Dr. Artur Tomaszewski is an Associate Professor at Gdańsk University of Technology, working in the Department of Teleinformatics within the Faculty of Electronics, Telecommunications and Informatics. His office is located in Building A, room 149. His research interests include: Computer Networking Software-Defined Networking (SDN) Wireless Communication Network Optimization 5G Networks Network Security Dr. Tomaszewski's recent research focuses on advanced networking technologies, with particular emphasis on controller placement optimization in SDN architectures, coordinated spatial reuse in Wi-Fi networks, and network function virtualization in 5G systems. His work combines theoretical optimization approaches with practical networking applications, addressing challenges in network reliability, performance, and security. His publications from 2021-2025 demonstrate a consistent research trajectory in network optimization, with increasing focus on machine learning applications in networking and next-generation Wi-Fi standards. He has contributed to both theoretical frameworks and practical implementations in the field of computer networking. Dr. Tomaszewski collaborates with researchers internationally, as evidenced by his co-authorship on publications with scholars from various institutions worldwide. Contact information: Email: artur.tomaszewski@pg.edu.pl
Christian Peukert serves as Professor of Digitization, Innovation and Intellectual Property at HEC Lausanne (Faculty of Business and Economics at University of Lausanne) and leads the Digital Markets Lab. His research examines how digitization transforms consumer behavior, firm strategies, and market dynamics, with particular focus on intellectual property frameworks and the economics of data and artificial intelligence. He actively contributes to the Digital Economy Network and teaches courses in strategy, innovation, applied econometrics, and data science. Peukert's research interests center on the economic implications of digital transformation across multiple domains. His work investigates copyright economics in the digital age, AI regulation challenges, open source software business models, and the impact of data governance frameworks like GDPR. He explores how technological changes affect market efficiency in creative industries including publishing, music, and comics, while examining the relationship between innovation incentives and intellectual property systems in digital environments. His publication portfolio demonstrates strong interdisciplinary engagement across law, economics, and computer science, with recent articles appearing in top journals like Management Science, Research Policy, and Organization Science. Notable research trends include examining AI training data economics, analyzing digital market regulation effectiveness, and studying the welfare impacts of mobile internet access policies. His work frequently employs empirical methods including field experiments, natural experiments, and large-scale data analysis. Peukert has received significant recognition for his scholarly contributions, including: Best Paper Award at Strategy Science Conference for research on startup funding and open source communities Best Paper Award at WISE for work on news recommendation algorithms Best Paper Award at INFORMS Annual Meeting 2024 for research on AI training data dynamics His research has attracted media attention from major outlets including Wall Street Journal, Washington Post, MIT Technology Review, and VoxEU, demonstrating the policy relevance of his work. Peukert maintains active collaborations with researchers across Europe and regularly contributes to policy discussions on digital markets and intellectual property through his leadership in the Digital Markets Lab and Digital Economy Network.
Prof. Dr. Karin Küffmann is a Professor of Business Informatics at the Faculty of Economics, Westphalian University of Applied Sciences in Gelsenkirchen, Germany. She serves as Head of the Digital Business and IT Management degree program and leads significant research projects including URBAN.KI (Artificial Intelligence for Municipalities) and ARIZON (XR in inner cities). Her work focuses on practical applications of digital technologies in urban development and business contexts across the Ruhr region. Her primary research interests include: IT Management and IT Controlling with process-oriented approaches Digital Business Models and Data Value Creation Sustainable Smart Cities development in the Ruhr region Artificial Intelligence applications for municipalities Extended Reality (VR/AR) in customer-oriented applications Digital and data-driven business models Prof. Küffmann's recent publications demonstrate a strong trajectory toward applied urban digitalization research, with particular emphasis on Smart City development in the Ruhr region, XR technologies for revitalizing vacant properties, and digital business models for traditional retailers. Her work consistently bridges theoretical concepts with practical implementation, focusing on sustainability and economic viability in municipal contexts. She is actively engaged in regional digitalization initiatives as an Ambassador of the Smart Region Emscher-Lippe, participating in various working groups and functions related to digitalization and evaluation at state and district levels. Within her university, she serves on multiple committees including appointment committees, equal opportunities committees, quality assurance committees, program development, and international affairs. Her professional activities extend beyond academia through consulting and coaching in digitalization, delivering lectures, and managing regional projects focused on IT strategies, data value creation, and the implementation of AI and VR technologies. She supervises theses on digital transformation topics, particularly AI applications in SMEs, and maintains active industry partnerships through her research projects.
Dr. A. K. Mistry is a Researcher at GSI Helmholtzzentrum für Schwerionenforschung GmbH in Darmstadt, Germany, specializing in scientific metadata and data management for nuclear physics experiments. They lead significant initiatives including NAPMIX (Nuclear, Astro, and Particle Metadata Integration for eXperiments) and contribute to HELPMI (Helmholtz Metadata Collaboration Initiative for Plasma and Laser-Plasma Physics). Dr. Mistry's research focuses on developing metadata frameworks that implement FAIR principles (Findability, Accessibility, Interoperability, and Reusability) for physics research data. Their work bridges nuclear physics experimentation with modern data science approaches to enable better data sharing and reuse across the international physics community. Recent projects include developing a Django & React web application for metadata generation and creating cross-domain metadata schemas that work across traditionally separate physics domains. The research output shows a strong trend toward addressing data management challenges in nuclear physics, with an emphasis on creating practical tools and standards that researchers can implement in their daily work. The publications span both theoretical framework development and practical implementation of metadata solutions for specific experimental setups. Dr. Mistry's scientific contributions are primarily in the development of research data infrastructure rather than traditional physics discoveries, positioning them at the forefront of the data science revolution in physics research. They are actively involved in mentoring through research projects and collaborations, providing students with opportunities to work at the intersection of physics and data science. Their work on metadata standards and data management tools represents an increasingly critical component of modern scientific research infrastructure.
Zhongxin Liu is a Distinguished Research Fellow (Assistant Professor) and Doctoral Supervisor at the College of Computer Science and Technology, Zhejiang University. He received his Ph.D. from Zhejiang University under the supervision of Prof. Shanping Li. His research focuses on intelligent software engineering (AI4SE), particularly using AI to help developers understand, write, change, and test code through learning from software "big data". His educational background includes: Ph.D. in Computer Science, Zhejiang University (2016-2021) Dr. Liu's research interests center around intelligent software engineering with emphasis on code intelligence, program analysis, and software testing. His work leverages machine learning, particularly large language models, to solve challenging problems in software development including code generation, bug localization, and test case generation. He has made significant methodological contributions to the field through his innovative approaches to understanding and improving the software development lifecycle. His recent publications (2024-2026) demonstrate a clear trend toward leveraging large language models for software engineering tasks, with particular focus on code generation, fault localization, and program analysis. The research spans multiple domains including vulnerability detection, smart contract development, and cross-domain code search, showing how AI can enhance traditional software engineering practices. Dr. Liu has received numerous prestigious awards for his contributions: ACM SIGSOFT Distinguished Paper Award (ISSTA 2025, ASE 2018-2020) Distinguished Paper Award (APSEC 2023) Zhejiang University Education Foundation Qizhen Scholar (2021) Distinguished Doctoral Thesis of Zhejiang University (2021) As a Doctoral Supervisor at Zhejiang University, Dr. Liu mentors graduate students in intelligent software engineering. He serves on program committees for major conferences including ICSE, ASE, and FSE. His research group actively recruits undergraduate interns, graduate students, and postdocs to work on code intelligence projects. Dr. Liu is also scheduled to be a Visiting Professor at the University of Stuttgart from October 2024 to January 2025. Dr. Liu leads a vibrant research group at Zhejiang University focused on advancing AI-powered tools for software development. His work bridges theoretical advances in machine learning with practical applications in software engineering, creating novel solutions that help developers be more productive and produce higher quality code.
Christoph Treude is an Associate Professor of Computer Science at Singapore Management University. His academic journey includes senior lecturer positions at the University of Melbourne and the University of Adelaide, and postdoctoral research at McGill University, the University of São Paulo, and the Federal University of Rio Grande do Norte. Treude's research focuses on improving software quality and developer efficiency through better access to relevant information. His methodology combines empirical studies with tool development that considers natural language artifacts in software repositories. His work spans several key areas of software engineering: Empirical studies of developer behavior and practices Integration of large language models in software development Human-AI collaboration frameworks Software documentation quality and maintenance Reproducibility in scientific software Security practices in code reviews His recent publications demonstrate a strong focus on the intersection of artificial intelligence and software engineering, examining how AI technologies can enhance developer productivity while maintaining code quality and security. His research often involves large-scale empirical studies across various software ecosystems and developer communities. Treude has received significant recognition for his contributions: ARC Discovery Early Career Research Award (2018-2020) Four best paper awards, including two ACM SIGSOFT Distinguished Paper Awards He serves on the Editorial Boards of IEEE Transactions on Software Engineering and Springer's Empirical Software Engineering journal, and holds the role of Open Science Editor for Elsevier's Journal of Systems and Software. Treude has chaired major conferences including ICSME 2020, ICPC 2023, and TechDebt 2023, and regularly participates in software engineering conference program committees. His research has been funded by industry leaders including Google, Facebook, and DST, and he has authored over 150 scientific articles with more than 250 co-authors.
Yao Wan is an Associate Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST) in Wuhan, China. He leads the ONE Lab, focused on empowering machines to interact with the physical world through unified natural language interfaces (Language + X paradigm). He obtained his Ph.D. from Zhejiang University and has research visiting experience at Chinese University of Hong Kong, University of Technology Sydney, and University of Illinois Chicago. His research bridges Artificial Intelligence and Software Engineering, with core interests in: Natural Language Processing for code intelligence Large Language Model applications Multimodal learning across code, vision, and UI domains Program analysis and code generation Software engineering automation His publications demonstrate strong focus on applying transformer-based models to software engineering challenges, with recent work expanding into multimodal applications. Research spans code model security, GUI generation, data visualization, and compiler understanding, predominantly using deep learning approaches. Awards: IEEE TCSE Distinguished Paper Award for SANER 2025 publication He actively mentors students through the ONE Lab and serves on program committees for top conferences including ASE, ISSTA, and ICSE. He is seeking highly-motivated undergraduate researchers to join his team. The ONE Lab conducts cutting-edge research at the intersection of programming languages and artificial intelligence, with ongoing projects in code intelligence, multimodal learning, and LLM applications for software engineering.
Thomas Degueule is a researcher at CNRS (Centre National de la Recherche Scientifique) in France, actively contributing to software engineering research since 2015. He serves on program committees for major conferences including ASE, ICSE, and SLE, with primary research interests in Software Evolution, Empirical Software Engineering, and Domain-Specific Languages. His work focuses on breaking change analysis in APIs and libraries, client-library compatibility testing, and dependency management. He develops practical tools like Roseau for source-based breaking change detection and investigates semantic versioning impacts in ecosystems like Maven Central. His empirical approach leverages large-scale repository analysis to address real-world software maintenance challenges, particularly in Java ecosystems. Recent publications (2023-2025) show consistent contributions to breaking change analysis and compatibility testing, appearing in top venues like ASE, ICSE, and ISSTA. His research bridges theoretical insights with practical tooling for software evolution challenges, demonstrating strong empirical methodology and tool-oriented contributions. No scientific awards are documented in the available information. Degueule has advised no publicly listed students and holds no mentioned research grants. His organizational roles include Program Co-Chair for SLE 2023 and committee positions across multiple conferences, reflecting significant service to the software engineering community.
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.
Soner Onder is a Professor in the Department of Computer Science at Michigan Technological University, with an affiliated appointment in the Electrical and Computer Engineering department. His work focuses on computer architecture, programming languages, and simulation techniques, contributing significantly to processor design and memory systems research. Dr. Onder received his PhD in Computer Science from the University of Pittsburgh in 1999. His academic career has established him as a leading researcher in computer architecture with publications spanning two decades in top-tier conferences. Dr. Onder's research spans multiple areas of computer architecture and compiler design, with emphasis on processor design, memory systems, and compiler optimizations. He has made significant contributions to memory disambiguation techniques, branch prediction mechanisms, and energy-efficient processor designs. His work often bridges hardware and software domains, exploring how compiler techniques can better exploit architectural features. Recent research focuses on memory dependence prediction, recovery mechanisms for mispredictions, and energy-efficient data access patterns, with his "Future Gated Single Assignment Form" representing an innovative approach to program representation that bridges compiler design and architectural support. US Patent 7747993: Methods and systems for ordering instructions using future values (2010) Dr. Onder has advised numerous PhD students to completion, including Scott Pomerville (2024), Gorkem Asilioglu (2020), Omkar Javeri (2020), and Zhaoxiang Jin (2018). His research has been supported by grants including "Statically Controlled Asynchronous Lane Execution (SCALE)" and "Vectorized Instruction Space (VIS)" projects. He developed the FAST (Flexible Architecture Simulation Tool) for architectural research and continues to lead an active research program with publications appearing in top-tier venues through 2018. Dr. Onder leads research in computer architecture with a focus on practical implementations. His FAST simulation tool provides a flexible platform for testing architectural innovations. His work often involves collaboration with both compiler researchers and hardware designers to create holistic solutions to performance bottlenecks in modern processors, demonstrating the interdisciplinary nature of his research that bridges hardware and software concerns in computer system design.
Clément QUINTON is an Associate Professor at the University of Lille, affiliated with both Inria and the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille). He is an elected member of the laboratory council and maintains offices in both the Inria building (Room B312) and the M3 building (Room 224) at the Cité Scientifique campus. Dr. QUINTON is a member of the Spirals research team and holds the HDR (Habilitation à Diriger des Recherches), a French academic qualification that allows him to supervise PhD students. Dr. QUINTON's research spans several areas within computer science, with a primary focus on parallel computing, polyhedral models, and hardware acceleration. His work bridges theoretical computer science with practical applications in embedded systems and biomedical monitoring. He has made significant contributions to the fields of: Polyhedral compilation and loop optimization techniques High-level synthesis for FPGA-based hardware acceleration Systolic array design and parallel architectures Disruption-tolerant wireless sensor networks for biomedical applications Sustainable software engineering and cloud configuration Application of Large Language Models to software development Analysis of Dr. QUINTON's publication history reveals a consistent research trajectory that began with foundational work in parallel algorithms and systolic arrays, evolving toward more contemporary applications involving FPGA acceleration, polyhedral compilation, and biomedical sensor networks. His recent work shows increasing interest in sustainable computing, cloud architectures, and the application of Large Language Models to software engineering challenges. This evolution demonstrates his ability to adapt theoretical computer science concepts to address emerging technological challenges. Dr. QUINTON has supervised numerous PhD students whose research aligns with his expertise: Virginie Amand: Sustainable software services using large-scale models Alexandre Bonvoisin: Frugal software architectures for cloud-native microservices Tristan Coignion: Energy impact of Large Language Models for code Edouard Guegain: Software optimization through configuration (defended September 2023) Brell Péclard Sanwouo Chekam: Automatic detection and correction of side-channel vulnerabilities in cryptographic libraries Nada Zine: Complex and self-adaptive software systems Maxime Huyghe: Sustainable software services development based on Language Models (LLM) As a member of the Spirals research team at Inria and CRIStAL, Dr. QUINTON contributes to a collaborative environment focused on software engineering, system architecture, and sustainable computing. His work bridges theoretical computer science with practical applications in healthcare monitoring, cloud computing, and energy-efficient software development.
Yassine Ouhammou is an Associate Professor in computer science at École Nationale Supérieure de Mécanique et d'Aérotechnique (ENSMA), where he is a member of the "Real-Time and Embedded Systems" research team at LIAS laboratory. His work focuses on critical real-time embedded systems with applications in avionics, drones, and control command systems. His research interests include: Software architectures for critical real-time embedded systems Design and analysis of critical real-time systems regarding their temporal performances Model-based design using domain specific languages (MoSaRT, AADL, Capella, Time4Sys) Real-time scheduling and dimensioning Knowledge repositories for expertise capitalization, reuse and reproducibility Collaborative engineering for complex systems design Model-driven engineering and formal methods Dr. Ouhammou's publication record shows consistent contributions to real-time systems, embedded architectures, and model-based approaches. His recent work demonstrates increasing emphasis on drone technology and avionic applications, with numerous collaborations on autopilot design, scheduling optimization, and verification methodologies. His research bridges theoretical computer science with practical aerospace engineering challenges, addressing safety-critical aspects of embedded systems. Professional service includes: PC Member of MEDES 2020, INISTA 2020, WIMS, SADASC 2020 PC Chair of DETECT 2019 General Co-Chair of RTNS 2018 PC Member of multiple international conferences since 2017 Dr. Ouhammou collaborates extensively with researchers in the field of real-time systems, particularly with Emmanuel Grolleau and other members of the LIAS laboratory. His work often involves interdisciplinary teams addressing complex challenges in aerospace and embedded systems engineering, with practical applications in drone technology and avionic systems.
