Andrea Polini is a Full Professor at the University of Camerino, focusing on interdisciplinary research at the intersection of blockchain technology, business process modeling, IoT systems, and software engineering. His work emphasizes formal methods for ensuring correctness in distributed systems, smart contract security, and model-driven approaches for IoT integration. Research interests include blockchain-based choreography execution, mutation testing strategies for smart contracts (e.g., ReSuMo and SUMO tools), and frameworks for IoT application portability (X-IoT). He also investigates process mining in public administration and humanitarian contexts, such as analyzing collaboration in crisis mapping platforms like the HOT Tasking Manager. Polini’s contributions span over 60 publications since 2014, with a strong focus on practical tools and methodologies (e.g., BProVe for business process verification, FloBP for IoT-enhanced processes). His work bridges theoretical computer science with real-world applications in healthcare, urban mobility (Tangramob framework), and disaster response systems.
Mariano Ceccato is an Associate Professor in the Department of Computer Science at the University of Verona, Italy. With a PhD in Computer Science from the University of Trento (2006), he has led numerous publicly funded and industrial research projects. His expertise includes security testing, penetration testing, code hardening, and empirical studies. Principal Investigator in multiple research initiatives Visiting Research Scientist at the University of Luxembourg Author/co-author of over 100 peer-reviewed papers Research Focus His work spans Android security, Ethereum smart contracts, REST APIs, and software protection techniques. Key contributions include: Security testing frameworks using reinforcement learning Advanced static analysis for Ethereum bytecode Novel obfuscation strategies for Java/Android Empirical studies on security report usability Comparative analysis of malware detection techniques Scientific Contributions He has received multiple awards including the Best Paper at IEEE ICST 2022, Best Research Paper at IEEE/ACM ICSE 2023, and ACM Distinguished Paper awards.
Dr. Liming Zhu is a Conjoint Full Professor at the School of Computer Science and Engineering, University of New South Wales, and leads the Software and Computational Systems Research Program at Data61, CSIRO. This research program comprises over 200 personnel working across key technology domains including big data analytics infrastructure, computational science platforms, trustworthy systems, distributed systems, business process management, legal informatics, provenance tracking, behavior analytics, blockchains, and software engineering. His research expertise spans software architecture in enterprise and embedded systems, dependable and secure distributed systems, DevOps and continuous deployment methodologies, big data analytics infrastructure and pipelines, blockchain applications, software ecosystems, and model-driven development. His work intersects with multiple Fields of Research including Computer Software, Distributed Computing, Software Engineering, Computer System Security, and Data Security. Zhu's publication record demonstrates significant scholarly impact with 97 journal articles, 186 conference papers, 39 preprints, 7 book chapters, and 1 authored book. His research program at Data61 focuses on translating theoretical advances into practical systems that address real-world challenges in data management, system security, and software development processes. The research program has particular strength in developing infrastructure for computational sciences, including specialized applications in imaging processing and bioinformatics/life sciences. Software and Computational Systems Research Program, Data61, CSIRO (Leadership role) School of Computer Science and Engineering, University of New South Wales (Conjoint Full Professor) Dr. Zhu actively supervises PhD students at UNSW, having successfully guided 6 doctoral candidates to completion as primary supervisor. His teaching focuses on software architecture courses, connecting academic theory with industry practice. The research program he leads serves as a bridge between academic inquiry and practical application, working closely with industry partners to develop innovative solutions in data platforms, trustworthy systems, and software engineering practices.
Mitchell Olsthoorn is an Assistant Professor in the Software Engineering Research Group (SERG) at Delft University of Technology. He is also a member of the Computational Intelligence for Software Engineering lab (CISELab) and the Delft Blockchain Lab (DBL). His academic career is built on a strong foundation from Delft University of Technology, where he earned all his degrees. Mitchell's research interests span a diverse range of topics including Network Security, Search-based Software Engineering, Pen-testing, Computational Intelligence, Software Testing, Security Testing, Fuzzing, and Blockchain. His work demonstrates a strong focus on applying computational intelligence techniques to solve complex software engineering problems, particularly in the areas of test case generation and security testing. His publication record shows a consistent research trajectory with a focus on developing innovative testing frameworks for various programming languages and blockchain technologies. His work bridges the gap between theoretical research and practical applications, with tools developed for JavaScript, Kotlin, and blockchain platforms. Cum laude distinction (top 5% in Netherlands) Mitchell actively contributes to the academic community through conference organization and program committee roles. His current position as Hot-off-the-Press Track Co-Chair for SSBSE 2025 demonstrates his growing recognition in the software engineering research community. He also serves on program committees for major conferences including ISSTA, ICST, and ASE. Mitchell maintains active research laboratories including the Computational Intelligence for Software Engineering lab (CISELab) and the Delft Blockchain Lab, where he continues to advance research in software testing and security.
Alceste Scalas is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on formal methods in concurrency, multiparty session types, distributed systems, and programming language theory. Scalas leads the Software Systems Engineering group and actively supervises PhD students in areas such as secure distributed systems and formal verification of communication protocols. His work integrates theoretical contributions (e.g., process algebra formalisms, Petri net encodings) with practical applications like verified network APIs and automated test synthesis for RESTful systems. Recent projects include COTS (OpenAPI test synthesis) and P4R-Type (verified P4 control plane tools). Scalas' research has been applied to secure cloud-edge systems (TaRDIS project), hybrid verification methodologies, and SDN protocol validation. He co-develops tools like Effpi for verified message-passing programs and PSTMonitor for session-type monitoring. He supervises PhD candidates working on secure distributed applications, formal methods in blockchain (Algorand smart contracts), and concurrency patterns in actor systems. His work bridges theoretical computer science with real-world system verification challenges.
Prof. Hartwig Anzt is a Professor at TU Munich, leading the Chair of Computational Mathematics within the TUM School of Computation, Information, and Technology. He also holds a professorship at the University of Tennessee and directs the Innovative Computing Lab (ICL). His research focuses on high-performance computing, particularly in sparse linear algebra, iterative methods, Krylov solvers, and preconditioning. He emphasizes sustainable software development and leads the Ginkgo open-source library for scientific computing. Academically, Anzt earned his PhD in 2012 from the Karlsruhe Institute of Technology (KIT) and led a Helmholtz junior research group there. He has extensive collaborations with institutions like Sandia National Laboratories, Argonne National Laboratory, and the University of Tennessee. His software projects include Ginkgo and MAGMA-sparse, both part of the xSDK ecosystem. Recent talks highlight his work on exascale computing, GPU optimization, and software sustainability. He advocates for platform-portable numerical libraries and has contributed to the Exascale Computing Project (ECP). His research addresses challenges in energy efficiency, fault tolerance, and algorithm design for multi/manycore architectures.
Alix Decrop is a Researcher affiliated with the Faculty of Computer Science at Namur Digital Institute. Their work bridges software engineering with cutting-edge AI techniques, focusing on REST API validation, natural language processing, and automated testing frameworks. Research Focus Decrop's research targets: Automated testing and specification inference for REST APIs using large language models Integration of NLP and data mining to enhance API documentation and reliability Development of benchmarks for evaluating API quality and functionality Publication Trends Their 2024-2025 publications demonstrate a consistent focus on automating software validation processes. Key themes include LLM-driven API testing, NLP-augmented data processing, and scalable benchmarking methodologies. All works emphasize practical applications in software quality assurance. Awards Prize for the Best Master's Thesis in Computer Science, General Impact (2024) Academic Engagement Decrop actively contributes to the software engineering community through conference participation including organizational roles at ISSTA 2025 and the Belgium-Netherlands Software Evolution Workshop.
Peter Hegedus is an Assistant Professor at the Software Engineering Department of the University of Szeged and a researcher at FrontEndART Ltd. His work bridges academic research with practical software development through teaching, empirical studies, and application development projects. Research Focus: He specializes in software engineering practices, with emphasis on empirical evaluation of code quality, static analysis techniques, and machine learning applications in software development. His research integrates big data methodologies, refactoring validation, and tool development for software maintainability. Academic Contributions: His recent publications highlight dataset creation for defect prediction, ML-based alarm filtering systems, and framework designs for machine learning model interaction. These works demonstrate expertise in both theoretical and applied aspects of software engineering. Teaching Responsibilities: He leads lectures and labs for courses on Enterprise Information Systems, Big Data, and software development practices, incorporating practical assignments like creating REST API testing tools and permission-based image browsers.
Emilio Tuosto is a Full Professor at the Gran Sasso Science Institute (GSSI) in L'Aquila, Italy, where he leads the Computer Science Department. Previously, he held an Associate Professor position at the University of Leicester's School of Informatics (UK). His research focuses on formal methods, distributed systems, and behavioral specifications. He earned his PhD in Computer Science from the University of Pisa in 2003 and holds a degree from the same institution (1998). His research explores theoretical and applied aspects of complex distributed systems, including automata-based models of distributed choreographies and contract-based interaction frameworks. Key projects include BehAPI (Marie Skłodowska-Curie Actions RISE project) and DeLiCE (Decentralised Ledgers in Circular Economy). He actively contributes to conferences like COORDINATION and ECOOP, and organizes GSSI's seminar series. Recent work includes tools like TRAC for data-aware coordination, MoCheQoS for QoS analysis, and CIRC (reversible computing). His publications span formal choreographic languages, static analysis for C programs, and behavioral types for local-first software. Collaborations involve institutions like IMT Lucca, University of Pisa, and international teams. Tuosto's contributions bridge theoretical foundations with practical implementations in distributed systems and formal methods.
Ilias Gerostathopoulos is a Professor at the Chair for Software and Systems Engineering (I4) at the Technical University of Munich. His research focuses on software architecture, self-adaptive systems, big data analytics, and component-based development for cyber-physical systems. Education: PhD in Model-Driven Development of Software-Intensive Cyber-Physical Systems (Charles University, 2015) Research Venues: Active in PC member roles and track co-chair positions across conferences like ECSA, FAS*, SEAA, SASO, and ICSE. Organized workshops on evaluation metrics and data-driven software engineering. Scientific Contributions: Developed frameworks for dynamic component ensembles Advanced architectural homeostasis in adaptive systems Created toolchains for smart farming and traffic optimization Pioneered experiment-driven adaptation in cyber-physical systems Scientific Awards: Second place in ACM Student Research Competition (MODELS'14) Teaching: Offered graduate-level seminars on software architecture trends and IoT systems co-developed with Christian Prehofer.
Michele Pasqua is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Verona, Italy, where he completed his entire academic training. His career demonstrates deep institutional continuity at this Italian university. His educational pathway includes: PhD in Computer Science (2019), University of Verona MSc in Computer Science and Engineering with Security specialization (2015), University of Verona BSc in Computer Science (2013), University of Verona Pasqua's research bridges theoretical formal methods and practical security applications. He pioneered abstract interpretation techniques for hyperproperties verification in his doctoral work, later expanding to REST API security testing through novel approaches combining deep reinforcement learning and natural language processing. His scholarship consistently addresses critical vulnerabilities in emerging technologies like blockchain smart contracts and web services, demonstrating both theoretical rigor and practical impact in software security. Analysis of his publication timeline reveals a strategic evolution from foundational formal methods (2020-2023) toward empirical security testing frameworks (2023-2025), with increasing emphasis on tool development and evaluation infrastructure for REST API security. This trajectory shows growing industry relevance while maintaining strong theoretical underpinnings in program analysis. No scientific awards are documented in the available materials. His advising activities and grant funding remain unspecified, though his leadership roles in workshops like NSAD and Lipari Summer School suggest active research group supervision. Similarly, no dedicated laboratory facilities are mentioned in the provided texts.
Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.
Professor Daniel Sundmark is affiliated with Mälardalen University's School of Innovation, Design and Engineering, specifically within the Division of Computer Science and Software Engineering. His research focuses on software testing methodologies, embedded systems, fault management, and resilience engineering. He has published extensively on topics such as API testing, concurrency bugs, and safety-critical systems. Key research areas include: Model-based testing frameworks Automated test generation for embedded systems Resilience and fault recovery in networking systems Requirements engineering and similarity analysis His work spans both academic contributions and industrial collaborations, addressing challenges in automotive, railway, and telecommunications domains. Recent publications emphasize resilient system design, real-time systems, and automated testing strategies. Notable projects include the Alcea architecture analysis method and studies on fault management frameworks. He also explores integration testing challenges and the role of test automation in agile development. His research outputs demonstrate a strong focus on bridging theory and practice, with over 50 peer-reviewed articles since 2001 covering topics from concurrency bug detection to TDD optimization.
Christopher Olbertz is a lecturer at the Computer Science Department within the College of Engineering at Saarland University of Applied Sciences. He teaches programming and software development courses, focusing on languages like C++, Java, Python, and Kotlin, as well as frameworks such as Spring and SpringBoot. His teaching emphasizes practical applications in software architecture, database systems, and web technologies. His research interests include: Object-Oriented Programming (C++ vs. Java) Software Design Patterns Dependency Injection and Layered Architecture Automated Testing and Performance Evaluation Cross-Platform Application Development Modern Java Features (Lambda Expressions, Collection API) He supervises bachelor theses on topics ranging from web-based applications and REST APIs to software testing and cross-platform development, often in collaboration with industry partners like Mercedes Benz Bank, ZF Friedrichshafen, and SAP Hybris.
Seán Russell is an Assistant Professor in Computer Science at University College Dublin (UCD), affiliated with the School of Computer Science. He holds a BSc and PhD from UCD, as well as a Professional Diploma in University Teaching and Learning. His research spans agent-based simulation systems, multi-agent architectures, and computer science education. Notable contributions include work on hypermedia MAS, microservices for scalable simulations, and pedagogical strategies for non-native English speakers in CS1 courses. Education: BSc in Computer Science, University College Dublin PhD in Computer Science (details unspecified) Professional Diploma in University Teaching and Learning, University College Dublin Research Interests: Russell focuses on agent-oriented programming, distributed systems (microservices/REST), and innovative teaching methodologies. His work bridges theoretical agent architectures with practical applications like traffic simulation and waste management. Recent education-focused research addresses code quality in CS1, error message comprehension, and linguistic barriers for non-native English students. Grants & Awards: UCD College of Science Teaching Excellence Award (2022) OPTI-ROUTE Grant (2017–2018) Teaching & Learning Award (2021/2022) Teaching & Leadership: Coordinates modules in programming fundamentals (e.g., Intro to Programming 1, Object-Oriented Design) and serves as Vice Chair of the Ireland ACM SIGCSE Chapter. Supervised a PhD student (2019–2024).
