Find Academic Supervisors

Jens PalsbergView profile

Professor

• Programming Languages
• Quantum Computing
• Software Engineering
• +3 more

Jens Palsberg is a Professor and former Department Chair of Computer Science at the University of California, Los Angeles (UCLA), where he currently serves as Director of the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence and co-director of UCLA's quantum research center. He chairs ACM SIGPLAN and is a member of the ACM Council. His research spans programming languages, software engineering, quantum computing, compilers, embedded systems, and information security. Palsberg has authored over 80 technical papers, co-authored the book Object-Oriented Type Systems , and revised Appel's textbook on Modern Compiler Implementation in Java . His recent work shows a significant shift toward quantum computing, including compiler techniques and program analysis for quantum systems. Analysis of his recent publications reveals a clear transition from traditional programming language research to quantum computing, with nearly half of his 2022-2024 publications focusing on quantum topics while maintaining strong work in software engineering and programming languages. His quantum research particularly emphasizes compiler optimization, abstract interpretation, and circuit analysis. ACM SIGPLAN Distinguished Service Award (2012) UCLA teaching award for quantum computing courses (2023) National Science Foundation CAREER and ITR awards Purdue University Faculty Scholar award IBM Faculty Award Okawa Foundation research award Palsberg has served in numerous leadership roles including general chair of POPL, conference chair of LICS, and vice chair of ACM SIGBED. His research has been supported by DARPA, Intel, British Telecom, and the National Science Foundation. He was instrumental in establishing UCLA's Masters degree in quantum science and has mentored numerous students through his legendary proof sessions. He leads a research group of over 30 professors in UCLA's quantum research center and maintains active collaborations across academia and industry, particularly with Amazon through the UCLA-Amazon Science Hub.

Lars GrunskeView profile

Professor

• Software Engineering
• Formal Methods
• Model Checking
• +5 more

Lars Grunske is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. His research focuses on software and systems engineering, safety-critical systems, and software evolution. Department: Computer Science University: Humboldt University of Berlin Academic Rank: Professor His work spans automated software analysis, probabilistic model checking, and formal methods for complex systems. Key collaborations include researchers from Swinburne University, University of Hull, and University of Queensland. Recent publications address research software engineering, program repair, and explainability in cyberphysical systems. Professional roles include leadership in examination boards and program committees for conferences like ICSE and ASE. Contact info: Email: grunskel@hu-berlin.de Phone: +49 30 2093-41142 Address: Unter den Linden 6, Berlin

Katja GelbrichView profile

Professor

• Automated Agents in Marketing
• Digital Services and Assistants
• Cross-Cultural Marketing
• +3 more

Prof. Katja Gelbrich is a Full Professor of International Management at the Catholic University of Eichstätt-Ingolstadt, leading the Department of Business Administration and International Management. She coordinates academic programs including the Bachelor's in Management & Marketing, a Master's Double Degree with Toulouse Business School, and doctoral studies. Educational Background: PhD in Marketing from University of Stuttgart (2001–2006) Postdoc in Business Administration at University of Stuttgart (2002–2006) Diploma in Business Administration from TU Dresden (1995–1997) Undergraduate studies in Business, Korean and Japanese Studies at Humboldt University Berlin (1990–1995) Research Focus: Her work centers on automated agents in marketing, digital services, cross-cultural consumer behavior, and service recovery strategies. Key areas include robotic customer service agents, virtual companions in e-commerce, and cross-national product recall management. Awards & Recognition: 2023 Best Conference Paper Award (Hanoi) 2022 Best Paper in Track Award (Glasgow) 2015 Teaching Excellence Award (Eichstätt) 2005 Textbook Award for Intercultural Marketing Professional Experience: Prior roles include Full Professor at TU Ilmenau (2006–2011) and managing a market research firm (2001–2006). She has held leadership positions in data mining at DaimlerChrysler and rejected multiple chaired professorships at other universities.

Claire Le GouesView profile

Professor

• Software Engineering
• Program Analysis
• Defect Repair
• +5 more

Claire Le Goues is a Professor of Computer Science at Carnegie Mellon University, primarily affiliated with the Software and Societal Systems Department (S3D) within the School of Computer Science (SCS). She serves as the Associate Department Head for Faculty within S3D and leads the squaresLab research group. Le Goues also co-directs the REUSE@CMU summer program and teaches software engineering and program analysis at undergraduate, master's, and PhD levels. Her research spans software engineering and programming languages, with a particular focus on how to construct, maintain, evolve, improve/debug, and assure high-quality software systems. Le Goues has made significant contributions to automated program repair, program analysis, and defect detection. Her work often bridges theoretical foundations with practical applications, addressing real-world challenges in software development and maintenance. Le Goues' recent publications demonstrate a clear trend toward integrating large language models and generative AI with traditional software engineering techniques. Her research examines how these technologies can enhance program repair (BatFix, AdverIntent-Agent), vulnerability detection (Interpretable Vulnerability Detection Reports), and testing (LWDIFF for WebAssembly). This represents an evolution from her earlier foundational work in program repair (GenProg) toward leveraging contemporary AI advancements. She has mentored numerous students through her squaresLab research group and has been instrumental in developing educational programs that prepare the next generation of software engineers. Le Goues is also known for her advocacy for double-blind review processes in academic conferences, having implemented this approach when co-chairing the Symposium for Search-Based Software Engineering in 2014.

Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Michael WehmöllerView profile

Professor

• Dental Technologies
• Medical Device Innovation
• Biomechanics
• +5 more

Prof. Dr.-Ing. Michael Wehmöller is a full Professor at the Faculty of Engineering and Computer Science, Osnabrück University of Applied Sciences. His career spans roles as a technical director in medical device companies and interdisciplinary research leadership in DFG/EU-funded programs, integrating CAD/CAM, additive manufacturing, and biomechanics. Research interests focus on dental technology , medical device innovation , biomechanics , biomaterials , and 3D printing . His work bridges engineering precision with clinical applications , particularly in craniofacial reconstruction and selective laser melting for implants. The 15 most recent publications highlight trends in computer-aided implant design biodegradable materials testing automated surgical planning with a strong emphasis on clinical translation of engineering solutions. Scientific recognition includes Innovationspreis NRW (1996) Innovationspreis Ruhrgebiet (2002) and ongoing membership in VDI medical technology panels . He leads the Digitale Dentale Technologien lab, focusing on next-generation implant systems.

Ying WangView profile

Associate Professor

• Dependency management
• Software ecosystem
• Software refactoring
• +7 more

Dr. Ying Wang is an Associate Professor and doctoral supervisor at the Software College of Northeastern University (China), where she has been working since February 2019. She serves as Assistant Dean at the School of Software and is an active member of several CCF committees including the System Software Committee, Software Engineering Committee, Open Source Development Committee, and Women's Committee. Dr. Wang received her Ph.D. in Software Engineering from Northeastern University in January 2019 under the supervision of Professor Zhiliang Zhu. She completed postdoctoral research at the Hong Kong University of Science and Technology (HKUST) from 2022 to 2023 under Professor Shing-Chi Cheung and was a visiting scholar at Microsoft Research Asia through the StarTrack Program in 2021. Her research focuses on intelligent software development technologies, large AI models, open source software big data analysis, and software supply chain security. She has made significant contributions to the governance of open source software ecosystems across multiple programming languages including Java, C#, Python, Go, JavaScript, Android, and Rust. Her work has led to the development of practical tools like 'League of Legends' for monitoring dependency defects in open source ecosystems, with several technologies commercialized by Huawei and Microsoft. Dr. Wang's recent publications demonstrate her expertise in cross-language dependencies, software component analysis, software refactoring, and the application of large language models in software engineering. Her work spans both theoretical foundations and practical applications, with a strong emphasis on real-world impact through industry collaboration. Among her notable achievements are the ACM SIGSOFT Distinguished Paper Awards at ICSE 2021 and ESEC/FSE 2023, making her the first researcher from Northeastern University to receive this honor. She has also received multiple awards for her doctoral dissertation and prototype implementations. Dr. Wang actively contributes to the academic community as an Associate Editor for IEEE Transactions on Software Engineering and serves on program committees for top conferences including ASE, ICSE, and ESEC/FSE. She mentors a large group of doctoral and master's students, with many alumni securing positions at major technology companies including Huawei, Microsoft, Alibaba, and Tencent.

Rui AbreuView profile

Professor

• Software Engineering
• Software Testing
• Debugging
• +6 more

Rui Abreu is a Professor at the Faculty of Engineering of the University of Porto (FEUP), Portugal, with extensive expertise in software quality, testing, and debugging. Previously, he served as Associate Professor at IST-ULisbon and Assistant Professor at the University of Porto. His research bridges academia and industry through roles including Visiting Researcher at Google NYC (2019-2020) and co-founding DashDash, a $9M Series A-funded startup for spreadsheet-based web app development. His educational background includes a Ph.D. in Computer Science - Software Engineering from Delft University of Technology and an M.Sc. in Computer and Systems Engineering from the University of Minho. His research focuses on automating software testing and debugging , with growing emphasis on quantum software testing, vulnerability detection, and AI-assisted development tools. Recent work explores large language models for loop invariant generation, interpretable vulnerability reports, and quantum mutation testing. His publication trends reveal a strong shift toward security-critical systems and emerging computing paradigms , with 30% of recent papers addressing quantum software challenges and 45% focusing on vulnerability detection/repair. The work consistently combines static/dynamic analysis with machine learning, targeting practical tool development for real-world engineering problems. 6 Best Paper Awards Distinguished Paper Award at ESEC/FSE 2019 Abreu actively mentors through conference committees (serving on 12+ program committees in 2024-2025) and industry engagement. His DashDash venture demonstrates successful technology transfer, while Google collaboration advanced C/C++ security tooling. Current work includes quantum software metrics and security commit standardization. He leads research teams focused on software quality automation, with recent projects including GZoltarAction (GitHub fault localization bot) and Maestro (vulnerability repair benchmarking platform). Future directions emphasize scalable security analysis for quantum systems and human-AI collaboration in debugging workflows.

David LoView profile

Professor

• AI for Software Engineering
• Mining Software Repositories
• Data Mining
• +4 more

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.

Xiaofei XieView profile

Assistant Professor

• Program Analysis
• Software Testing
• Vulnerability Detection
• +4 more

Xiaofei Xie is an Assistant Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he has been employed since 2022. Prior to this position, he was a postdoctoral researcher at Nanyang Technological University in Singapore from 2018 to 2021. His research primarily focuses on program analysis, software testing, vulnerability detection, and quality assurance of AI systems. SMU is ranked No. 9 (No. 5 in Asia) in the Software Engineering category according to CSRankings. Dr. Xie's research interests span multiple critical areas in software engineering and AI systems. His work on program analysis includes detecting non-termination bugs and developing practical methods like EndWatch for real-world software. In software testing, he has made significant contributions to deep learning systems testing, autonomous driving systems testing, and smart contract security. His research on vulnerability detection encompasses various aspects of AI security, including backdoor attacks, adversarial examples, and security testing for web-based deep learning frameworks. His quality assurance work for AI systems includes developing metrics for robustness evaluation and creating testing methodologies for diverse AI applications. Dr. Xie's publication record shows a strong trend toward integrating large language models with traditional software engineering techniques. His recent work demonstrates increasing focus on testing autonomous systems, securing AI models, and applying advanced machine learning techniques to traditional software engineering problems. The research spans multiple domains including deep learning frameworks, smart contracts, autonomous driving systems, and federated learning environments. Among his notable achievements are multiple ACM SIGSOFT Distinguished Paper Awards (ASE 2019, ASE 2023, ISSTA 2022), the ACM Tianjin Doctoral Dissertation Award 2019, and the Best Paper Award at APSEC 2020. His work has been accepted to top-tier conferences including ICSE, FSE, ASE, ISSTA, and security venues like USENIX Security. Dr. Xie actively serves the academic community as a PC co-chair for ICECCS 2025 and as a program committee member for numerous prestigious conferences including ICSE, FSE, ASE, ISSTA, and AAAI. He has also organized workshops such as the Workshop on AI and Software Testing/Analysis (AISTA) and served as Guest Editor for special issues on AI security. His service demonstrates leadership in bridging software engineering with AI and security research communities.

Yi LiView profile

Associate Professor

• Software Engineering
• Program Analysis
• Automated Reasoning
• +2 more

Yi Li is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He leads the Software Reliability and Security Lab (SRSLab@NTU) which focuses on program analysis and automated reasoning techniques for software engineering and security applications. Dr. Li received his BComp degree in Computer Science from the National University of Singapore in 2011, followed by MSc (2013) and PhD (2018) degrees in Computer Science from the University of Toronto. His educational background has positioned him at the forefront of software reliability research. His research interests span software engineering, program analysis, automated reasoning, and formal methods, with particular focus on software reliability, security, and smart contract analysis. Dr. Li's work develops practical solutions for constructing high-quality software systems that are both reliable and sustainable. His research has identified critical issues in API documentation, smart contract security, program termination, and library compatibility across multiple programming ecosystems. Dr. Li's publications demonstrate consistent contributions to software engineering research over the past five years, with recent work focusing on smart contract analysis, API documentation verification, and program termination detection. His research methodology often combines static and dynamic analysis techniques with machine learning approaches to address challenging problems in software reliability and security. Five ACM Distinguished Paper Awards at top conferences Two Best Artifact Awards recognizing research reproducibility Publications at premier venues including ASE, ISSTA, FSE, and ICSE Dr. Li actively contributes to the software engineering community through service on program committees of flagship conferences (ICSE, FSE, ASE, ISSTA) and co-chairing program committees for ICFEM'23, ICECCS'20, SEAIS'22, and ICFEM'19 Doctoral Symposium. His lab, SRSLab@NTU, has developed multiple influential tools including InvCon, DocCon, and EndWatch, while fostering research in software reliability and security for emerging technologies.

Zhi JinView profile

Professor

• Requirements Engineering
• Self-Adaptive Systems
• Learning from Source Code
• +5 more

Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.

Reyhaneh JabbarvandView profile

Assistant Professor

• Neural Program Analysis
• Software Testing
• Bug Localization
• +4 more

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Intelligent CAT Lab. Her research focuses on improving software quality, reliability, and maintenance through neuro-symbolic approaches that combine AI techniques with formal methods. Her research interests span Neural Program Analysis, Software Testing (with emphasis on mobile apps and autonomous software), Bug Localization, and Applied Optimization for Software Analysis. She has made significant contributions to the fields of energy testing for Android applications, neuro-symbolic approaches for code analysis, and large language models for software engineering tasks. Dr. Jabbarvand's recent publications reveal strong trends in applying machine learning to software engineering problems, particularly using neuro-symbolic methods to bridge the gap between deep learning and formal program analysis. Her work on code translation, test flakiness, and test oracle generation demonstrates her focus on practical applications of AI in software development workflows. Google PhD Fellowship in Programming Technology and Software Engineering Rising Star in EECS NSF CAREER Award Dr. Jabbarvand has received research funding from multiple sources including NSF, IBM Research, and C3.ai. She actively mentors students through her Intelligent CAT Lab and has served on numerous program committees for major software engineering conferences including ICSE, FSE, and ISSTA. She teaches courses on Advanced Topics in Software Engineering, ML for Code, and Software Engineering I. Her lab focuses on neuro-symbolic approaches to software engineering problems, bringing together PhD, undergraduate, and high school students to tackle challenges in AI-assisted software development and testing.

• Digitized Building Maintenance
• Building Information Modeling (BIM)
• Automation and Robotics in Construction
• +3 more

Dr. Hendrik Morgenstern serves as a Postdoc and Senior Engineer at RWTH Aachen University's Chair and Institute of Construction Management, Digital Engineering and Robotics in Construction (ICoM), where he advances digital solutions for building maintenance and construction robotics. His work focuses on integrating Building Information Modeling (BIM) with diagnostic data to optimize infrastructure lifecycle management. Morgenstern earned his B.Sc. and M.Sc. in Civil Engineering with specialization in Functional and Structural Engineering from Karlsruhe Institute of Technology (KIT), complemented by studies in Sustainable Development. He completed his Dr.-Ing. doctorate at RWTH Aachen in 2023 with research on automated maintenance planning using BIM-enriched diagnostic data. His research program centers on digitized building maintenance, BIM applications for existing structures, and robotics automation in construction. Key contributions include predictive maintenance frameworks using Bayesian inference, geopolymer material development for structural repair, and point cloud integration for as-built modeling. He emphasizes resource efficiency and data-driven decision-making across all projects. Analysis of his 15 publications (2021-2025) reveals three dominant trends: (1) Convergence of BIM with non-destructive diagnostics for predictive maintenance, (2) Development of smart materials like temperature-stable geopolymers for crack injection, and (3) Integration of robotics and AI for automated facility management. His work consistently bridges civil engineering fundamentals with computer science innovations. Morgenstern actively contributes to major research initiatives including the RoboTUNN project (awarded bauma Innovation Award 2025 for tunneling robotics) and the BIM4People consortium focused on digital transformation in construction. His work demonstrates strong industry collaboration through projects with German construction firms and participation in standards development.

• Software Security
• Software Testing
• Program Analysis
• +2 more

Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing