Julian Gutierrez is a Lecturer at the University of Oxford's Department of Computer Science and an Associate Member of the same department. He holds a Ph.D. in Informatics from the University of Edinburgh (2011) and has held postdoctoral roles at the University of Cambridge and Oxford. His research focuses on theoretical computer science, artificial intelligence, logic, and concurrency, particularly in game theory, verification, and multi-agent systems. Education: Ph.D. in Informatics (University of Edinburgh, 2011), Engineering degrees in Computer Science and Electronics (Pontificia Universidad Javeriana, Colombia). His research interests emphasize logic and games in AI, concurrency semantics, and verification of multi-agent systems. Key contributions include work on Nash equilibria, rational verification, and tool development for equilibrium analysis. He has authored over 15 papers in top venues like IJCAI, AAMAS, and CONCUR. Awards: Overseas Research Studentship Award, School of Informatics Scholar, multiple academic excellence distinctions, and INFIVALLE Scholarship. He advises students in theoretical computer science and collaborates with the Avispa Research Group. His academic genealogy traces to Turing and Gauss through notable figures like Alan Turing and Alonzo Church.
Dr Felix M. Simon is a Postdoctoral Research Fellow in AI and Digital News at the Reuters Institute for the Study of Journalism , University of Oxford, and a Research Associate at the Oxford Internet Institute (OII) . He was previously a Knight News Innovation Fellow at Columbia University’s Tow Center for Digital Journalism (2021-2024) and holds affiliations with the Center for Information, Technology, and Public Life (CITAP) at the University of North Carolina at Chapel Hill. His research focuses on AI’s impact on News production and distribution Democratic discourse Political economy of technology-media power dynamics Misinformation and disinformation with a particular emphasis on generative AI applications and election-related information integrity. Recent publications include studies on AI chatbot responses during UK and European elections, misinformation trends, and news innovation strategies. He received the Hans Bausch Media Prize (2023) and has secured grants from the Leverhulme Trust, Minderoo-Oxford Challenge Fund, and Balliol College. Simon holds a DPhil in Communication (OII, with distinction) MSc in Social Science of the Internet (OII) BA in Film and Media Studies (Goethe-University Frankfurt) and advises media organizations and NGOs on AI, journalism, and democracy-related issues.
André de Matos Pedro is an Assistant Professor in the Department of Computer Science at the University of Beira Interior. He teaches courses including Teoria da Computação (Theory of Computation), Programação Funcional (Functional Programming), and Segurança e Fiabilidade de Software (Software Security and Reliability). His research focuses on formal methods, runtime verification, and programming language theory. His publication record shows consistent focus on formal verification methods applied to real-time and embedded systems. Recent work emphasizes runtime monitoring frameworks, SAT/SMT-based verification techniques, and applications in safety-critical domains like autopilot systems. The research trajectory demonstrates increasing emphasis on practical applications of temporal logic and co-simulation testing platforms.
Konstantin Korovin is an Associate Professor and Reader in Formal Methods at the University of Manchester. He leads the Formal Methods Research Group and is a core developer of the iProver theorem prover, a tool for automated reasoning in first-order logic with applications to verification, neuro-symbolic systems, and machine learning integration. His work focuses on combining automated reasoning techniques with machine learning, particularly in areas like premise selection, neural architecture for term synthesis, and hybrid verification systems. Affiliations: Centre for Digital Trust and Society, SCorCH Project (Secure Code for Capability Hardware) Research Beacons: Digital Futures Key research interests include automated theorem proving, verification of machine learning models, non-linear constraint solving, and neuro-symbolic reasoning. He has contributed to tools like ESBMC (for C++ program verification) and SMLP (a symbolic machine learning prover). His work spans theoretical advancements in superposition calculus and practical applications in hardware verification and systems biology. Collaborations include projects on DNA-based computing, robotic scientific discovery (e.g., Genesis), and formal methods for industrial hardware verification. Korovin’s research is supported by grants from the Engineering and Physical Sciences Research Council (EPSRC) and industry partnerships.
Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.
Dr. Yang Deng is a tenure-track Assistant Professor at the School of Computing and Information Systems, Singapore Management University, and a Lee Kong Chian Fellow. Previously, he was a Postdoctoral Research Fellow at NExT++ (National University of Singapore). His research focuses on Natural Language Processing, Information Retrieval, and Large Language Models, with special interests in Proactive Conversational AI, Trustworthiness of LLMs, and Human-Centered Information Seeking. He has published over 40 papers in top-tier venues including ACL, EMNLP, WWW, and SIGIR. PhD from The Chinese University of Hong Kong (2023) Research Advisor to CHEANG Chi Seng Research Domains: Natural Language Processing Information Retrieval Large Language Models Human-Agent Interaction Digital Transformation Trustworthy AI Scientific Recognition: Lee Kong Chian Fellowship Google South Asia & Southeast Asia Research Awards 2024 EMNLP 2024 Outstanding Area Chair NeurIPS 2024 Best Reviewer
M.Sc. Pascal Esser is a researcher at the Department of Informatics at Technical University of Munich (TUM). He specializes in theoretical computer science, formal methods, and machine learning, with a focus on neural networks and verification techniques. His teaching responsibilities include courses on theoretical computer science fundamentals such as Petri Nets, Automata and Formal Languages, Logic, and Model Checking. He has contributed to research in representation learning, graph neural networks, and probabilistic models, as evidenced by his recent publications. Esser is involved in the development of tools like Automata Tutor and has collaborated on projects such as PaVeS and ConVeY. His work bridges formal methods and artificial intelligence, emphasizing rigorous theoretical foundations while exploring practical applications in neural network verification and algorithm design. Education: Master of Science in Computer Science (degree details unspecified). Research Interests: Formal verification, machine learning theory, neural networks, representation learning, graph algorithms, and theoretical computer science. Professional Activities: Active in teaching advanced undergraduate and graduate courses since 2020, with a focus on foundational topics in informatics and emerging areas like neural network verification. Egger's research trends emphasize interdisciplinary approaches, combining insights from statistical learning theory with algorithmic analysis to address challenges in modern AI systems. His publications highlight advancements in understanding model dynamics, kernel-based methods, and graph neural network architectures. While no specific grants or awards are listed, his sustained academic contributions indicate active engagement in the informatics research community. He is part of a research group at TUM including notable figures like Javier Esparza and Jan Křetínský, contributing to tools and frameworks for automata theory and model checking. His work often intersects with practical software implementations such as the Automata Tutor educational platform and Strix verification tools.
Marcello La Rosa is a Professor in the field of Business Process Management at Queensland University of Technology. His work focuses on process mining, workflow systems, and business process modeling. He has contributed extensively to research on process variability, predictive monitoring, and automated discovery techniques for business processes. His research has been published in top-tier journals such as Information Systems , ACM Transactions on Management Information Systems , and IEEE Transactions on Knowledge and Data Engineering . La Rosa's research interests include process model repositories, conformance checking, and the application of machine learning in process analytics. His work bridges theoretical advancements with practical tools such as the Apromore platform, which supports process model management and analysis. He has collaborated internationally on projects involving business process configuration, blockchain integration, and event log analysis. His contributions span over 134 publications and include seminal works on process mining manifesto, configurable process models, and drift detection in business processes. He has also co-edited special issues on BPM workshops and contributed to standards in workflow management systems like YAWL.
Professor Tony Sahama is a leading academic in Health Informatics and E-Health Systems at Edith Cowan University, Australia. His primary affiliation is with the School of Electrical Engineering, Computing, and Mathematical Sciences, specializing in the Department of Computer Science and Software Engineering. Dr. Sahama's research focuses on enhancing healthcare systems through innovative technologies like blockchain, IoT, and big data analytics, with an emphasis on privacy, security, and patient empowerment. His work spans electronic health records (EHR), clinical decision support systems (CDSS), and data governance frameworks to improve healthcare outcomes and interoperability. He has contributed to over 178 publications, including seminal works on information accountability frameworks, log-based privacy audits, and blockchain applications in healthcare. His research also addresses challenges in data sharing, patient access control, and integrating traditional medical systems with modern informatics. Sahama is actively involved in international conferences and collaborations, driving advancements in health informatics policy and technology adoption globally.
Georgios Manis is an Associate Professor in the Department of Computer Science and Engineering at the School of Engineering, University of Ioannina, Greece. He holds a PhD from the National Technical University of Athens and has been a faculty member at the University of Ioannina since 2002, progressing from Lecturer to Associate Professor in 2018. He has also served as temporary teaching staff at the University of Patras, University of Crete, and University of Ioannina in the late 1990s and early 2000s. Education: B.Sc. in Computer Engineering (Diploma), National Technical University of Athens (NTUA), 1987–1992 MSc in Advanced Methods in Computer Science (Distributed and Parallel Systems), Queen Mary, University of London, 1992–1993 PhD in Computer Engineering, NTUA, School of Electrical and Computer Engineering, 1993–1997 His research interests lie at the intersection of Biomedical Engineering and Computing Systems , with a strong emphasis on Biomedical Signal Processing , Entropy Analysis , and Machine Learning . He has pioneered work in Bubble Entropy —a parameter-free entropy measure—and developed fast algorithms for entropy computation. His work also extends to compiler design and parallel computing, particularly in the automatic parallelization of recursive functions and loops. The trends in his recent publications reflect a dual focus: (1) biomedical applications involving entropy, heart rate analysis, and disease diagnosis using machine learning (especially Random Forests and SVMs), and (2) high-performance computing, including parallelization techniques and compiler optimizations for multi-core and SVP architectures. His research is highly interdisciplinary, combining signal processing, algorithm design, and clinical applications. Scientific Leadership and Recognition: Guest Editor, Special Issue on “Entropy in Biomedical Engineering”, Entropy (MDPI) Member of the IPAN Laboratory, University of Ioannina Active contributor to IEEE, Elsevier, and MDPI journals He has supervised several graduate students and is involved in funded research projects such as Palimpsest and Homore , focusing on smart systems for cultural interaction and elderly monitoring. His advising contributions are evident in co-authored papers with students like Evanthia Tripoliti and Aristeidis Mastoras. He teaches both undergraduate and postgraduate courses, including Compilers I/II and Biomedical Data Analysis . Laboratories and Teams: He is a member of the IPAN lab at the University of Ioannina, which supports interdisciplinary research in informatics and biomedical applications. His collaborative network includes researchers from Greece and abroad, particularly in the fields of biomedical signal analysis and entropy-based methods.
Andrei Popescu is a Senior Lecturer (Associate Professor level) in the Department of Computer Science at the University of Sheffield, where he conducts research in formal methods, proof assistants, and information flow security. He previously held academic positions at Middlesex University and TU Munich. University: University of Sheffield Department: Department of Computer Science Previous Affiliations: Middlesex University, TU Munich His research focuses on the logical foundations and practical applications of proof assistants, particularly Isabelle/HOL. He has made foundational contributions to inductive and coinductive datatypes, syntax with bindings, higher-order logic, and the formal verification of secure systems. His work bridges theoretical logic with real-world systems such as conference management (CoCon) and social media platforms (CoSMeDis). The recent publications highlight a strong trend in formalizing deep logical results (e.g., Gödel’s incompleteness theorems), advancing datatype theory, verifying complex security properties, and applying formal methods to practical systems. His work consistently appears in top-tier venues such as POPL, CAV, ITP, and CSF. Distinguished Paper Award at POPL 2025 Distinguished Paper Award at POPL 2024 Distinguished Paper Award at POPL 2023 RS 3 Best Paper Award for 2012–2013 He has advised PhD students including Lorenzo Gheri and has been actively involved in organizing major academic events such as the Midlands Graduate School, CPP, ITP, and TABLEAUX conferences. He has served on numerous program committees including POPL, ITP, CSF, and CAV, and has led research projects funded by VeTSS and industrial partners. He is a key contributor to the Isabelle proof assistant ecosystem, particularly in the development of the (co)datatype package and foundational consistency results. His work combines deep theoretical insight with practical implementation, making significant impacts in both academia and applied security.
Jackie Chi Kit Cheung is an Associate Professor at the School of Computer Science, McGill University , and holds the Canada CIFAR AI Chair at Mila - Quebec AI Institute. His research bridges Natural Language Processing (NLP) with insights from linguistics and psychology, focusing on system evaluation, automatic summarization , and computational semantics . Key Affiliations : Mila - Quebec AI Institute, Centre for Research on Brain, Language and Music, Centre for Intelligent Machines His lab develops state-of-the-art NLP systems while proposing challenge datasets and evaluation measures. Research emphasizes broader applications in education , health , and language revitalization . Recent work includes the ACL 2024 COSMIC framework for summarization evaluation (SAC Award). He teaches advanced courses like Formal and Neural Models of Pragmatics (Winter 2024) and Evaluation of NLP Systems (Winter 2025). His group includes 10 PhD students, 8 Master's students, and active collaborations with institutions like Mila and SRI International. Scientific Recognition : Canada CIFAR AI Chair SAC Award for ACL 2024 paper Current projects explore representational harms in LLMs , long-context modeling , and mechanistic hallucination mitigation . Students and co-supervised researchers work on fairness, robustness, and commonsense reasoning.
Maria Cusson is a Clinical Associate Professor of Physical Therapy at Quinnipiac University, currently serving as the DPT Director of Clinical Education . She holds dual credentials as a licensed Physical Therapist (PT) and Juris Doctor (JD), with an MS in Allied Health from the University of Connecticut. Her roles span teaching, clinical education leadership, and policy development across the Schools of Medicine, Nursing, and Health Sciences. Education : BS in Physical Therapy (UCONN), MS in Allied Health (UCONN), JD (Quinnipiac University) Her research focuses on clinical education models , legal risks in healthcare training , and mental health impacts during clinical rotations . She has pioneered adaptive programs for children with limb differences and explored pandemic-related disruptions in physical therapy education. Her work bridges healthcare law and clinical pedagogy, emphasizing compliance and student support. Maria's recent publications highlight trends in virtual learning communities , legal frameworks for clinical placements , and innovative rehabilitation strategies . She actively engages in professional development through presentations at conferences like the American Physical Therapy Association's Education Leadership Conference. As a faculty advisor for Camp NoLimits , she advocates for inclusive healthcare access. Her institutional roles include chairing the Clinical Education and Compliance Committee and contributing to university-wide policy reforms. Contact: Office Location - Medicine, Nursing, Health Sci 367K; Email: Maria.Cusson@quinnipiac.edu.
Ludovic Apvrille is a Professor at Telecom Paris (Institut Polytechnique de Paris), where he leads research in the Communications and Electronics (Comelec) department and previously headed the LabSoC (Laboratory on System on Chip). His work focuses on embedded systems design , with particular emphasis on safety, security, and formal verification of complex systems including automotive applications, drones, and cyber-physical systems. Research Areas: Embedded Systems, Cybersecurity, Model-Driven Engineering, Formal Verification, AI-assisted Design Key Tools: TTool, SysML-Sec, AVATAR, SMASHUP, DIPLODOCUS His recent publications analyze security vulnerabilities in RISC-V architectures using the gem5 simulator, while his ongoing work explores AI integration in system modeling and unified verification techniques for hardware/software co-designs. He actively supervises research projects in safety-security-performance trade-offs and microarchitectural security , with applications to autonomous vehicles and critical infrastructure systems. Grants & Projects: EVITA project, PEPR-5G HISEC, MoVe4SPS, PEPR-Security ARSENE
Peng Fu is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His academic career focuses on the theoretical foundations of programming languages with particular emphasis on quantum computing applications. Dr. Fu received his educational training from prestigious institutions: Ph.D. in Computer Science from University of Iowa (2014) B.Eng. in Computer Science from Huazhong University of Science and Technology (2009) Dr. Fu's research program centers around type theories, quantum programming languages, and their categorical semantics . His work bridges the gap between theoretical computer science and practical quantum computing applications. He develops formal systems that enable reliable quantum circuit programming through strong type systems and categorical models. His research has significant implications for the future of quantum software development, where correctness and reliability are paramount due to the fragile nature of quantum states. An analysis of Dr. Fu's publication record reveals a consistent trajectory toward increasingly sophisticated quantum programming frameworks. Starting with foundational work on lambda encodings and type theory, he has progressively focused on quantum-specific challenges. His recent papers on Proto-Quipper variants demonstrate innovative approaches to quantum circuit programming with features like dynamic lifting, reversing, and control structures. The research shows strong integration of category theory with practical programming language design, creating bridges between abstract mathematical structures and executable quantum code. Dr. Fu actively mentors the next generation of computer scientists, currently seeking Ph.D. students to join his research group. His teaching portfolio includes advanced courses such as CSCE 790: Quantum Programming Languages (Spring 2025) and CSCE 544: Functional Programming (Fall 2024), where he introduces students to cutting-edge concepts in programming language theory and quantum computing. His commitment to education extends to developing comprehensive course materials and providing guidance to graduate students pursuing research in quantum programming languages. Dr. Fu maintains an active presence in the quantum computing research community, regularly presenting at major conferences including the International Conference on Quantum Physics and Logic (QPL), ACM SIGPLAN Symposium on Principles of Programming Languages (POPL), and specialized quantum computing workshops. His work contributes to the growing ecosystem of quantum programming tools and methodologies that will be essential for practical quantum computing applications.
