Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.
Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.
Michael Hilton is an Associate Teaching Professor at Carnegie Mellon University's School of Computer Science, where he serves as the Associate Department Head for Education in the Software and Societal Systems Department. He directs the Software Engineering Minor and the Software Engineering Concentration programs, and teaches various software engineering courses including Foundations of Software Engineering, Introduction to Software Construction, and Crafting Software. His research interests focus on improving the lives of software engineers, with particular emphasis on flaky tests , continuous integration , and structured editors . His work bridges both the research and educational aspects of software engineering, aiming to make software development processes more reliable and efficient while educating the next generation of software engineers. Analysis of his recent publications reveals a strong focus on flaky test detection and mitigation, with significant contributions to understanding test reliability in continuous integration environments. His research spans empirical studies of testing practices, machine learning approaches to flakiness prediction, and educational aspects of software engineering. A notable trend is his increasing focus on the intersection of software testing and education, particularly examining how students interact with modern development tools and practices. Professor Hilton is highly active in the software engineering research community, serving on program committees for major conferences including ASE, ICSE, and SIGCSE. He has contributed to numerous workshops and symposia focused on software engineering education and testing research. As an educator, he has developed and taught multiple software engineering courses covering object-oriented design, testing, concurrency, and software construction principles. He emphasizes practical, hands-on learning experiences that prepare students for real-world software development challenges. His educational research explores best practices for teaching software engineering and the impact of new technologies like generative AI on software engineering education. He maintains an active research lab focused on software engineering tools and education, collaborating with both academic and industry partners to address practical challenges in software development workflows. His current projects include investigations into flaky test patterns, continuous integration practices, and the integration of modern development tools into software engineering curricula.
Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.
Djamel Eddine Khelladi is a CNRS Researcher at the IRISA laboratory within the DIVERSE team at University of Rennes, specializing in software engineering with emphasis on model-driven techniques and empirical validation. His work bridges theoretical frameworks and industrial-scale applications, particularly in evolving software ecosystems. His academic foundation includes a Ph.D. from Sorbonne University (formerly University Pierre et Marie Curie) at the Laboratory of Computer Science of Paris 6 (LIP6), followed by postdoctoral research at Johannes Kepler University Linz's Institute for Software Systems Engineering. This trajectory established his expertise in software evolution and model-driven approaches. Khelladi's research centers on software evolution challenges, particularly model-code co-evolution in highly-configurable systems like the Linux kernel. He develops scalable analysis tools (e.g., HyperAST, HyperDiff) and investigates empirical phenomena in build systems, configuration management, and polyglot programming environments. Recent work increasingly integrates large language models for automated co-evolution tasks while maintaining rigorous empirical validation. His publication trends reveal a consistent focus on practical tooling for software evolution, with growing exploration of AI-assisted engineering. Key themes include scalability in software history analysis, reproducibility in configurable systems, and debugging multi-language environments, often using Linux kernel ecosystems as testbeds. As an active community contributor, Khelladi serves on program committees for ASE, ICSE, and ESEC/FSE while advancing research through the DIVERSE team at IRISA. This group specializes in variability-intensive software systems, providing the collaborative environment for his empirical and tool-building research.
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.
Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.
Nira Liberman is a Professor of Social Psychology at Tel Aviv University, where she has been a faculty member since 2001. She completed all her formal education at Tel Aviv University, earning her Bachelor's degree in Psychology and Comparative Literature, Master's degree in Social Psychology, and PhD in 1997. She also completed postdoctoral work at Columbia University and taught at Indiana University, Bloomington before returning to Tel Aviv University. Professor Liberman's research focuses on psychological distance and its effects on decision making, evaluation, motivation, and mental representation. She is particularly known for her work on Construal Level Theory (CLT), which examines how psychological distance influences cognitive processing. Her research also extends to Obsessive-Compulsive Disorder (OCD), where she developed the Seeking Proxies for Internal States (SPIS) model, as well as to essentialism, disgust, and conservatism. Her Social Psychology Lab investigates how people mentally traverse beyond the immediate 'here and now' to consider future and past time, distant locations, other people, and hypothetical (counterfactual) events. The lab examines what enables such expansion of mental scope and the consequences of this expansion, including how psychological distancing affects cognitive performance, working memory, and decision making. Professor Liberman's publication record shows consistent productivity across multiple domains of psychology. Her recent work continues to explore the intersection of Construal Level Theory with OCD mechanisms, moral judgment, and cognitive performance. Her research demonstrates strong theoretical integration across social, cognitive, and clinical psychology domains, with particular attention to the embodied and neural correlates of abstract thinking. Professor Liberman has established productive collaborations with researchers worldwide, most notably with Yaacov Trope with whom she has co-authored numerous foundational papers on Construal Level Theory. Her work has been published in top-tier psychology journals including Journal of Personality and Social Psychology, Psychological Review, and Journal of Experimental Psychology. Her lab continues to investigate fundamental questions about how people navigate between proximal and distal concerns, how abstraction enables traversing psychological distance, and how these processes relate to clinical phenomena like OCD. Professor Liberman's research program represents a sophisticated integration of basic cognitive processes with real-world applications in clinical and social domains.
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.
