Sergiy Bogomolov is an Associate Professor in Cyber-Physical Systems at the School of Computing, Newcastle University, UK. His research focuses on developing algorithms and tools for modeling and analyzing complex systems, with a particular emphasis on formal verification, control theory, and artificial intelligence applications in cyber-physical systems. He has over 40 publications in top venues such as EMSOFT, HSCC, AAAI, and IJCAI, and has won multiple awards including Best Paper awards at HSCC'16 and HVC'14. His work emphasizes scalable solutions for hybrid systems analysis and has been supported by agencies like the US Air Force and the Australian Defence Science and Technology Group. Education: PhD and M.Sc. from the University of Freiburg, Germany. He previously held positions at ANU (Australia) and IST Austria as a postdoc. Research interests include hybrid systems reachability analysis, safety verification, and the integration of AI techniques with formal methods. His software contributions include SpaceEx extensions and the JuliaReach toolbox. Scientific awards include Best Repeatability Evaluation Package Award (HSCC'16), Best Tool Award (ARCH'16), and Best Paper Award (HVC'14). He advises PhD students Kostiantyn Potomkin and Abdelrahman Hekal, and collaborates on projects like parameter synthesis and autonomous systems safety.
Derek Rayside is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, and serves as Associate Dean for Co-operative Education and Professional Affairs. His research spans autonomous systems, software engineering, formal methods, and construction project management. He holds a faculty position in a top-tier engineering program with affiliations to both academic and administrative roles at the university. Research interests include advancing autonomous vehicle decision-making through reinforcement learning and computer vision techniques, improving software development methodologies via formal verification and collaborative platforms, and enhancing project management practices in complex construction environments. His work integrates interdisciplinary approaches combining control theory, machine learning, and social network analysis. Recent publications focus on autonomous driving challenges (action recognition, intersection navigation), software engineering education (game-based learning), and formal methods for system validation. His contributions bridge theoretical advancements with practical applications in safety-critical systems and industrial workflows. Teaching responsibilities include the ECE351 course on signals and systems. He has led curriculum development efforts and maintains a personal research website at the University of Waterloo's ECE department.
Doğan Ulus is an Assistant Professor at the Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey. He holds a PhD in Computer Science from Université Grenoble Alpes (2018) and BSc/MSc degrees in Electrical Engineering from Boğaziçi University (2011, 2013). His research focuses on developing testing, verification, and validation tools for complex cyber-physical systems, particularly automated driving systems and autonomous robots. Education: PhD, Université Grenoble Alpes (2018) MS, Boğaziçi University (2013) BS, Boğaziçi University (2011) His primary research interests include runtime verification , simulation-based testing , formal methods , and automata theory , with applications in safety validation of autonomous systems. He previously worked as a postdoctoral researcher at Boston University Robotics Lab and as a Senior Verification Engineer at Samsung Semiconductor, Inc., in San Jose, CA, USA. Doğan contributes to academic committees such as the Systems and Networks Committee and Web Committee at Boğaziçi University. His work emphasizes the integration of formal methods with practical software development practices for robust system validation.
Thomas Preindl is a Researcher at the Automation Systems department of TU Wien's Faculty of Mechanical and Automotive Engineering and Transportation. His work focuses on blockchain technology, BIM integration, and cyber-physical systems, with applications in construction, energy systems, and Industry 4.0. He holds a BSc and a diploma in engineering from TU Wien. Education : BSc in Engineering, TU Wien (year not specified); Diploma in Automation Engineering, TU Wien (2019). Research : Preindl’s research explores blockchain applications in process automation, decentralized systems, and circular economy. Notable projects include DiCYCLE (2022–2026), exploring blockchain for sustainable construction, and FORA (2017–2021) on facility management frameworks. His work often bridges BIM with smart contracts to enhance transparency and efficiency in construction workflows. Articles : His recent publications address confidential process execution on blockchain, storage optimization for containerized systems, and BIM-integrated blockchain tools. Key themes include decentralized frameworks, fault detection, and energy system dependability. Grants & Projects : Funded by the Austrian Research Promotion Agency (FFG), his projects span 2015–2027, focusing on circular economy, energy data spaces, and IoT integration in industrial systems. Advising : Supervised 6 diploma theses on topics like zero-knowledge proofs in BPM, decentralized storage systems, and BIM data management via smart contracts. Labs/Teams : Active in TU Wien’s Automation Systems group, contributing to interdisciplinary projects in industrial automation and digital transformation.
Willard Rafnsson is an Associate Professor in Programming Logic and Semantics at IT University of Copenhagen. His research develops formal methods for security and privacy, particularly probabilistic programming approaches for quantifying privacy risks. Research investigates privacy risk assessment methodologies, attacker knowledge modeling, and formal verification techniques. Recent work examines privacy vulnerabilities in genetic data and develops tools for leakage quantification. Rafnsson leads projects on practical static analysis and reliable AI for autonomous systems, focusing on security foundations.
Pantelis Frangoudis is a PostDoctoral Researcher at the Distributed Systems Group of the Vienna University of Technology since 2019. His academic journey includes a Ph.D. in Computer Science (2012), M.Sc. (2005), and B.Sc. (2003) from the Department of Informatics, Athens University of Economics and Business (AUEB) , Greece. His research spans Edge Computing , IoT , Network Softwarization , Federated Learning , and Wireless Networking , focusing on adaptive systems and security. Education B.Sc. in Computer Science (2003), AUEB M.Sc. in Computer Science (2005), AUEB Ph.D. in Computer Science (2012), AUEB His research explores Edge Computing Continuum systems, with emphasis on QoS-aware orchestration , serverless edge deployment , and self-adaptive IoT services . Recent work addresses federated learning for 6G , WebAssembly for edge serverless , and dynamic resource allocation in heterogeneous environments. Key projects include AloTwin (2023–2025), INTEND (2024–2026), and EDENSPACE (2019–2022). His publications (2025–2020) cover machine learning for 5G/6G , MEC application orchestration , and IoT security , with a focus on distributed systems and edge-cloud collaboration . Scientific Awards ERCIM Alain Bensoussan Post-Doctoral Fellowship He has supervised multiple diploma theses at TU Vienna, including topics like serverless edge computing , traffic light AI optimization , and vertical farming IoT systems . His collaborations with institutions like EURECOM (France) and IRISA/INRIA (France) reflect a global academic footprint.
Fikret Çalışkan serves as a Professor in the Department of Control and Automation Engineering at Istanbul Technical University. With an h-index of 15 and 55 research outputs documented through Scopus, his academic career spans multiple decades of contributions to control systems engineering. His research profile shows consistent publication activity from 1995 through projected 2025 works, with significant output in recent years. Professor Çalışkan's research focuses on advanced control systems with particular expertise in Kalman filtering techniques , fault detection and isolation , and autonomous aircraft systems . His fingerprint analysis reveals strong specialization in actuator systems (80%), Kalman filters (100%), and multiple fault detection methodologies (47-53%). His work bridges theoretical control engineering with practical applications in UAV technology, indoor positioning systems, and aircraft propulsion. Analysis of his recent publications shows a clear trajectory toward increasingly complex autonomous systems, with growing integration of machine learning techniques (particularly reinforcement learning) with traditional control methodologies. His work demonstrates strong interdisciplinary connections between aerospace engineering, robotics, and signal processing, with applications spanning from indoor navigation to hybrid-electric aircraft propulsion. Professor Çalışkan has supervised 15 research projects throughout his career, securing funding for significant research initiatives including AI-based visual inspection systems for aircraft surfaces (2020-2022) and nonlinear multirotor modeling, fault detection, and implementation (2018-2021). His research demonstrates strong industry relevance with practical applications in aviation safety, autonomous systems, and energy-efficient control solutions.
Professor Antonis Papachristodoulou is a faculty member at the University of Oxford , serving as the Professor of Control Engineering and an Official Fellow at Kellogg College . He previously held roles as a Tutorial Fellow at Worcester College (2010-2024), EPSRC Fellow (2015-2021), and Director of the EPSRC & BBSRC Centre for Doctoral Training in Synthetic Biology (2014-2023). His academic journey includes an MA/MEng in Electrical and Information Sciences from the University of Cambridge (2000) and a PhD in Control and Dynamical Systems at the California Institute of Technology (2005) with a minor in Aeronautics. His research spans Control Engineering , Systems Biology , and Synthetic Biology , focusing on robust analysis of nonlinear networked systems, Sum of Squares optimization, and applications in biological systems, fluid mechanics, and smart grids. His recent publications highlight advancements in Distributed Control , Neural ODE-based Controllers , and Safe Reinforcement Learning . Scientific Awards: European Control Award (2015) O. Hugo Schuck Best Paper Award IEEE Fellow He leads the SYSOS (System of Systems) Group , collaborating with Oxford Biochemistry, Engineering Science, and international institutions. His grants include the EPSRC Programme Grant EEBio and co-I roles in bioengineering and autonomous systems initiatives.
Dr. Neelofar Neelofar is a Lecturer at RMIT University's School of Computing Technologies , specializing in Automated Software Engineering and AI systems testing. She holds a PhD from the University of Melbourne and has held academic positions at Monash University since 2021. Current Role: Lecturer, RMIT University (City Campus, Australia) Research Focus: Automated Software Engineering, Autonomous Vehicle Testing, and Responsible AI Her research addresses critical challenges in: Safety validation of AI-based systems Search-based testing methodologies Quality assessment of Large Language Models Algorithmic fairness and benchmarking Notable contributions include developing: Instance Space Analysis for test scenario evaluation Position-based adversarial testing frameworks for autonomous vehicles Hybrid fault localization techniques combining static and dynamic analysis
Michalis Famelis is an Assistant Professor at the Department of Computer Science and Operations Research , affiliated with the Faculty of Arts and Sciences at Université de Montréal . He leads research in the GEODES Software Engineering Research Group , focusing on formal yet practical methods for software development. His work integrates formal verification, model-driven engineering, and empirical methods to address challenges in software design and uncertainty management. Educated at the University of Toronto (PhD 2016, MSc 2010) and the National Technical University of Athens (DiplEng 2008), he completed a postdoctoral fellowship at the University of British Columbia . He teaches courses such as IFT1025 Programming 2 and IFT6755 Software Analysis . His research projects include a Wellcome Trust-funded platform for climate-sensitive disease modeling and CRSNG grants for formal software design support. He has supervised 6 master’s students, focusing on topics like design uncertainty, API usage verification, and software product lines. Notable collaborations involve Climate-Sensitive Infectious Disease Modelling and Formal Support for Software Design . His work emphasizes improving developer workflows through tool development and empirical studies.
Dr. Blair Archibald is a Lecturer in the School of Computing Science at the University of Glasgow. He holds a PhD in Computing Science from the same institution (2018). Previously, he was a Research Associate on the Science of Sensor Systems (S4) project. He is a member of the Systems, PLUG, and FATA research groups and a Software Sustainability Institute Fellow since 2017. His research focuses on computational modeling of complex systems using formal methods like Milner's Bigraphs and probabilistic model checking. He also investigates parallel and distributed computing, programming languages, and functional programming. His work emphasizes making formal methods accessible to non-experts through graphical techniques and tools such as BigraphER. Archibald has contributed to frameworks like YewPar, a C++ library for parallel combinatorial search, and has explored applications in transport systems resilience and human-swarm interaction. His interdisciplinary approach aims to apply formal methods to real-world challenges, such as decarbonizing transport through digital twinning. Key awards include the Software Sustainability Institute Fellowship (2017). His current research interests span BDI agent verification, probabilistic bigraphs, and scalable parallel algorithms. He is actively involved in supervising PhD students in these areas. Lab/Team Affiliations: Systems Research Group, PLUG (Programming Languages and User Interfaces Group), and FATA (Formal Analysis, Theory and Algorithms) at the University of Glasgow.
Suresh Perinpanayagam is Professor of Engineering at the University of York, where he leads transformative research in digital/data-centric engineering, digital twins, and AI. His work aims to revolutionize system design by leveraging data and high-performance computing to provide a more realistic and synergistic approach to complex future systems. He is affiliated with the School of Physics, Engineering and Technology at the University of York, where he has established the Data-Centric Engineering and Digital Twinning Synergy (DACEDITS) research group. Professor Perinpanayagam holds a Bachelor's and Master's degree in Aeronautical Engineering from Imperial College, London, and a PhD in Mechanical Engineering from Imperial College, London (Rolls-Royce Vibration University Technology Centre). His research focuses on harnessing digital technologies to revolutionize engineering design, control, development, and through-life supportability within aerospace, transport, energy and built infrastructure domains. Digital twins form a cornerstone of his work, creating virtual replicas of physical systems that are continually updated with real-time data for remote monitoring and predictive analytics. His team combines advanced modeling and simulation with data analytics and machine learning algorithms to gain actionable intelligence from real-time data, facilitating predictive maintenance and fault detection. Key application areas include fusion energy systems, electric/hydrogen aircraft, and autonomous transport vehicles, where the goal is to minimize extensive testing and validation while addressing global challenges in energy, electrification, circular economy practices, and net-zero emissions goals. Analysis of Professor Perinpanayagam's recent publications reveals a strong focus on applying digital twin technology and machine learning to critical engineering systems. His research spans aerospace applications (particularly for more electric aircraft), railway systems, and power electronics reliability. A notable trend is the increasing emphasis on explainable AI for safety-critical systems in aerospace, addressing certification challenges while maintaining high reliability standards. His work consistently bridges theoretical advancements with practical industrial applications, particularly in collaboration with major aerospace companies. Professor Perinpanayagam has secured research grants exceeding £5 million throughout his career. He has cultivated extensive industrial collaborations with leading companies including Boeing, Rolls-Royce, BAE Systems, Thales, Airbus Group, Safran, Meggitt, UKAEA, Heathrow Airport, Assystem, Awaretag and Chitendai Ltd. He has served as Principal Investigator for significant projects such as the Future Landing Gear Phase 2 project (£2 million) and the LAND One project with Airbus, as well as a £1 million project from Safran/ATI for the OLLGA project. As an educator and mentor, Professor Perinpanayagam has been the principal supervisor for seven PhD candidates and one Master's by Research student, all of whom have successfully completed their degrees. He has also supervised Individual Research Projects for thirty-five Master's students. His teaching encompasses data-centric engineering for intelligent systems, covering machine learning, digital twin technology, intelligent transport systems, IoT/sensory systems, predictive analytics, asset management, resilience engineering, project management, and system availability and maintainability. Professor Perinpanayagam leads the Data-Centric Engineering and Digital Twinning Synergy (DACEDITS) research group at the University of York, which pioneers the integration of digital and data technologies to revolutionize engineering design and support. His team brings together cross-disciplinary expertise in advanced modeling and simulation, data analytics, and artificial intelligence to develop next-generation engineering systems that are highly efficient, reliable, and economically viable. The group maintains strong industry partnerships that facilitate the translation of research into practical applications.
Dr Colin Paterson is a Senior Lecturer in Machine Learning Safety at the University of York's Department of Computer Science, working within the Institute for Safe Autonomy (ISA). His research focuses on ensuring safety in autonomous systems, particularly addressing uncertainties in deployed systems. He holds a PhD in Computer Science (2018, York) and a prior PhD in Control Systems Engineering (1993, Coventry), alongside qualifications in education and business. Education: PhD: Computer Science, University of York (2018) PhD: Control Systems Engineering, Coventry University (1993) PGCE: Secondary Education, Leeds Trinity University (2012) BA: Business, Finance, Mathematics, Open University (2011) BEng: Computer & Control Systems Engineering, Coventry University (1990) His research interests span autonomous systems engineering, machine learning assurance, ethical AI design, and verification methodologies. He contributes to the development of safety frameworks for self-adaptive systems and has published extensively on topics like runtime safety monitors, hazard mitigation in autonomous vehicles, and transfer assurance in machine learning. Dr Paterson currently serves as GTA Coordinator and Training Officer within the department. His work emphasizes bridging theoretical safety models with practical implementation in real-world autonomous systems.
Dr. Dimitar Petrov is an Associate Professor in Computer Science at Ca’ Foscari University of Venice, specializing in static analysis and cybersecurity. He is affiliated with ETH Zürich's Institute of Pharmaceutical Sciences (IPW) as staff under Tit.-Prof. Jörg Scheuermann. His research focuses on applying abstract interpretation-based methods to detect security vulnerabilities in systems ranging from blockchain smart contracts to IoT devices. Education details are not explicitly provided in the text, but his extensive publication history indicates advanced expertise in formal methods. His research interests include software verification, privacy enforcement, and the application of static analysis tools like LiSA across diverse domains such as robotics, microservices, and mobile applications. Key research trends in his articles emphasize blockchain security (smart contract vulnerabilities, consensus protocols), IoT/IoMT security (device interactions, privacy policies), and the integration of machine learning with program analysis. His work bridges academic research with industry challenges, addressing compliance with regulations like GDPR and the EU Data Act. Prior to his current roles, he has contributed to open-source frameworks like LiSA and collaborated on projects involving automated policy extraction, cross-language analysis, and vulnerability detection in automotive systems. His research group at Ca’ Foscari actively engages in both theoretical advancements and practical tool development. Labs/Teams: Part of the Software and System Verification group at Ca’ Foscari, and collaborates with ETH Zürich's Institute of Pharmaceutical Sciences on interdisciplinary projects combining formal methods with healthcare technology.
Dr. Gethin Norman is a Senior Lecturer in the School of Computing Science at the University of Glasgow, where he serves as Deputy Head of School and Senior Adviser. He holds a BSc in Mathematics from the University of Oxford and a PhD in Computer Science from the University of Birmingham. His research focuses on formal verification, quantitative methods, and probabilistic systems, with applications in software security, systems biology, and game theory. A key contribution is his work on the PRISM probabilistic model checker, which received the 2016 HVC Award for its impact in formal verification. He also leads the Formal Methods research group under the Formal Analysis, Theory and Algorithms section. Education: BSc Mathematics, University of Oxford PhD Computer Science, University of Birmingham Research Interests: Formal verification of real-time and probabilistic systems Algorithmic game theory and equilibrium analysis Development of tools like PRISM and PRISM-games Applications in security protocols, robotics, and biological systems Teaching: Coordinates undergraduate courses: Algorithmic Foundations 2 (COMPSCI2003) and Algorithmics I (H) (COMPSCI4009) Awards: 2016 HVC Award for PRISM probabilistic model checker. Labs/Teams: Core contributor to the PRISM model checking tool, leading research in formal methods and stochastic game verification.
