Find Academic Supervisors

Chanchal K. RoyView profile

Professor

• Code Clone Detection and Analysis
• Software Evolution and Maintenance
• Software Analytics Research
• +3 more

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.

Koushik SenView profile

Professor

• Software Engineering
• Programming Languages
• Formal Methods

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.

Miryung KimView profile

Professor

• Software Engineering
• Software Engineering for Data Intensive Computing
• Automated Testing
• +7 more

Miryung Kim is a Professor and Vice Chair of Graduate Studies in UCLA's Computer Science Department, where she directs the Software Engineering and Analysis Laboratory. She is renowned for her pioneering work in software evolution, code clone management, and establishing the emerging field of Software Engineering for Data Intensive Computing (SE4DA and SE4ML). Her research focuses on automated testing and debugging for Apache Spark, developer tools for heterogeneous computing, and conducting systematic studies of refactoring practices in industry. She led the first large-scale study of data scientists in industry and developed JDebloat, a Java bytecode debloating tool that made significant tech transfer impact to the Navy. Her recent publications demonstrate strong trends in fuzz testing for big data analytics and heterogeneous computing, with a focus on natural input generation, co-dependence awareness, and leveraging hardware probes for acceleration. Her work bridges software engineering with data-intensive and heterogeneous computing paradigms. ACM SIGSOFT Influential Educator Award (2022) ICSME Most Influential Paper Award (2023 and 2020) NSF CAREER award Google Faculty Research Award Okawa Foundation Research Award Humboldt Fellow ACM Distinguished Member As an academic advisor, she has produced eight tenure-track faculty members at institutions including Columbia, Purdue, and Virginia Tech. Her research has been supported by National Science Foundation, Air Force Research Laboratory, Google, IBM, Intel, Okawa Foundation, Samsung, and Office of Naval Research. She previously served as Program Co-Chair of ESEC/FSE 2022 and has delivered keynotes at ASE 2019 and ISSTA 2022. She maintains active industry collaborations, serving as an Amazon Scholar at Amazon Web Services and having spent time as a visiting researcher at Microsoft Research.

Baishakhi RayView profile

Associate Professor

• Software Engineering
• Security
• Artificial Intelligence
• +3 more

Baishakhi Ray is an Associate Professor of Computer Science at Columbia University, working at the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin, and has established herself as a leading researcher in applying artificial intelligence to software engineering challenges. Her educational background includes a Ph.D. from the University of Texas, Austin, which provided the foundation for her research career at the forefront of AI and software engineering. Dr. Ray's research focuses on leveraging artificial intelligence to solve fundamental challenges in software engineering and security. Her work spans multiple areas including code generation with large language models, vulnerability detection, software testing, and program analysis. She has pioneered approaches that combine deep learning with traditional software engineering techniques to create more robust, secure, and efficient software development processes. Her research has practical implications for improving code quality, enhancing software security, and accelerating development cycles through AI assistance. Her recent work demonstrates a strong emphasis on semantic-aware code generation, execution reasoning, and addressing hallucinations in code language models. She has also made significant contributions to evaluating the functionality and security of AI-generated code, identifying critical challenges in the practical adoption of AI for software development. Dr. Ray has received numerous prestigious awards recognizing her contributions to the field: IEEE TCSE Rising Star NSF CAREER award IBM faculty award VMware Faculty award Distinguished Paper awards at FSE'17, ASE'22, and ISSTA'23 ICSME Most Influential Paper award Publications featured in CACM Research Highlights As an Amazon Visiting Academic and active participant in major software engineering conferences, Dr. Ray has established herself as a thought leader in AI for software engineering. Her research has been widely covered in trade media, indicating its relevance and impact on industry practices. She has mentored numerous students through their research and has been instrumental in shaping the next generation of researchers in this interdisciplinary field. Her work demonstrates a consistent focus on bridging theoretical advances with practical applications, ensuring that her research has tangible benefits for the software development community. The trajectory of her publications shows an evolving research agenda that has successfully adapted to the rapidly changing landscape of AI and its applications to software engineering.

Jeff HuangView profile

Associate Professor

• Programming Languages
• Software Engineering
• Concurrency
• +1 more

Jeff Huang is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, specializing in programming languages and software engineering with a focus on concurrency and runtime verification. His research develops advanced program analysis techniques and tools to enhance software performance and reliability. Programming Languages Software Engineering Concurrency Runtime Verification His work has been recognized with prestigious awards including the ACM SIGSOFT Early Career Researcher Award, NSF CAREER Award, Google Faculty Research Award, and DARPA Young Faculty Award. Notably, his research has earned multiple SIGPLAN Research Highlights and PLDI Distinguished Paper Awards. ACM SIGSOFT Early Career Researcher Award NSF CAREER Award Google Faculty Research Award Mozilla Research Award Facebook Research Award DARPA Young Faculty Award ACM SIGSOFT Outstanding Dissertation Award ACM SIGPLAN PLDI Distinguished Paper Award SIGPLAN Research Highlights Jeff Huang actively contributes to academic communities as a committee member in venues like SPLASH, ICSE, ISSTA, and PLDI. He has authored influential papers on concurrency bug detection, pointer analysis, and language translation tools, spanning both theoretical foundations and practical implementations.

Jia LiView profile

Assistant Professor

• Programming Language Processing
• Code Understanding
• Code Generation
• +7 more

Jia Li is an Assistant Professor at the College of AI, Tsinghua University, where they lead the Tsinghua University Programming Language Processing Group (THU-PLP). They completed their PhD at Peking University in 2025 under the supervision of Prof. Zhi Jin and Prof. Ge Li. Dr. Li's research focuses on Programming Language Processing (PLP), which aims to develop artificial intelligence techniques for understanding and generating source code. Their work spans two main areas: foundation models for PLP and applications of PLP in software development and beyond. They develop new model architectures, training strategies, inference approaches, and evaluation metrics to improve code understanding and generation capabilities. Their application research explores how PLP can enhance software development efficiency through code generation, test generation, and code optimization, as well as its applications in embodied AI and neuroscience. Dr. Li's recent publications demonstrate a strong focus on advancing code generation and understanding through large language models. Their work addresses key challenges in repository-level code completion, class-level code translation, vulnerability detection, and benchmarking evolving code generation capabilities. They've made significant contributions to developing efficient models like aiXcoder-7B and creating comprehensive benchmarks like EvoCodeBench and ClassEval-T. NeurIPS 2025 Spotlight Paper (3.2% acceptance rate) for "SATURN: SAT-based Reinforcement Learning to Unleash Language Model Reasoning" Dr. Li actively mentors students and researchers, seeking highly-motivated interns to join the THU-PLP research group. They have established collaborations with researchers at Peking University, as evidenced by their joint publications with supervisors Prof. Zhi Jin and Prof. Ge Li. Dr. Li leads the Tsinghua University Programming Language Processing Group (THU-PLP), which focuses on cutting-edge research at the intersection of programming languages and artificial intelligence. The group maintains active GitHub repositories for their research projects, including EvoCodeBench, SkCoder, and CodeEditor, demonstrating their commitment to open science and reproducible research.

Tobias GlasmachersView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +6 more

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Alessandra GorlaView profile

Associate Professor

• Malware detection for mobile applications
• Automatic software repair
• Software testing
• +4 more

Alessandra Gorla is an associate researcher professor at IMDEA Software Institute in Madrid, Spain, with a strong background in software engineering research. She previously worked as a postdoctoral researcher with Andreas Zeller at Saarland University in Germany and completed her PhD under Mauro Pezzè at the University of Lugano in Switzerland. Her research bridges theoretical foundations with practical applications in mobile software systems. Her research focuses on malware detection for mobile applications, automatic software repair, software testing and analysis. She has developed techniques for detecting behavior anomalies in graphical user interfaces, identifying third-party libraries in mobile apps, and leveraging intrinsic software redundancy for reliability. Her work spans both Android and iOS ecosystems, with particular attention to permission systems, release practices, and security implications. Analysis of her recent publications reveals a strong trend toward mobile application security and analysis, with increasing focus on iOS systems alongside traditional Android research. Her work combines static and dynamic analysis techniques, often incorporating natural language processing for comment analysis and test generation. There's a clear progression from foundational work on intrinsic software redundancy to more applied research on mobile security and testing. FRITZ-KUTTER AWARD! for PhD thesis on Automatic Workarounds BEST PAPER AWARD! for Search-based Security Testing of Web Applications BEST STUDENT POSTER AWARD! for Automatic Workarounds as Failure Recoveries Dr. Gorla actively mentors students and seeks motivated individuals for internship and PhD opportunities in software engineering. She has served in various organizational roles including Tool Demonstrations co-chair for FSE 2016, Artifact Evaluation co-chair for ESSoS 2016 and ISSTA 2016, and multiple program committee positions at top software engineering conferences. Her work has been supported through collaborations with major research institutions and industry partners. At IMDEA Software Institute, Dr. Gorla leads research on mobile application analysis, particularly focusing on behavioral analysis of Android and iOS applications. Her CHABADA prototype for clustering Android apps by description topics and identifying API usage outliers demonstrates her practical approach to malware detection. She also investigates intrinsic software redundancy for building more resilient systems.

Ying ZouView profile

Professor

• Software Engineering
• Artificial Intelligence for Software Engineering
• Software Evolution
• +2 more

Dr. Ying Zou is a Professor in the Department of Electrical and Computer Engineering at Queen's University's Smith Engineering faculty in Kingston, Ontario, Canada. With an extensive publication record spanning from 2018 through 2025, Dr. Zou has established herself as a leading researcher in empirical software engineering with a growing focus on AI integration. Dr. Zou's research focuses on Software Engineering , Artificial Intelligence for Software Engineering (AI4SE) , Software Evolution , Software Analytics , and Empirical Software Engineering . Her work bridges theoretical approaches with practical applications, examining developer behavior, code quality improvement, and AI techniques for software engineering tasks. Recent publications demonstrate a clear progression from traditional empirical studies toward more AI-centric approaches, particularly in code refactoring, type inference, and performance analysis. Analysis of Dr. Zou's publication trends reveals a strategic evolution in her research focus. Early work centered on empirical studies of Stack Overflow and GitHub, while recent publications increasingly integrate large language models and AI techniques for software engineering tasks. Her research spans multiple dimensions including code quality, developer productivity, open source community dynamics, and performance optimization, with consistent methodological rigor in empirical validation. Dr. Zou has served in numerous leadership roles across major software engineering conferences including ASE, ICSE, and ESEC/FSE. She has been a Program Committee member for multiple tracks and conferences, and notably served as New Faculty Mentoring Co-Chair for ESEC/FSE 2026. Her service to the community extends to organizing conference tracks, chairing sessions, and mentoring new researchers in the field.

Thomas LeichView profile

Professor

• Software Product Line Engineering
• Requirements Engineering
• IT Security
• +11 more

Prof. Dr.-Ing. Thomas Leich holds the Volkswagen Financial Services Endowed Professorship for Business Informatics, specifically Requirements Engineering, at Harz University of Applied Sciences. He is affiliated with the Faculty of Automation and Information, focusing on software variability and configurable systems. University: Harz University of Applied Sciences School: Faculty of Automation and Information Academic Rank: Professor Research Interests span software product line engineering, IT security, Industry 4.0, and feature-oriented programming. His work bridges academic innovation with industrial applications, particularly in secure and scalable systems for automotive and enterprise contexts. Recent Publications emphasize security in configurable systems, empirical software engineering studies, and automotive platform management. Articles often involve collaborations with teams at Otto von Guericke University and international institutions. Scientific Awards include the Hugo Junkers Research Award (2013), the Wissenschaftspreis der Wernigeröder Stadtwerkestiftung (2017), and the 2012 Faculty Research Award from the University of Magdeburg. Supervision of over 20 theses since 2008, spanning topics in ERP security, program comprehension, and embedded systems. He leads the EXPLANT project, funded by DFG, and contributes to FeatureIDE , an extensible framework for feature-oriented development.

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

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

Earl T. BarrView profile

Professor

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

Earl T. Barr is a Professor of Software Engineering at University College London (UCL), where he heads the System Software Engineering Group and is a member of the Centre for Research on Evolution, Search and Testing (CREST). He received his Ph.D. in Computer Science from the University of California, Davis in 2009. His educational background includes: Ph.D. in Computer Science, 2009, University of California, Davis Barr's research focuses on software engineering, particularly program analysis, automated program repair, and the application of machine learning to code (AI4Code). He is renowned for his work on dual channel analysis, which examines how code combines natural language elements (in identifiers, comments, and stylistic choices) with formal programming language. His research also explores game theory applications to software development processes and how dual channel constraints can improve type inference and code understanding. His recent publications demonstrate a strong trend toward leveraging large language models for code understanding and modification, with particular emphasis on dual channel constraints and natural type inference. His work bridges traditional software engineering techniques with modern AI approaches, showing how machine learning can enhance program analysis and repair while maintaining rigorous theoretical foundations. Notable awards include: ACM SIGSOFT Distinguished Paper Award for Automated Software Transplantation (2015) ACM SIGSOFT Distinguished Paper Award for Learning Natural Coding Conventions (2014) ACM SIGSOFT Distinguished Paper Award for Collecting a Heap of Shapes (2013) Best Paper Award for TrustDavis: A Non-Exploitable Online Reputation System (2005) Barr actively supervises numerous PhD students and postdocs, with current research focusing on AI for code, software security, and program analysis. He collaborates extensively with Santanu Dash of Royal Holloway on dual channel program analysis, and they jointly supervise PhD students through the EPSRC Centre for Doctoral Training in Cyber Security for the Everyday. His work often combines theoretical rigor with practical tool development, resulting in several publicly available research tools that influence both academic research and industry practice. He leads the System Software Engineering Group at UCL, which focuses on developing novel approaches to software engineering challenges at the intersection of human and machine aspects of programming. Current research directions include dual channel analysis for vulnerability detection, game-theoretic approaches to development processes, and AI-assisted software development.

Jean-Rémy FalleriView profile

Professor

• Software Engineering
• Software Evolution
• Source Code Differencing
• +3 more

Jean-Rémy Falleri is a Full Professor of Computer Science at Enseirb-Matmeca (Bordeaux INP) and a researcher at LaBRI laboratory in Bordeaux, France. He serves as head of the Computer Science department at Enseirb-Matmeca and co-head of the Systems and Data department at LaBRI. From 2020 to 2025, he was appointed as a junior member (later honorary member) of the prestigious Institut Universitaire de France. His educational background includes a habilitation from Université de Bordeaux (2015), a PhD from Université Montpellier 2, and a Master's degree from IMT Mines Alès (formerly École des Mines d'Alès). He also completed post-doctoral research at INRIA Lille in the RMoD group. Falleri's research focuses on software engineering, particularly software evolution and the development of practical tools to analyze and understand code changes. His work bridges theoretical research with practical applications, resulting in tools like GumTree for visualizing code differences and Roseau for detecting breaking changes in libraries. His research interests span source code differencing, API analysis, breaking change detection, and software maintenance techniques. His publication record shows consistent contributions to top software engineering conferences including ICSE, ASE, ICSME, and FSE, with research themes evolving from code clone detection and developer expertise extraction to modern challenges in API evolution and Docker configuration analysis. Junior Member of Institut Universitaire de France (2020-2025) Honorary Member of Institut Universitaire de France (2020-2025) Falleri has supervised numerous Master's students, PhD candidates, and post-doctoral researchers throughout his career. He has held significant service roles including Head of LaBRI's Systems and Data department (since 2021), Head of LaBRI's Software Engineering group (2015-2021), and member of various conference program committees. His work has practical impact through actively maintained tools that address real challenges in software development and evolution.

Chao NiView profile

Associate Professor

• Software Quality Assurance
• Mining Software Repositories
• Software Security
• +3 more

Chao Ni is an active Associate Professor at the School of Software Technology, Zhejiang University, specializing in software engineering with a focus on software quality assurance and mining software repositories. His research spans multiple key areas including vulnerability detection, defect prediction, automated program repair, and software security. He has consistently published in top-tier software engineering conferences such as ASE, ICSE, ESEC/FSE, and ISSTA, demonstrating significant contributions to the field. Ni also serves on program committees for major conferences, indicating recognition by his peers in the software engineering community. Dr. Ni's research interests center around Software Quality Assurance and Mining Software Repositories , with particular emphasis on vulnerability detection, defect prediction, and automated program repair. His work often combines traditional software engineering approaches with machine learning techniques to address challenging problems in software development and maintenance. Recent publications show a strong focus on security aspects of software engineering, including smart contract security, JavaScript breaking changes, and vulnerability detection in C/C++ code. Analysis of Dr. Ni's recent publications reveals a consistent research trajectory focused on improving software quality through advanced analysis techniques. His work spans multiple subdomains including vulnerability detection (particularly in smart contracts and C/C++ code), defect prediction (especially just-in-time approaches), and automated program repair. A notable trend is his increasing integration of machine learning techniques with traditional software engineering methods, creating hybrid approaches that leverage both semantic features and expert knowledge. His research has practical applications in software security, maintenance, and testing. Dr. Ni has served on program committees for numerous prestigious conferences including APSEC, ASE, ESEC/FSE, and Mining Software Repositories. His service as a committee member across multiple tracks (Technical Track, Research Papers, Data and Tool Showcase) demonstrates his broad expertise and recognition in the software engineering community. His GitHub profile (jacknichao) shows active engagement with open source projects related to his research, particularly in vulnerability detection tools.

Lingxiao JiangView profile

Associate Professor

• Software Engineering
• Program Analysis
• Software Testing & Debugging
• +5 more

Lingxiao Jiang is an Associate Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU) in Singapore. His research and teaching interests focus on software engineering, program analysis, testing & debugging, code search & reuse, and deep learning & analysis of code. He is actively involved in multiple research tracks across major software engineering conferences including ASE, ICSE, ESEC/FSE, and MSR. His research interests encompass practical techniques and tools that enhance software quality, increase development productivity, reduce maintenance cost, and improve user experience. He also explores programming languages, systems, security & privacy, data mining, and machine learning to solve software engineering and security problems. His work focuses on context-aware software mining, analysis, and deep learning, with applications in code clone detection, code query processing, bug detection, search for bug fixes, refactoring, and testing & debugging techniques. Professor Jiang's research involves large-scale, diverse, contextual data sources beyond program code, including code change histories, bug databases, test suites, developer activities, user feedbacks, and socio-technical information. His methodology leverages static & dynamic program analysis, code representations, software engineering methodologies, natural language processing, information retrieval, data mining, deep learning, and distributed computing techniques. His research has resulted in numerous publications with significant impact, including the DECKARD tool which received the ACM SIGSOFT Impact Paper Award in 2018. His work spans various areas including scalable code clone detection, deep learning of code, and analysis of software repositories. Scientific Awards: ACM SIGSOFT Impact Paper Award 2018 for DECKARD paper ICSE '18 Distinguished Paper Award for Hierarchical learning of cross-language mappings Professor Jiang actively advises graduate students and has mentored numerous PhD and Master's students to completion. He also leads the Centre for Research on Intelligent Software Engineering (RISE) at SMU, which focuses on high-quality and impactful research in intelligent software engineering. His research has practical applications in various domains including Android security, smart contracts, drone security, and compiler testing.