Chaiyong Ragkhitwetsagul is an Assistant Professor at the Faculty of ICT, Mahidol University, Thailand, and co-founder of the SERU research group. His research focuses on software engineering with emphasis on maintenance, evolution, and program comprehension. Education: PhD in Software Engineering, University College London (UCL) MS in MSIT-Very Large Information Systems, Carnegie Mellon University BE in Computer Engineering, Kasetsart University His work investigates code proficiency metrics for Python and JavaScript to improve maintenance practices, employs search-based techniques for multi-objective clone configuration, and pioneers autorepairability as a novel software quality dimension. Recent publications demonstrate strong empirical focus on developer productivity and code quality assessment. 2024 publications reveal a cohesive trend toward practical tool development (e.g., jscefr) and empirical validation of software quality characteristics, bridging theoretical research with real-world engineering challenges. Active across major software engineering venues (ASE, ICSE, ICSME), he contributes to the community through program committees while leading the SERU research group's mission to advance software engineering science at Mahidol University.
Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.
Saba Alimadadi is an Assistant Professor in the School of Computing Science at Simon Fraser University, specializing in software engineering with focus on program analysis, debugging, and testing for dynamic languages including JavaScript, TypeScript, and Python. Her research develops practical tools to improve developer productivity through semi-automated comprehension and analysis techniques. Her research interests include: JavaScript program analysis for asynchronous code Debugging methodologies for web applications Testing techniques for dynamic languages Developer productivity tooling Code optimization for server-side JavaScript Dr. Alimadadi's publication record shows consistent contributions to top software engineering venues, with recent work focusing on asynchronous JavaScript analysis, code coverage criteria, and JavaScript application optimization. Her research bridges theoretical program analysis with practical tool development for real-world developer challenges. She serves on program committees for major conferences including ICSE (2021-2026), ASE (2019-2025), and ISSTA (2018-2025), and has held organizational roles such as SPLASH/ISSTA 2026 Poster/Demo/SRC Co-Chair and ASE 2022 Proceedings Chair. At Simon Fraser University, Dr. Alimadadi teaches CMPT 276: Introduction to Software Engineering and CMPT 982/479: Special Topics on Web Engineering, while actively recruiting graduate students for her research group.
Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering since May 2024. Previously, he served as an Assistant Professor at the same institution from July 2019 to May 2024. He earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan, an MS degree from the Chinese Academy of Sciences in June 2014 under Profs. Ye Yang and Wen Zhang, and BE and BHRM degrees from Sichuan University in June 2011. Dr. Wang's research focuses on the intersection of Software Engineering and Artificial Intelligence, with two main thrusts: 1) leveraging AI technologies to address software reliability challenges (AI for SE), and 2) developing software reliability techniques to improve AI infrastructure systems (SE for AI). His specific interests include software testing, program analysis, software reliability, and machine learning applications in software engineering. His research has led to tools that have detected hundreds of true bugs across various open-source projects. His recent publications reveal a strong focus on applying large language models to software engineering tasks, analyzing vulnerabilities in deep learning libraries, and developing techniques for software testing and reliability. The research spans multiple subfields including API testing, vulnerability detection, bias analysis in generated code, and automated assurance case generation. TOSEM Distinguished Reviewer Award 2023 APSEC'23 Distinguished Paper Award ACM SIGSOFT Distinguished Paper Award (ICPC 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2020) Best Paper Award at PROMISE 2019 Dr. Wang actively mentors students at all levels, currently supervising multiple PhD and MASc students. His research group has produced numerous publications in top-tier software engineering venues, including ICSE, FSE, ASE, and TOSEM. He also serves on the editorial board of ACM TOSEM and has been involved in organizing major conferences like ASE and CASCON.
Sébastien Bardin is a Senior Researcher and CEA Fellow at the Software Safety and Security Laboratory of Commissariat à l'Energie Atomique (CEA) in Paris-Saclay, France, with affiliation to University Paris-Saclay. He leads significant research initiatives including the BINSEC group on binary-level security analysis (since 2012) and the QBricks group on quantum program verification (since 2020), and served as Head of the Software Quantum Program at CEA LIST from 2021-2023. His research focuses on formal methods and automatic program analysis with applications in software security. Key areas include binary-level security analyses such as vulnerability detection & assessment, reverse engineering, and malware deobfuscation, as well as quantum programming and verification. His methodological expertise spans symbolic execution, abstract interpretation, software model checking, and SMT solving. Bardin's publications demonstrate consistent impact across top venues in security and formal methods, with notable papers at ESOP, CAV, ICSE, NDSS, and S&P. His work on robust symbolic execution, directed fuzzing, and binary-level analysis has established him as a leading researcher in security-oriented program analysis. Recent publications show growing emphasis on quantum program verification alongside continued contributions to binary security analysis. Scientific Awards: ICSE 2021 ACM SIGSOFT Distinguished Paper Award RTAS 2021 Best Paper Award CAV 2021 Selected Paper CEA Fellow (2021-present) ACM Senior Member (2021-present) Bardin actively mentors PhD students and has advised numerous researchers who have received recognition including GDR Sécurité and GDR GPL PhD awards. His service to the community is extensive, with leadership roles in GDR Sécurité, RESSI, and organizing major conferences including CAV 2023 (sponsor co-chair) and FIC 2023 (scientific program chair). He also contributes to multiple program committees across security and formal methods venues.
Agnieszka Fihel is a Research Fellow at the Centre of Migration Research, University of Warsaw, and Assistant Professor at Université Paris Nanterre. Her academic career spans demographic research with a focus on migration and mortality patterns in European contexts. Dr. Fihel defended her PhD thesis in economics in 2009 at University of Warsaw. She conducted post-doctoral research at the Institut national d'études démographiques (INED) in Paris in 2009 and 2011. Dr. Fihel's research focuses on contemporary demographic phenomena , particularly international mobility in ageing populations and mortality patterns in countries of post-communist transition . Her scholarly work demonstrates expertise in analyzing complex demographic transitions, with special attention to Central and Eastern European contexts. Recent publications reveal her expanding research into cause-of-death certification methodologies and pandemic mortality assessment. Dr. Fihel's publications reveal a consistent focus on demographic methodology, particularly in analyzing migration impacts on population structures and developing more accurate mortality measurement techniques. Her work often employs comparative approaches across European nations, with special attention to Poland's demographic transformation. Scholarship of the Foundation for Polish Science (2009) Scholarship of the Polish Minister of Science for young prominent scholars (2012-2015) Dr. Fihel has maintained an active research career through collaborations with institutions like the Institut national d'études démographiques (INED) in Paris. Her work has been supported by prestigious funding bodies including the Foundation for Polish Science and the Polish Ministry of Science. As a Research Fellow at the Centre of Migration Research at the University of Warsaw, Dr. Fihel contributes to one of Poland's leading demographic research centers, which focuses on migration patterns and their societal impacts.
Professor Tadeus Uhl is a faculty member at the University of Applied Sciences Flensburg in the School of Information and Communication. His research focuses on telecommunications, video quality assessment, and Quality of Service (QoS) technologies. He maintains an active research profile with numerous publications spanning from 2017 to 2024. Professor Uhl's research interests center around video quality evaluation, telecommunications technologies, and network performance analysis. His work examines cutting-edge topics including 8-bit and 10-bit MP4 coding, machine learning applications for video quality assessment, and user experience with different video resolutions (1K, 2K, and 4K). He has also conducted significant research on 5G technology, VoIP systems, and Quality of Service in IoT environments. His recent publications show a clear trend toward video quality assessment using modern machine learning techniques, with a focus on real-world streaming services like Netflix. His work bridges theoretical network performance concepts with practical applications in video streaming, cellular networks, and offshore communication systems. Professor Uhl has made substantial contributions to understanding Quality of Service parameters across various communication technologies, from traditional VoIP systems to modern IoT networks and offshore wind farm communications infrastructure. His research has been published in notable venues including IEEE conferences, the Journal of Telecommunications and Information Technology, and various scientific journals focused on telecommunications and multimedia technology.
