جستجوی استاد راهنمای دانشگاهی

Nadia Polikarpovaمشاهده پروفایل

دانشیار

• Program Synthesis
• Program Verification
• Type Systems
• +۵ مورد دیگر

Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.

Toby Jia-Jun Liمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Toby Jia-Jun Li is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he leads the SaNDwich Lab. He also serves as the Director of the Human-Centered Responsible AI Lab in the Lucy Family Institute for Data & Society and is a Faculty Fellow at the Institute for Educational Initiatives (IEI). Previously, he was affiliated with Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and GroupLens Research. Dr. Li's research spans the intersection of Human-Computer Interaction (HCI), End-User Software Engineering, Machine Learning (ML), and Natural Language Processing (NLP), with recent work focusing on addressing societal challenges in the future of work through human-AI collaborative approaches. His work has resulted in over 40 publications at premier venues including CHI, UIST, CSCW, ACL, and ICSE, with 8 papers winning Best Paper or Honorable Mention awards. His recent publications demonstrate a strong focus on human-AI collaboration across various domains, including code understanding, privacy, accessibility, and creative tools. The work shows a trajectory toward increasingly sophisticated integration of human-centered design with AI capabilities, particularly using large language models to enhance human productivity and address societal challenges. Google Research Scholar Award recipient Recipient of Yahoo! Fellowship ($100,000/year) Best Paper Award at UIST 2020 Best Paper Honorable Mention Award at CHI 2021 Best Paper Award at CSCW 2024 Best Paper Award at CHI 2025 Dr. Li actively mentors Ph.D. students and has established collaborations with Google, Microsoft Research, IBM Research, Adobe, Verizon, and J.P. Morgan. His research has been supported by NSF, Google Research Scholar Program, AnalytiXIN Initiative, Yahoo! InMind project, and J.P. Morgan. He is currently recruiting Ph.D. students and undergraduate researchers for his SaNDwich Lab, which focuses on developing interactive systems to empower individuals to create, configure, and extend AI-powered computing systems.

Elena Simperlمشاهده پروفایل

استاد

• Human-centric AI
• Knowledge Graphs
• Knowledge Engineering
• +۷ مورد دیگر

Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +۷ مورد دیگر

Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.

Chanchal K. Royمشاهده پروفایل

استاد

• Code Clone Detection and Analysis
• Software Evolution and Maintenance
• Software Analytics Research
• +۳ مورد دیگر

Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.

Alexander Kollerمشاهده پروفایل

استاد

• Computational Linguistics
• Neural-Symbolic Models
• Semantic Parsing
• +۵ مورد دیگر

Professor Alexander Koller is a leading academic in Computational Linguistics at Saarland University's Department of Language Science and Technology. He holds a courtesy appointment in Computer Science and contributes to the Saarland Informatics Campus - one of Europe's premier computer science research centers. He leads the Computational Linguistics group and serves as speaker for the DFG-funded Research Training Group 'Neuroexplicit Models of Language, Vision, and Action'. PhD in Computer Science (Saarland University) Former positions: University of Potsdam, Columbia University, University of Edinburgh Sabbatical experiences: Meta AI (Paris), Allen Institute for AI (Seattle) His research focuses on computational modeling of meaning and reasoning in NLP, combining neural and symbolic approaches. Key contributions include semantic parsing systems like the AM parser and Alto, neurosymbolic models, and the GIVE Challenge for NLG evaluation. His recent work explores LLMs' limitations in problem-solving and compositional generalization. Recent publications highlight diverse applications across semantic parsing, dialogue systems, and LLM evaluation. Awards include ACL 2020 Best Theme Paper and multiple Outstanding Paper recognitions at ACL conferences. 2025 - AI Action Summit keynote speaker 2023 - ACL Outstanding Paper Awards 2022 - ELLIS Faculty appointment He maintains the DialogOS system for spoken dialogue development and teaches advanced computational linguistics topics. His group includes multiple postdocs and PhD students working across LLMs, dialogue systems, and semantic modeling.

Muhammad Ali Gulzarمشاهده پروفایل

استادیار

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +۵ مورد دیگر

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

Michael Pradelمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Program Analysis
• +۹ مورد دیگر

Michael Pradel is a full professor at the University of Stuttgart, specializing in software engineering, programming languages, and machine learning. He will join CISPA as a faculty member from September 2025 while retaining his Stuttgart position. His research focuses on: Neuro-symbolic software analysis Web application analysis Dynamic analysis and test generation Quantum software testing Machine learning for code Recent publications address: LLM-based program repair (RepairAgent, Treefix) WebAssembly analysis (Wasm-R3, LintQ) Python security and analysis (DyLin, DyPyBench) Quantum program analysis (LintQ) Scientific awards: Ernst-Denert Software Engineering Award Emmy Noether grant (1.3M Euro) ERC Starting Grant (1.5M Euro) 3x ACM SIGSOFT Distinguished Paper Award at FSE ACM Distinguished Member Best Paper/Distinguished Paper Awards at ISSTA, ASE, ASPLOS, MSR Key contributions include: DeepBugs for name-based bug detection Getafix for automated bug fixing LintQ for quantum program analysis DyLin for Python dynamic analysis Neuro-symbolic developer tools

• Computer Security
• Network Security
• Systems Security
• +۴ مورد دیگر

Nuno Santos is an Associate Professor in the Department of Computer Science and Engineering at Instituto Superior Técnico (IST), University of Lisbon, and a senior researcher at INESC-ID Lisbon. He leads the SysSec team, focusing on systems security and privacy. His research spans secure enclaves, network security, and censorship-resistant systems. Education: Ph.D. in Computer Science (2013) from Max Planck Institute for Software Systems (MPI-SWS) in affiliation with Saarland University. Visiting research stints at Vrije Universiteit Amsterdam (2018) and Technical University of Munich (2024). Research Interests: Systems security, privacy, trusted execution environments (TEEs), network security, censorship resistance, AI security, and secure cloud computing. He has pioneered work on mitigating vulnerabilities in TrustZone-based TEEs and enhancing confidential computing. Publications: Over 30+ peer-reviewed articles in top-tier venues like S&P, USENIX Security, CCS, and NDSS. Recent trends focus on AI-driven security (e.g., automated exploit generation, prompt-to-SQL injections) and confidential computing (e.g., AMD SEV-SNP analysis). Awards: IST Outstanding Teaching Award (2019/2020), 2024 Prémio Científico Universidade de Lisboa/Caixa Geral de Depósitos. Advising & Grants: Supervised MSc theses in areas like AI-powered vishing attacks and confidential VMs. Active in conference organization (e.g., USENIX Security’25 Vice Chair, IEEE EuroSP’26 Co-Chair). Labs/Teams: Leads the SysSec team at INESC-ID Lisbon, collaborating on projects like AnyTEE (TEE framework) and FlowLens (network security tool).

Cuiyun Gaoمشاهده پروفایل

استاد

• Software Repository Mining
• Natural Language Processing
• Code Analysis
• +۵ مورد دیگر

Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.

Kevin Baumمشاهده پروفایل

پژوهشگر

• Explainable AI
• AI Ethics
• AI Fairness
• +۴ مورد دیگر

Kevin Baum is a computer scientist currently serving as the deputy head of the Neuro-Mechanistic Modelling (NMM) department at the German Research Center for Artificial Intelligence (DFKI) since January 2023, and head of the Centre for European Research in Trusted AI (CERTAIN) at DFKI since December 2023. Based at the Saarland Informatics Campus in Saarbrücken, Germany, he completed his doctorate in philosophy in March 2024, combining technical expertise with philosophical depth. His work bridges computer science with ethics, focusing on making AI systems transparent and accountable to human users. Dr. Baum's research program centers on interdisciplinary questions concerning the explainability and transparency of AI systems. His work spans multiple significant projects including the Explainable Intelligent System (EIS) initiative and project E7 of the Transregional Collaborative Research Centre 248 "Foundations of Perspicuous Software Systems" (CPEC). He has developed frameworks for understanding stakeholder perspectives on explainable AI and investigated how different information types about automated systems affect user perceptions of fairness and justice. His approach consistently combines theoretical foundations with practical implementations across diverse contexts. Analysis of his publication trends reveals a clear trajectory from theoretical foundations in machine ethics toward practical implementations of explainability requirements in real-world contexts. His work demonstrates increasing focus on human oversight effectiveness, fairness monitoring, and ethical considerations across various AI applications. He has made significant contributions to both academic discourse and practical AI development guidelines, with publications spanning computer science, philosophy, psychology, and human-computer interaction venues. Award for Ethics for Nerds lecture series As a research leader, Dr. Baum contributes to shaping AI development practices through his departmental leadership and interdisciplinary collaborations. His current work with CERTAIN focuses on establishing European research standards for trusted AI development and deployment, emphasizing the practical implementation of ethical requirements in AI systems. He maintains active collaborations across multiple institutions and disciplines, reflecting his commitment to bridging technical and philosophical considerations in AI development. At DFKI, he leads research that combines neuroscientific insights with AI development to create more interpretable systems. The NMM department focuses on both theoretical research on explainable AI foundations and practical applications in various domains, with particular attention to how different stakeholders understand and require explanations from AI systems.

Xin Pengمشاهده پروفایل

استاد

• Software Analytics
• Intelligent Software Development
• Microservice
• +۴ مورد دیگر

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.

Shuvendu K. Lahiriمشاهده پروفایل

پژوهشگر

• Formal Verification
• Smart Contracts
• Program Synthesis
• +۵ مورد دیگر

Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.

Jie M. Zhangمشاهده پروفایل

استادیار

• Software Testing
• AI for Software Engineering
• Software Engineering for AI
• +۵ مورد دیگر

Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.

Shin Yooمشاهده پروفایل

استاد

• Software Testing
• Debugging
• Search-based Software Engineering
• +۷ مورد دیگر

Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.