Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.
Fabrizio Rossi is a Full Professor of Operations Research at the University of L'Aquila, Department of Information Engineering, Computer Science and Mathematics. He has been a member of the Board of Administration at the university since 2019 and previously from 2010 to 2012. His academic career spans over two decades, including roles as Associate Professor (2005–2019) and Assistant Professor (1997–2002). Education: Ph.D. in Operations Research, University of Rome 'La Sapienza' (1996) Laurea Degree in Electrical Engineering, University of Rome 'La Sapienza' (1992) Visiting Student in IEOR at Columbia University, New York (1996) Operations Research School, Scuola di Matematica Interuniversitaria (1996) Research Interests: Fabrizio Rossi specializes in large-scale optimization methodologies, including Integer Programming and Combinatorial Optimization. His work addresses complex applications in telecommunications network design, manufacturing process optimization, logistics systems, and healthcare. Recent innovations include algorithms for DNA sequence optimization and tumor classification using machine learning. He focuses on practical solutions through advanced techniques like lift-and-project operators and robust optimization frameworks, with significant contributions to scheduling and resource allocation in call centers and satellite missions. Scientific Awards: Informs Computing Society Prize (2014) (collaborative with Jim Ostrowski, Jeff Linderoth, Stefano Smriglio) Finalist, Euro Excellence in Practice Award (2006) for research on terrestrial broadcasting migration Advising and Grants: As a project leader, he coordinated major initiatives such as: PRIN 2010–2012 : Integer Programming methods for radio transmission networks 2008–2009 : Analog-to-digital broadcasting migration optimization IST SAILOR Project (2002–2005) : Satellite UMTS emulation systems He also contributed to European Space Agency's MAS Project (2004–2006) and collaborated with Italian regulatory bodies on telecommunication projects. His research portfolio includes over 50 projects since 1993, emphasizing real-world applications in manufacturing, logistics, and healthcare systems. Labs/Teams: His research is conducted within the Department's optimization groups and industry partnerships. Collaborations include ESA satellite scheduling, RAIWay frequency assignment, and healthcare institutions for treatment planning systems.
Sonia Haiduc is an Associate Professor in the Department of Computer Science at Florida State University (FSU). Her research focuses on software engineering, particularly software maintenance, evolution, program comprehension, and source code search. She holds a Ph.D. (2013) and M.Sc. (2009) from Wayne State University, and a B.Sc. (2006) from Babeș-Bolyai University in Romania. **Education:** Ph.D. in Computer Science, Wayne State University, 2013 M.Sc. in Computer Science, Wayne State University, 2009 B.Sc. in Computer Science, Babeș-Bolyai University, 2006 **Research Interests:** Software Engineering Methodologies Source Code Analysis Empirical Software Engineering Video Tutorials for Software Learning Developer Communications Human Factors in Software Development Her service contributions include organizing key conferences like IEEE International Conference on Program Comprehension (ICPC) and chairing roles in workshops such as Mining Unstructured Data (MUD). She has been recognized with awards including the Google Anita Borg Scholarship and multiple ACM SIGSOFT travel awards. **Grants & Labs:** Director of the SERENE Group at FSU National Science Foundation Award CCF-1526929 (2015–2018): Focuses on text retrieval in software engineering Teaching spans courses like Software Engineering II and Graduate Software Engineering, with extensive TA experience in foundational computer science courses.
Michele Martone is a Researcher at the High Performance Systems Division of the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. His work focuses on High Performance Computing (HPC) , sparse matrix computations , and code restructuring techniques , with a strong emphasis on OpenMP/MPI parallelization and semantic patching using the Coccinelle tool. Research Interests : HPC, automated code restructuring, sparse matrix optimization, semantic patching, and free/open source software (FLOSS) development. Key Tools : Author of the librsb library for sparse matrix operations, SparseRSB for Octave, PyRSB for Python integration, and the FIM image viewer. Publications : His recent work includes semantic patching for HPC refactorings, Coccinelle-based tooling, and performance optimization of sparse linear algebra libraries. Teaching : Delivers trainings and talks on Coccinelle, OpenMP/MPI, and FLOSS at events like HIPS25 , FOSDEM , and deRSE conferences. Advocacy : Strongly promotes free software, transparency in science, and public access to code developed with taxpayer funding.
Gabriele Bavota is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a Laurea (cum laude) and PhD in Computer Science from the University of Salerno (2013). Before joining USI in 2016, he was an Assistant Professor at the Free University of Bolzano-Bozen (2014–2016) and a research fellow at the University of Sannio (2013–2014). Research Focus: Software maintenance, mining software repositories, empirical software engineering, and AI-driven code analysis. Awarded the 2018 ACM Sigsoft Early Career Researcher Award and leads the ERC Starting Grant project DEVINTA, exploring deep learning applications in software engineering. Authored over 180 publications in top venues like ICSE, FSE, and TOSEM, with multiple best/distinguished paper awards. His work bridges empirical studies, AI tools, and developer-centric solutions, addressing challenges in code quality, refactoring, and automated testing. He has served as program chair for ICPC 2016, SANER 2017, and ICSME 2023, and holds editorial roles in TSE, EMSE, and JSS. Labs/Teams: Principal Investigator of the DEVINTA project and part of the Software Institute at USI, fostering collaborations in AI, software engineering, and empirical research.
Houari Sahraoui is a Full Professor and Vice Dean of Planning and Infrastructure at the Faculty of Arts and Sciences, University of Montreal, where he also previously served as Department Director from 2013 to 2017. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) and has been actively supervising graduate students and conducting research in software engineering since at least 2000. Dr. Sahraoui's research focuses on automated software engineering , with particular emphasis on model transformations learning from examples using evolutionary approaches. His work spans reverse engineering (comprehension) and reengineering (refactoring, migration to component-based software), utilizing static and dynamic analysis techniques. He also investigates visualization of large sets of multidimensional data for software comprehension and maintenance. His research integrates artificial intelligence approaches to enhance various phases of the software development lifecycle. His recent publications demonstrate a strong trend toward integrating large language models and artificial intelligence with traditional software engineering practices. The research spans code review automation, model-driven engineering, microservices identification, and digital twins for applications like vertical farming. Many publications focus on improving automation through techniques like parameter-efficient fine-tuning, knowledge distillation, and social diversity metrics. Dr. Sahraoui was named a Fellow of Automated Software Engineering in 2023, an honor recognizing his significant and sustained contributions to the ASE community, both scientifically and professionally. This prestigious title is awarded by the IEEE/ACM International Conference Steering Committee. Dr. Sahraoui has supervised over 50 Master's and PhD students throughout his career, with recent supervision focusing on AI-assisted software engineering, code review automation, and model-driven approaches. His research has been supported by multiple grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), MITACS, and industry partners, with projects spanning from 2000 to projected completion in 2031. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) which focuses on open and distributed systems and software experimentation. His current projects include 'Improving automation and assistance for software engineering tasks with generative AI' (2025-2031) and work on digital twins for vertical farming.
Prof. Tevfik Kosar is a Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), part of the School of Engineering and Applied Sciences. His research focuses on data-intensive computing, distributed systems, petascale storage, and energy-efficient data transfer optimization. He has taught numerous courses on operating systems, distributed systems, and green computing, including CSE 421/521 (Operating Systems), CSE 709 (Green Computing), and CSE 710 (Distributed File Systems). His achievements include the UB Exceptional Scholar Award (2020), SEAS Researcher of the Year (2018), and an NSF CAREER Award (2011). He leads the DIDCLAB , which develops innovative solutions for data-intensive distributed computing. Prof. Kosar holds a PhD from the University of Wisconsin-Madison (2005), MS from Rensselaer Polytechnic Institute (1999), and BS from Bogazici University (1997). His work spans academic contributions, industry collaboration, and sustainability-focused research. Teaching Highlights: Courses: Operating Systems (repeated annually since 2011), Green Computing (since 2013), and specialized seminars on distributed file systems. Grants & Projects: Collaborative NSF grants for GreenSW (2024), CloudScent (2023), and energy-aware data transfer optimization (2018–present). His research emphasizes minimizing energy consumption in cloud and HPC systems while maximizing data transfer efficiency. Recent projects include GreenABR+ for energy-aware video streaming and Greendataflow for zero-carbon data movement. He has also pioneered tools like FlowTracer for AI training cluster analysis and PhoneLab , a smartphone testbed for real-world network studies.
Sebastian Herold is an Associate Professor in Computer Science at Karlstad University, Sweden. His research focuses on software architecture, design, and quality, with an emphasis on mitigating architecture erosion and technical debt through empirical industry collaboration. He has held roles at RWTH Aachen University (2005 diploma/Master's equivalent), Clausthal University of Technology (PhD 2011), and Lero - The Irish Software Research Centre (2013–2015). Education: RWTH Aachen University of Technology, Germany: Diplom-Informatik (2005) Clausthal University of Technology, Germany: PhD in Computer Science (2011) Research interests span software architecture evolution, empirical studies in software engineering, privacy patterns (GDPR compliance), and usability evaluation in digital health. His work includes developing tools like InMap for automated code-to-architecture mapping and organizing workshops like SAEroCon on architectural consistency. Publications highlight trends in synthetic data for healthcare usability, machine learning for architecture mapping, and empirical validation of software practices. Recent work integrates AI methods for software engineering challenges and explores efficiency in digital health evaluations. Awards: None explicitly listed. Grants and advising: No specific grants or student advisees mentioned; collaborations with industry and Lero are emphasized. Labs/Teams: Active in Karlstad University's Department of Computer Science, with contributions to interdisciplinary projects like GDPR-focused MOOCs and healthcare software development studies.
Mark van den Brand is a Full Professor of Software Engineering and Technology at Eindhoven University of Technology (TU/e), where he holds the chair in Software Engineering and serves as Scientific Director of the Digital Twin Lab (part of EAISI). He concurrently holds a Visiting Professorship at Royal Holloway University of London. His academic journey includes a PhD in Computer Science from Radboud University (1992), prior roles as part-time Associate Professor at Vrije Universiteit Amsterdam, and senior research positions at CWI. He also serves as Editor-in-Chief of the Journal on Software Engineering of Autonomous Systems (JSEAS) and editorial board member of Computer Languages, Systems and Structures . His research focuses on software engineering fundamentals, digital twins, model-driven engineering, safety-critical systems, and automotive software. Key contributions include work on domain-specific languages, model consistency, and safety assessment frameworks. He leads the Software Engineering and Technology cluster at TU/e and directs educational initiatives in the Department of Mathematics and Computer Science. Recent publications emphasize digital twin engineering, model management, and automotive systems safety. He collaborates internationally on projects like the C-ITS reference architecture and functional safety for autonomous driving. His work bridges academic research with industrial challenges through tools like SAMOS and COGENT, addressing scalability and consistency in large-scale systems. Current educational responsibilities include courses on programming, digital systems, and data science. He actively engages with industry through the TU/e innovation ecosystem, focusing on impactful research solutions for modern software engineering problems.
Işıl Dillig is an Associate Professor in the Department of Computer Science at the University of Texas at Austin, leading the UToPiA research group. She focuses on programming languages, with emphasis on static analysis, verification, and synthesis to enhance software reliability and security. Education: BS, MS, PhD in Computer Science from Stanford University Her research addresses critical challenges in: Program analysis for security and correctness Automated program synthesis for complex tasks Verification of concurrent and database-driven systems Recent publications span neurosymbolic synthesis, database integration, and smart contract optimization. Trends include hybrid AI-formal methods approaches and domain-specific language design. Scientific recognition includes: Sloan Fellowship NSF CAREER award She actively contributes to academic leadership as committee member, program chair, and keynote speaker in top conferences like PLDI, OOPSLA, and POPL.
Reid Holmes is a Professor in the Department of Computer Science at the University of British Columbia , part of the Faculty of Science . His research focuses on improving software engineering practices, particularly in end-user programming, developer tool design, and empirical software engineering. He leads the Software Practices Lab and has contributed extensively to understanding developer workflows, testing methodologies, and educational tools for programming. Education: PhD in Computer Science, University of Calgary (2008) MSc in Computer Science, University of British Columbia (2004) BSc in Computer Science, University of British Columbia (2002) Research Interests: End-user programming environments, software testing, developer productivity tools, human-centered AI, educational technology, and empirical studies on software development practices. His work emphasizes bridging the gap between theoretical advancements and practical usability for both professional developers and novice programmers. Recent Article Trends: Focus on hybrid programming environments (e.g., block-based and graph-based systems), human-AI collaboration in testing/assertion generation, and age-inclusive IDE design. His research often involves empirical evaluations of tool effectiveness and developer workflows. Awards: FSE Most Influential Paper Award ICSE Most Influential Paper Award UBC Computer Science Teaching Award CS-Can/Info-Can Outstanding Research Prize Advising & Grants: Supervised over 30 graduate students and postdocs. Noted for collaborative projects with industry partners (e.g., Mozilla, Microsoft). Active in curriculum development for software engineering education. Labs/Teams: Leader of the Software Practices Lab , collaborating with industry and academic partners on tools like CodeShovel , AutoAssert , and Devy (conversational developer assistant).
Ioannis Stefanakos is a Research Associate at the Department of Computer Science, University of York, affiliated with the High Integrity Systems research group. His work focuses on formal verification of autonomous systems, safety-critical robotics, and software performance analysis. Current projects include assurance frameworks for drones, adaptive reinforcement learning in healthcare robotics, and probabilistic modeling of software performance. Recent research emphasizes interdisciplinary applications in UAV systems, medical decision support systems, and collaborative manufacturing robots. No academic awards are listed, though his work has been published in top-tier conferences. Advising and grants details are not explicitly stated, but his involvement in doctoral forum papers suggests potential supervision roles in software systems research.
Wei Yang is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, working within the Erik Jonsson School of Engineering and Computer Science. He holds a regular faculty position with an office in ECSS 4.225 and is actively engaged in teaching, research, and mentoring graduate students. His academic journey spans multiple prestigious institutions, and he currently serves on editorial boards for major software engineering journals. Dr. Yang received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2018, advised by Prof. Carl A. Gunter and Prof. Tao Xie. He earned his M.S. in Computer Science from North Carolina State University in 2013 under Prof. Tao Xie, and his B.E. in Software Engineering from Shanghai Jiao Tong University in 2011 under Prof. Jianjun Zhao. He was also a visiting researcher at the University of California, Berkeley, invited by Prof. Dawn Song. Dr. Yang's research spans multiple cutting-edge areas at the intersection of software engineering and security. His primary focus centers on software engineering for AI systems , particularly addressing challenges in deploying AI on edge devices like mobile phones, IoT devices, and autonomous vehicles. His pioneering work on efficiency robustness (initiated in 2019) explores how different inputs can trigger varying computational costs in neural networks, leading to novel attacks and defenses. He also develops infrastructure support for AI deployment , including compiler toolchains for dynamic-shaped neural networks and security analysis for IoT deployments. His research extends to mobile testing (since 2012), malware detection using expectation context analysis, and intelligent tools for software engineers and security researchers. Dr. Yang's publication record demonstrates a clear trajectory toward addressing critical challenges in AI security and efficiency. His recent work shows increasing focus on foundation models, large language models, and their security implications, while maintaining strong connections to practical software engineering challenges. The publications reveal a consistent pattern of high-impact research in top-tier venues across software engineering, AI, and security domains. NSF CAREER Award (2022) ACM SIGSOFT Distinguished Paper Award (2021) Amazon Research Award Dr. Yang actively mentors a large group of graduate and undergraduate students, with several PhD students currently working under his supervision. He serves as a faculty advisor for the ASTRO (AI Security and Trustworthiness Operations) team, which was selected as a red teaming participant in the Amazon Nova AI Challenge. His research is supported by significant grants, including the NSF CAREER award providing approximately $500,000 over five years. He emphasizes practical student development, helping them navigate the job market and transition from academic training to professional careers. Dr. Yang leads a vibrant research group focused on software engineering and security challenges in AI systems. His team, including the ASTRO group participating in the Amazon Nova AI Challenge, develops innovative techniques for testing, securing, and improving AI-based systems. The research environment fosters collaboration across multiple domains, with students working on projects ranging from mobile security to foundation model engineering.
Gail C Murphy is a Professor in the Department of Computer Science at the University of British Columbia and currently serves as Vice-President Research & Innovation. She is also a co-founder of Tasktop Technologies Incorporated. Her research focuses on improving the productivity of knowledge workers, particularly software developers, through empirical studies and tool development for large software system evolution. Professor, Department of Computer Science, University of British Columbia Vice-President Research & Innovation, University of British Columbia Co-founder, Tasktop Technologies Incorporated Her research spans software engineering, human-computer interaction, and productivity tools. Key areas include task context modeling, code refactoring, developer collaboration in hybrid teams, and the application of generative AI in software development practices. Recent publications highlight trends in leveraging semantic analysis for task-relevant information identification, empirical studies on developer productivity, and the integration of human-centered AI tools in software engineering workflows. Awards include the IEEE Harlan Mills Award (2018) and an Impact Award (2022). Contact details include the University of British Columbia (VPRIO) address at Suite 580, 1958 Main Mall, and the Computer Science department at 201-2366 Main Mall, Vancouver, BC, Canada. Phone numbers and emails are provided for liaison purposes.
Gregor Kiczales is a Professor of Computer Science at the University of British Columbia , with a career spanning over three decades. His work focuses on programming language design, modularity, and aspect-oriented programming (AOP). Primary affiliation: University of British Columbia Verification email: gregor@cs.ubc.ca Kiczales' research centers around modularity and aspect-oriented programming , with significant contributions to understanding crosscutting concerns, developing AOP frameworks like AspectJ, and exploring novel abstractions for software systems. His work includes: Foundational research in AOP semantics and implementation Studies on code-design alignment and software architecture Developing registration-based abstractions and late-binding mechanisms Investigating scalability challenges in AOP systems
