Mahmoud Alfadel is an Assistant Professor at the University of Calgary, Canada, actively contributing to software engineering research through program committee roles at ASE, ICSE, and ESEC/FSE conferences from 2021-2025. His research centers on Software Ecosystems, Release Engineering, and Empirical Software Engineering, with specific focus on build systems, continuous integration pipelines, dependency management, and software quality metrics in open-source environments. Methodologically, he employs large-scale empirical studies of real-world development practices. Analysis of his 2021-2025 publications reveals consistent investigation into build technology evolution (particularly Bazel), dependency-induced waste in NPM, and testing practices like fuzzing adoption. His work bridges theoretical software engineering concepts with practical industry challenges, often through case studies of major open-source projects like Kubernetes.
Dr. Hai Dong is a Senior Lecturer at the School of Computing Technologies, RMIT University in Melbourne, Australia, with promotion to Associate Professor scheduled for 2026. He serves as the Founding Director of the CloudTech-RMIT Green Cryptocurrency Joint Research Laboratory (GreenCryptoLab) and Leader of the Smart Sensing and Services Research Area. Previously, he held research fellow positions at both RMIT University and Curtin University. Dr. Dong is a Senior Member of IEEE and chairs the IEEE Computational Intelligence Society Task Force on Deep Edge Intelligence. Dr. Dong's research spans several cutting-edge domains including Service-Oriented Computing, Edge Intelligence, Blockchain, AI Security, Cyber Security, and Machine Learning. His work bridges theoretical foundations with practical applications, particularly in secure and efficient computing systems. He has developed innovative approaches for smart contract security, edge computing optimization, federated learning, and blockchain applications with a strong focus on sustainability and real-world impact. His publication record demonstrates consistent high-impact contributions across top venues including AAAI, ASE, ICML, TSE, and TSC. Dr. Dong's research shows a clear trajectory toward increasingly sophisticated integration of AI with edge computing and blockchain systems, with growing emphasis on security, privacy, and resource efficiency. Recent work highlights his leadership in addressing emerging challenges in LLM-generated smart contracts and secure federated learning systems. Best Research Paper Award at ICSOC 2016 Best Paper Award at IEEE ICBC 2025 2023 RMIT Award for Research Engagement and Impact - Industry Engagement in Graduate Research Dr. Dong has successfully supervised numerous PhD and Master's students to completion, with many going on to prestigious positions. He has secured over $5 million in research funding as Chief Investigator from sources including ARC, CRC, QNRF, and industry partners like ANZ, CloudTech, and Telstra. His GreenCryptoLab research facility represents a significant industry-academic partnership focused on sustainable blockchain technologies. Dr. Dong maintains active collaborations with researchers worldwide and serves on committees for over 100 international conferences.
Lingling Fan is an Associate Professor (100 Young Academic Leaders of Nankai University) at Nankai University, China. Her research focuses on software security analysis, software testing and analysis, and big data-driven analysis, with significant contributions to mobile application security, particularly in Android security and accessibility. Her research interests include: Software Security Analysis, with emphasis on mobile application security and vulnerability detection Software Testing and Analysis, particularly for Android applications and accessibility issues Big Data-driven Analysis for security and quality assessment of software systems Dr. Fan's publication record shows strong trends in automated security testing, vulnerability detection in open-source ecosystems, and accessibility analysis for mobile applications. Her work spans multiple disciplines including software engineering, security, and human-computer interaction, with a particular focus on practical applications for Android ecosystem and measurable real-world impact. Her notable scientific awards include: ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICSE 2018) Research Tool Award at NASAC 2018 National Scholarship from The Ministry of Education, China (2018) ACM SIGSOFT CAPS Award (ASE 2018) Dr. Fan has served in various academic service roles including as a program committee member for major conferences such as ASE, ICSE, FSE, and ISSRE. She has also been a reviewer for prestigious journals including IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Software Engineering (TSE), and ACM Transactions on Software Engineering and Methodology (TOSEM).
Brittany Johnson-Matthews is an Assistant Professor in the Department of Computer Science at George Mason University, where she directs the INSPIRED Lab (INterdisciplinary Software Practice Improvement REsearch and Development). Her work bridges software engineering, human-computer interaction, and machine learning to address sociotechnical challenges in software development. Her educational background includes: Ph.D. in Computer Science from North Carolina State University (2017) B.A. in Computer Science from the College of Charleston (2011) Dr. Johnson-Matthews' research centers on sociotechnical problems in software development, with emphasis on developer productivity, tool support, work environments, ethics, and software for social good. She employs interdisciplinary approaches to study how developers interact with tools and environments, particularly in the context of emerging technologies like AI. Her work often involves empirical studies and tool development to promote fairness, inclusivity, and well-being in software engineering. Analysis of her recent publications (2023-2026) reveals a consistent focus on the human aspects of software engineering. Key themes include the impact of AI-assisted tools on developer well-being, fairness in machine learning toolkits, and ethical considerations in software development. Her research frequently involves building and evaluating tools (e.g., for detecting harmful terminology or causal testing) and conducting empirical studies across open source and industrial settings. She leads the INSPIRED Lab, which fosters interdisciplinary collaboration to improve software practices through research in human-centered computing, empirical software engineering, and ethical AI.
Dr. Guoqiang Li is an Associate Professor in the School of Computer Science at Shanghai Jiao Tong University, where he conducts research at the intersection of formal methods, programming languages, and security. His academic journey includes prior positions as Assistant Professor (2009-2013) and subsequent promotion to Associate Professor (2014-present) at the same institution. He has held international appointments including a postdoctoral fellowship at Nagoya University (2008-2009), an academic visit at the University of Oxford (2015-2016), and a Guest Associate Professorship at Kyushu University's Research and Development Center for Smart Mobility (2016-2020). Dr. Li earned his B.S. from Taiyuan University of Technology (2001), M.S. from Shanghai Jiao Tong University (2005), and Ph.D. from Japan Advanced Institute of Science and Technology (2008). His educational background reflects a strong foundation in both Chinese and Japanese academic traditions. His research focuses on formal verification, programming language theory, zero-knowledge proofs, knowledge reasoning, and intelligent system verification and security. Over the past decade, his work has evolved from traditional formal methods applied to timed automata and process calculi to contemporary topics including neural network verification, zero-knowledge proof systems, and the application of large language models to program analysis. His publication record demonstrates a clear progression toward addressing verification challenges in increasingly complex modern systems. Dr. Li's recent publications reveal a strategic research trajectory: early work centered on timed automata and formal verification of concurrent systems, while current research addresses the verification of AI systems, zero-knowledge cryptographic protocols, and LLM-enhanced program analysis. This evolution reflects his commitment to applying rigorous formal methods to emerging computational challenges. Distinguished Paper Award at ICSE 2020 for 'Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning' Consistent publication in top-tier venues including ASE, ICSE, FSE, and OOPSLA Recognition through multiple National Natural Science Foundation of China (NSFC) grants as Principal Investigator Dr. Li actively contributes to the academic community as a Senior Member of the China Computer Federation, Deputy Director of Theoretical Computer Science for the Shanghai Computer Society, and committee member for multiple technical organizations. He serves as Organization Chair for SEKM'20-22 and FMAC'17, Publicity Chair for TASE'21-23, and has participated in program committees for numerous international conferences. His teaching portfolio includes undergraduate and graduate courses in algorithm design, mathematical foundations, and scientific writing, demonstrating his commitment to both theoretical computer science education and practical application.
Cameron Freer is a Research Scientist in the MIT Probabilistic Computing Project , with prior roles including Instructor in Pure Mathematics at MIT, Postdoctoral Fellow at CSAIL, and Project Associate Professor at Keio University. His work bridges probabilistic computing, logic, and theoretical computer science. Education PhD in Mathematics, Harvard University, 2008 (Thesis: Models with High Scott Rank ) Research Interests Freer's research explores the deep interplay between randomness and computation , focusing on: Foundations of probabilistic programming languages and systems Efficient samplers for discrete and continuous distributions Mathematics of random structures like graphons and exchangeable processes Computability in measure theory and probabilistic inference Publications Overview His recent work (2020–2024) advances probabilistic programming systems (e.g., GenSQL), theoretical frameworks for random graphs via Markov categories, and computable approaches to PAC learning. Earlier contributions include exact sampling algorithms, computable exchangeability, and algorithmic barriers in conditional probability. Academic Service Steering Committee Member, LAFI (formerly PPS) workshop series (2017–2025) Program Committee Chair/Co-chair, PPS 2017–2018 Session Chair, POPL 2017 (PPS track) Industry & Visiting Roles Chief Scientist, Remine (2017–2018) Research Scientist, Gamalon Labs (2013–2016) Lyric Labs Visiting Fellow, Analog Devices (2013–2014) Project Associate Professor, Keio University (2021–2024) Labs & Collaborations Freer collaborates extensively with the MIT Probabilistic Computing Project, Harvard Logic Group, and international partners in Oxford, CMU, and Keio University. His work integrates theoretical insights with practical systems in AI and probabilistic inference.
Mara Salvato is a Senior Scientist at the Max Planck Institute for Extraterrestrial Physics (MPE) and the Origins Excellence Cluster in Garching, Germany, specializing in high-energy astrophysics with a focus on X-ray astronomy and active galactic nuclei (AGN). Her work bridges observational astronomy, cosmology, and data science through comprehensive multiwavelength surveys. Research Interests: Photometric redshifts for AGN, developing advanced methods to determine distances to active galaxies using multiwavelength data X-ray Surveys, particularly through missions like eROSITA, analyzing large-scale cosmic structures and AGN populations Environment of AGN and morphology of their host galaxies, studying how active nuclei relate to their galactic environments across cosmic time Multiwavelength survey integration, creating comprehensive catalogs that combine data from across the electromagnetic spectrum Dr. Salvato's research has significantly advanced our understanding of AGN populations and their evolution through innovative approaches to photometric redshift estimation and multiwavelength data analysis, with particular emphasis on the COSMOS field and eROSITA survey data. Scientific Awards: 2017/2018/2019/2022: Listed among the top 100 Highly Cited Researchers in Space Science (Clarivate Data, ex Thomson Reuters); one of the only 9 women in the list at that time 2023/2024/2025/2026: Listed among the top 3% scientists in Germany, Europe and World 2024: Ranked N.84 among the Best female scientists in the world 2023: Listed among the 100 women more successful women in Italy (Forbes Italia) Dr. Salvato leads significant contributions to major astronomical surveys and has developed influential methodologies for photometric redshift estimation specifically tailored for AGN populations. Her work on the COSMOS field and eROSITA survey has provided critical insights into the evolution of supermassive black holes and their host galaxies. She maintains active collaborations across international astronomical communities and contributes to major data archives that support the broader research community.
Yeting Li is a researcher at the Institute of Information Engineering, Chinese Academy of Sciences, with academic affiliation at the University of Chinese Academy of Sciences. Their work bridges software security and artificial intelligence, focusing on practical vulnerabilities in modern systems. Research spans vulnerability analysis in Kubernetes ecosystems, AI-driven binary similarity detection , and semantic-enhanced static analysis for baseband firmware. Recent work explores large language models for security applications including fuzz driver generation and data contamination mitigation in benchmarking, alongside accessibility-focused testing for speech recognition systems. Publications reveal a clear trajectory toward integrating AI with traditional security analysis, particularly in containerized environments and binary code analysis. Emerging themes include LLM-based tooling for vulnerability identification and specialized testing methodologies for emerging technologies like automatic speech recognition and deep learning operators. Key contributions include Kubernetes resource injection vulnerability studies, Aster for stutterer accessibility testing, and ACETest for deep learning operator validation. Research demonstrates consistent focus on empirical evaluation of security tools across ASE, ICSE, and ISSTA venues from 2023-2025.
Saikat Dutta is an Assistant Professor in the Department of Computer Science at Cornell University, where he joined in August 2024. His research sits at the intersection of Software Engineering and Machine Learning, with a focus on improving the reliability of machine learning systems and leveraging machine learning techniques to solve challenging software engineering problems. His research interests span several key areas including automated test generation and debugging of ML/DL libraries , using AI/ML for automated software engineering , improving performance and effectiveness of regression tests in ML libraries , and static and dynamic analyses for probabilistic programming . His work bridges theoretical foundations with practical applications in real-world systems. Dutta has developed multiple influential frameworks and tools including BugsInDLLs (a database of reproducible bugs in deep learning libraries), FLEX (for fixing flaky tests in ML projects), and TERA (for optimizing stochastic regression tests). His research has been published in top-tier venues including ICSE, FSE, ISSTA, PLDI, and ICLR. Amazon Research Award 2025 Meta AI LLM Evaluation Research Grant 2025 Mavis Future Faculty Fellowship 2022-23 Facebook PhD Fellowship 2020-22 3M Foundation Fellowship 2019-2020 Dutta actively mentors PhD students including Yingao (Elaine) Yao, Shinhae (Joseph) Kim, and Junkai Huang. He has served on program committees for major conferences including ASE, ISSTA, and ICSE. His teaching includes courses on Software Engineering in the Era of ML/AI.
Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.
Salah Sadou is a researcher affiliated with IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires) and CNRS (Centre National de la Recherche Scientifique) at University of South Brittany, France. His research focuses on advancing program analysis techniques through innovative applications of machine learning. His primary research interests include: Abstract Interpretation methodologies Program analysis and verification Compiler optimization techniques Machine learning applications in static analysis Loop transformation heuristics Salah Sadou's recent work demonstrates a significant trend toward integrating deep learning approaches with traditional abstract interpretation frameworks. His research on loop unrolling heuristics represents an important contribution to improving the precision and efficiency of static program analysis tools, with potential applications across software verification and compiler optimization domains. As a member of IRISA, Salah Sadou contributes to one of France's leading computer science research institutes, which maintains strong connections with both academic and industrial partners in the software engineering community.
Sen Chen is a Professor at Nankai University, holding positions in both the College of Cryptology and Cyber Science and the College of Computer Science. He leads the Nankai Software Security Laboratory (NKSSecLab) and is a member of Professor Zheli Liu's research group. Previously, he served as a tenured associate professor and research professor at Tianjin University (2021-2024), and as a research assistant professor at Nanyang Technological University (NTU), Singapore. Dr. Chen's research focuses on software security and software supply chain security, with particular emphasis on vulnerability analysis and malware detection. His work spans multiple domains including mobile security, AI security, open-source security, and intelligent software development and testing. His research has led to significant contributions in automated security vulnerability detection, software composition analysis, and security tool development for various platforms including Android, Java, and blockchain systems. Analysis of Dr. Chen's recent publications (2023-2025) reveals a strong focus on software supply chain security, with particular attention to vulnerability detection and remediation in open-source ecosystems. His work demonstrates expertise in applying advanced machine learning techniques to security problems, especially in the context of Android applications and containerized environments. There's a clear trajectory toward addressing emerging challenges in AI security and large language model supply chains, reflecting his ability to adapt research directions to evolving technological landscapes. ACM SIGSOFT Distinguished Paper Award (FSE 2024) ACM SIGSOFT Distinguished Paper Award (ASE 2023) First Place of the 13th Challenge Cup China College Students' Entrepreneurship Competition ACM SIGSOFT Distinguished Paper Award (ICSE 2023) Prototype Research Tool Award 2nd Place (Freestyle) in CCF ChinaSoft 2022 First Place of The 8th China International College Students' 'Internet+' Innovation and Entrepreneurship Competition ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM China Rising Star Award (ACM Tianjin Council) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) First Class of Progress of Science and Technology Prize of Tianjin, 2020 Dr. Chen has successfully secured funding from multiple prestigious sources including key R&D programs, general and pre-research projects of the National Natural Science Foundation of China, and the Populus euphratica Forest Fund. His theoretical research has been applied by major companies such as State Grid, China Automotive Industry Corporation, and Huawei. He has mentored students to win national gold medals in both the 'Internet Plus' and Challenge Cup programs, demonstrating his commitment to student development and practical application of research. Dr. Chen leads NKSSecLab (Nankai Software Security Laboratory), which focuses on cutting-edge research in software security and supply chain security. The lab has developed several notable tools including SCTruster (a digital trust chain platform for software supply chain security) and LiDetector. The lab maintains strong international collaborations with institutions like Nanyang Technological University in Singapore and has established itself as a leading research group in software security within China.
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.
Jingyi Wang is an Assistant Professor at Zhejiang University (ZJU), China, leading the IS2 (Intelligent System Security) Lab. He holds a US-equivalent tenure-track position and has established himself as a prominent researcher in software engineering for AI and formal methods applied to security. His educational background includes: Bachelor's degree from Xi'an Jiaotong University (2013) PhD from Singapore University of Technology and Design (2018) Research Fellow at National University of Singapore (2019-2020) Wang's research focuses on developing principled methodologies for building trustworthy AI models and secure systems. His work spans testing, verification and repair of AI models/systems, AI safety and security, and formal reasoning of security. He has developed innovative approaches for neural network testing, verification, and repair, with particular emphasis on explainability, fairness, and robustness. His publication record shows a clear progression from foundational work on concolic testing (2018) to increasingly sophisticated methods for evaluating and improving AI systems, with recent focus on large language models and decentralized identity systems. His research bridges formal methods with practical software engineering challenges in AI security. His notable achievements include: Two ACM SIGSOFT Distinguished Paper Awards (ICSE 2018 and 2020) ACM SIGSOFT Research Highlights Best Paper Award Runner-up at IEEE TDSC 2024 Wang actively contributes to the research community as Program Committee member for top conferences including CCS, ICSE, ISSTA, ASE, WWW, and AAAI. He serves as PC Co-chair for ICFEM 2025, Large Model Safety Workshop 2025, and SAC-SVT 2026. His leadership in the IS2 Lab demonstrates his commitment to advancing intelligent system security through rigorous research.
Max Hort is a researcher at Simula Research Laboratory in Norway specializing in Machine Learning for Software Engineering, Defect Detection, and Software Fairness. He has established himself as an active contributor to the software engineering research community through numerous publications and program committee roles at major conferences. His research focuses on: Machine Learning for Software Engineering Defect Detection Software Fairness Program Repair Log Parsing Hort's recent work demonstrates a strong emphasis on applying large language models to software engineering challenges, particularly in program repair and defect detection. His publications reveal a methodical approach to evaluating AI techniques in software engineering contexts, with special attention to reliability concerns like non-determinism in language models. He has also made significant contributions to the understanding of software fairness and bias mitigation. Through his active participation in program committees for ASE, ICSE, ESEC/FSE, and SANER conferences, Hort has earned recognition as a knowledgeable contributor to the field. His work bridges theoretical machine learning advancements with practical software engineering challenges, addressing critical issues in modern software development as AI-assisted programming becomes increasingly prevalent.
