Computational Linguistics
• Artificial Intelligence
• +۴ مورد دیگر

Georg Rehm holds an Adjunct Professorship in Computational Linguistics and Language Technology at Humboldt-Universität zu Berlin's Faculty of Philosophy. He is a Principal Researcher and Research Fellow at DFKI Berlin's Speech and Language Technology Lab, and Head of the W3C Germany/Austria Chapter. His roles include coordinating major EU initiatives like the European Language Grid (ELG) and QURATOR. Rehm's work spans Language Technology, AI, and multilingual systems, with a focus on digital language equality, curation technologies, and open science infrastructure. He leads projects such as HIVEMIND (2025-2027) and Language Data Space (2023-2025), and has contributed over 275 publications in computational linguistics and related fields. His research addresses challenges in multilingual NLP, fake news detection, and ethical AI deployment. Rehm oversees strategic initiatives like the META-NET network and chairs conferences such as ACL Industry Track and Sci-K workshops. He advises on EU policy frameworks for language technology and collaborates with organizations like the DIN Presidential Committee and FOCUS.ICT. His academic network spans global institutions, and he actively participates in program committees for top venues including ACL, NAACL, and COLING. Rehm's lab develops tools like the Knowledge Storage Ecosystem and contributes to open-source LLM projects like Occiglot and OpenGPT-X. He holds a PhD and has over 25 years of industry-research experience. His research interests include semantic storytelling, interoperable metadata, and AI ethics. He is affiliated with multiple institutes, including the DFKI Lab Berlin and the W3C, and serves on editorial boards for journals like Language Resources and Evaluation.