Michael Soltys is an Adjunct Professor in the Department of Computing and Software at McMaster University. His work bridges theoretical computer science, algorithms, and practical applications in cybersecurity and education. Research interests include logic, circuit complexity, and pairwise comparisons. Publications span 2002–2021, with recent focus on cloud computing education, digital forensics, and graph theory. Active in academic service through editorial contributions (e.g., Foreword for Franco-Canadian workshop proceedings). His scholarly activity maps to subdisciplines such as Computer Systems Theory, Logic, Discrete Applied Mathematics, and Operations Research. Dr. Soltys' work on pairwise comparisons and clique covers provides foundational insights for decision systems and graph optimization. He has also contributed to cloud curriculum development and malware analysis frameworks. As an educator, he co-authored An Introduction to the Analysis of Algorithms (2018, 2012, 2009), a textbook exploring algorithmic foundations. Current affiliations include the McMaster Experts database, with collaborations across theoretical computer science and applied cybersecurity research.
Andrey Chechulin is a Professor at the St. Petersburg Federal Research Center of the Russian Academy of Sciences, Institute of Informatics Problems, where he leads research in cybersecurity within the Information Security Department. His work spans over 15 years with more than 80 publications in top-tier security conferences and journals. Dr. Chechulin's primary research interests include cybersecurity, cyber-physical systems security, social network analysis, and bot detection. His work focuses on developing practical methodologies for incident investigation, access control, and vulnerability assessment. He has pioneered approaches for analyzing social media bots, particularly on the VKontakte platform, and developed innovative frameworks for cryptocurrency transaction anomaly detection using neural networks. His publication record shows consistent contributions to major security venues including PDP, COMSNETS, and Sensors, with a notable increase in output since 2018. Recent work demonstrates his adaptation to emerging security challenges in AI-generated content detection and blockchain security. Best Paper Award at PDP 2021 Cybersecurity Research Excellence Award (2019) Dr. Chechulin maintains strong collaborative relationships with Igor V. Kotenko (60 joint publications), Dmitry Levshun, and Maxim Kolomeets. His research often bridges theoretical security concepts with practical implementations for real-world systems, including smart city infrastructure and automotive security applications.
Dr. Timothy Cribbin is a Senior Lecturer in the Department of Computer Science within the College of Engineering, Design and Physical Sciences at Brunel University London. He has been with the university since 2001, initially joining as a lecturer and advancing to his current position. His academic home is firmly rooted in the intersection of information science, human-computer interaction, and data analytics. His educational background includes: PGCert Learning and Teaching in Higher Education, Brunel University (2007) PhD Information Science, Brunel University (2005) for research exploring spatial-semantic interfaces for exploratory document search MSc Industrial Psychology, University of Hull (1996), where he was awarded the Tom Hoyes Memorial Prize BSc (Hons) Psychology, University of Portsmouth (1994) Dr. Cribbin's research focuses on information visualization, interactive search interfaces, and text analytics, with particular expertise in processing and modeling large text collections to uncover meaningful insights. His work spans the design and evaluation of algorithms, interaction models, and end-user tools that support search, navigation, exploration, and sense-making within connected information spaces like scholarly publications and social media platforms. Early in his career, he pioneered work on interactive visualization using distance-similarity and spatial-semantic metaphors, making key contributions through the application of geodesic distance and second-order similarity transformations. More recently, his research has centered on citation-enhanced information retrieval and social media analytics, including the development of the Chorus Twitter analytics project. His scholarly output reveals a consistent trajectory from foundational work in information visualization to increasingly applied research in social media analytics and text mining. Throughout his career, Dr. Cribbin has maintained a strong focus on human-centered approaches to information processing, with particular attention to how users interact with and make sense of complex information spaces. His recent work demonstrates growing interest in psychological aspects of information processing, author classification, and the analysis of linguistic patterns in online radicalization. Dr. Cribbin has received notable recognition including: Tom Hoyes Memorial Prize for his MSc in Industrial Psychology Fellowship of the Higher Education Academy (FHEA) He has secured research funding for projects including "Predicting online radicalisation" and "Facilitating social media research in social sciences." Dr. Cribbin serves as a Deputy Senior Tutor (Academic Misconduct) and provides supervisory duties for final year undergraduate and Masters dissertation projects. He regularly acts as a reviewer for conferences and journals in information science, social media analytics, and information visualization. Dr. Cribbin is a key contributor to the User Centred Design research group and is the founder and lead programmer of the Chorus Twitter analytics project. His work bridges theoretical research with practical applications, particularly in the areas of social media analytics and text mining.
Gustavo de Almeida is a Researcher at the Department of Energy Technology , School of Energy Systems , Lappeenranta-Lahti University of Technology (LUT). He holds a verified email at LUT and has over 15 years of experience as a professor in Brazil before joining LUT in 2022. Education: M.Sc. in Chemical Engineering, Federal University of Minas Gerais (2003) Ph.D. in Chemical Engineering, University of São Paulo (2006) His research focuses on data analysis applications in the process industry and AI in digital learning , with specific interests in Industrial AI, Process Monitoring, Model Interpretability, and Data-Centric AI. Recent work includes online course development and applications of machine learning in chemical engineering processes. Key publication trends: Spanning 2025 to 2010, his 15 most recent articles emphasize Industrial AI, fault detection, process optimization, and bioenergy technologies. Topics include explainable AI for energy systems, stochastic boiler optimization, and SO2 emission monitoring in industrial processes.
Pedro Cabalar is Full Professor at the Department of Computer Science of the University of Corunna, Galicia, Spain, and current coordinator of the inter-university Master in Artificial Intelligence (Universities of A Coruña, Santiago de Compostela and Vigo). He also serves as Area Editor (Theory Foundations) for Theory and Practice of Logic Programming and as Standard Editor for the Artificial Intelligence journal. Education: PhD in Computer Science, University of Corunna, 2001 Master in Computer Science, Politéchnic University of Madrid, 1993 Bachelor in Computer Science (3-year degree), University of Santiago de Compostela / University of Corunna, 1989 Research interests revolve around Knowledge Representation & Reasoning , especially Answer Set Programming , non-monotonic reasoning , temporal and modal logics , and causal reasoning . He investigates theoretical foundations (equilibrium logic, temporal extensions, deontic operators) and practical systems (telingo, eclingo, aspBEEF), with applications ranging from planning and diagnosis to explainable AI and healthcare decision support. His recent articles (2023-2025) exhibit a clear trend toward temporal and metric extensions of ASP , explainability , and hybrid reasoning systems , often combining logic programming with deontic or probabilistic features. Scientific awards & recognition: University of Corunna Dissertation Award, 2003 Best Paper Award at LPNMR 2019 Best Student Paper at ICLP 2020 Best Student Paper at JELIA 2019 Grants & projects: He currently leads or co-leads nationally funded Spanish projects (GEISER 2024-2028, ARLEKIN 2021-2024) and has coordinated EU COST actions (DigForASP) as well as earlier MINECO projects on temporal ASP and medical reasoning (TARDIS, MERLOT, FEAST, etc.). PhD supervision: He has successfully supervised three PhD theses (Martín Diéguez, Jorge Fandiño, Brais Muñiz) and continues to advise students within the Information Retrieval Laboratory (IRLab) and the Spanish node of Potassco Solutions.
Jerry Lacmou Zeutouo is a Lecturer in the field of Networks and Data at Université de Picardie Jules Verne (UPJV), contributing to computer science research through advanced algorithm development and optimization. His work primarily focuses on parallel computing, dynamic programming, and computational biology. Academic Rank: Lecturer at UPJV Institution: Université de Picardie Jules Verne Research Interests: Dr. Zeutouo specializes in designing and optimizing parallel algorithms for dynamic programming and database security. His research spans: Coarse-Grained Multicomputer (CGM) architectures K-anonymity in distributed databases RNA folding and bioinformatics applications Longest Common Subsequence (LCS) constraints Publication Trends: His work emphasizes solving computational bottlenecks via parallelism, particularly in cloud environments, bioinformatics, and database security. Key themes include: Transformer-based models for cold start mitigation in FaaS Multicomputer algorithms for dynamic programming Four-splitting and Four-Russians techniques for optimization
Zhao Xin is a dedicated researcher at the Language Technology Group (LLMC) within National Institute of Informatics, focusing on Natural Language Processing and Knowledge Representation. His academic journey includes a Doctoral Courser at The University of Tokyo, a Master Courser at Nara Institute of Science and Technology, and a Bachelor’s in Japanese at Xi'an Jiaotong University. Education: Doctoral Courser (2023–2026), Information Science and Technology, The University of Tokyo Master Courser (2018–2020), Computer Science, Nara Institute of Science and Technology Bachelor Courser (2013–2017), Foreign Languages and Literatures, Xi'an Jiaotong University His research interests span Natural Language Processing , Domain Adaptation , Cross-lingual Transfer , Model Interpretability , and Semantic Search . Recent projects include analyzing neuron-level controllability in language models and cross-lingual knowledge transfer for Japanese NLP tasks. Zhao’s publications (2024–2025) emphasize fact knowledge evaluation , neuron activation analysis , and cross-lingual entity alignment . He received the Young Investigator Award for his work on multilingual knowledge tracing. Proficient in Japanese and English , Zhao contributes to tools like llm-jp-eval and Megatron-LM , with expertise in LLM pre-training , domain adaptation , and Python/Machine Learning frameworks.
Carlos Zednik is an Assistant Professor for Philosophy of Artificial Intelligence at Eindhoven University of Technology , affiliated with the Industrial Engineering and Innovation Sciences department. He leads the Eindhoven Center for Philosophy of AI and participates in the alignAI (ERC) and ROBUST AI (NWO) consortia. His work bridges philosophy with AI and neuroscience, focusing on explainable AI (XAI), mechanistic explanation, and cognitive modeling. Education: BSc in Computer Science and Philosophy, Cornell University MSc in Philosophy of Mind, University of Warwick PhD in Cognitive Science, Indiana University Bloomington Zednik’s research investigates philosophical questions about biological and artificial intelligence, emphasizing: Methodological principles in cognitive psychology and neuroscience Norms and best practices for XAI in machine learning Knowledge representation in transformer models and large neural networks His recent publications explore the integration of cognitive models into XAI, the role of Bayesian reverse-engineering in cognitive science, and the mechanistic explanation of network neuroscience. Zednik also contributes to international standardization efforts through ISO/IEC TS 6254 and DIN SPEC 92001 . Scientific Awards include fellowships from: DAAD Alexander-von-Humboldt Foundation StandICT Fellowship Zednik supervises PhD students Zeynep Kabadere , Michela Ghezzi , Céline Budding , Miriam Gorr , and Hannes Boelsen , while mentoring postdocs Manuel Barbosa de Oliveira and Philippe Verreault-Julien . His teaching spans philosophy of AI, ethics of machine learning, and decision theory, with innovation projects on generative AI in higher education.
Bo Hui is an Assistant Professor of Computer Science at The University of Tulsa's College of Engineering & Computer Science. He received his Ph.D. in Computer Science and Software Engineering from Auburn University in 2023 and earned his B.S. in Computer Science from Xi'an Jiaotong University in 2013. Prior to his Ph.D., he worked as a senior software engineer in the industry. Education: Ph.D., Computer Science and Technology, Auburn University, 2023 B.S., Computer Science and Technology, Xi'an Jiaotong University, 2013 Dr. Hui's research focuses on data mining and machine learning, particularly in designing machine learning methods for complex real-world data while addressing social concerns such as privacy in AI. His work spans multiple application domains including traffic prediction, social recommendation systems, and biological data analysis. He has made significant contributions to graph neural networks, knowledge graph unlearning, and federated learning systems. Dr. Hui's publication record demonstrates a strong focus on machine unlearning (the 'right to be forgotten' in AI models), graph neural networks, and traffic prediction systems. His work bridges theoretical machine learning with practical applications, with a growing emphasis on privacy-preserving AI techniques. He frequently publishes in top-tier venues including ICCV, ICLR, KDD, and AAAI. Awards and Honors: NSF award #2348177 for machine unlearning research AAAI student scholarship award Dr. Hui currently advises two Ph.D. students (Ruimeng Ye and Yang Xiao) who began their studies in Fall 2024. His research is supported by an NSF grant (#2348177) focused on machine unlearning. He has served as a reviewer for major conferences including ICLR, AAAI, KDD, CVPR, ACL, and EMNLP. His teaching includes Data Mining (2024 Spring) and Interaction Design (2023 Fall). His research group appears to focus on data mining and machine learning with applications to real-world problems requiring privacy-aware solutions.
Luke Mathieson is a Senior Lecturer and Deputy Head of School (Teaching and Learning) in the School of Computer Science at the University of Technology Sydney. His academic career spans theoretical computer science with a focus on computational complexity and its applications. Dr. Mathieson's educational background includes a PhD in Theoretical Computer Science from Durham University, a Masters and Postgraduate Diploma in Higher Education from Macquarie University, and dual Bachelor's degrees in Computer Science (Honors) and Science (Chemistry) from the University of Newcastle Australia. His research interests are centered on parameterized complexity and its applications, extending to various areas of complexity theory, algorithmics, quantum computing, graph theory, and related mathematics. A major theme of his research is the complexity of graph editing problems, a topic in which he specializes. His recent work bridges theoretical complexity with practical applications in AI education, network science, and quantum computing. Dr. Mathieson has taught an extensive range of computer science subjects, particularly focusing on the theory of computation, computational complexity, and algorithmics. At UTS, he teaches or has taught subjects including Data Structures and Algorithms, Applications Programming, Computing Science Studio, Theory of Computing Science, Programming, and Advanced Algorithms. He serves as the Course Director for the Bachelor of Science in Information Technology suite of degree programs and the Course Coordinator for the IT Core. Senior Lecturer, University of Technology Sydney, School of Computer Science (2022-present) Lecturer, University of Technology Sydney, School of Computer Science (2021-2022) Scholarly Teaching Fellow, University of Technology Sydney, School of Computer Science (2017-2021) Research Associate, University of Newcastle Australia, Centre for Information Based Medicine (2014-2017) Adjunct Lecturer, Macquarie University, Department of Computer Science (2014) Postdoctoral Fellow, Macquarie University, Department of Computer Science (2011-2013) Research Associate, University of Newcastle Australia, School of Electrical Engineering and Computer Science (2010-2011) His research demonstrates consistent productivity across theoretical computer science with notable contributions to parameterized complexity and network controllability. Recent publications show an expanding scope incorporating quantum computing applications and educational technology innovations. The QB-suite: a framework for quantum algorithm design and benchmarking (2024-2027) National Industry PhD Program: Improving biosecurity through livestock history recording (2024-2028) Random Number Generation and Analytics for Client Understanding (2018-2019) He maintains active research collaborations across multiple institutions and is affiliated with the Faculty Centre for Quantum Software and Information (QSI) at UTS, reflecting his growing involvement in quantum computing research.
Leon Moonen is a Research Professor and Head of the Data-Driven Software Engineering Department at Simula Research Laboratory in Oslo, Norway. He also holds a visiting professor position at the Department of Data Science and Analytics at BI Norwegian Business School. His research focuses on developing advanced data-driven techniques and tools to help software engineers create more secure, trustworthy, and resilient systems. His work combines software analysis, machine learning and AI, software reverse engineering, software repository mining, program comprehension, and empirical software engineering. Moonen's research addresses three main areas: (1) cybersecurity, particularly automated assessment and repair of software security vulnerabilities, as well as automated support for cyber threat intelligence; (2) autonomous self-healing systems, investigating how bio-inspired approaches can build more resilient systems; and (3) intelligent analytics to leverage data from software development, evolution, and operation to support decision-making. His recent publications demonstrate a strong focus on applying large language models to software engineering challenges, including automated programming, program repair, log analysis, and vulnerability detection. The research shows a progression from traditional software engineering approaches toward increasingly sophisticated AI-driven techniques. Professor Moonen has led projects supporting smarter evolution and testing of safety-critical cyber-physical product families, high-integrity software engineering, and software analytics for continuous quality assessment. He collaborates closely with industrial partners including Kongsberg Maritime and Cisco Norway. Before joining Simula, Moonen worked at Delft University of Technology and the Centre for Mathematics and Computer Science (CWI) in Amsterdam. He is also a co-founder of the Software Improvement Group (SIG), which has grown from 6 to over 150 employees since 2000.
Prof. Dr. Martin Matzner is a Professor at Friedrich-Alexander University Erlangen-Nürnberg, holding the Chair of Digital Industrial Service Systems. He studied Business Informatics at the University of Münster and Turku School of Economics, earned his doctorate in 2012 for work on service networks, and received a teaching license in Business Informatics in 2016. His research focuses on IT-supported services, business process management, and design-oriented business informatics research, with significant contributions to digital transformation and smart service systems. Current affiliation: Friedrich-Alexander University Erlangen-Nürnberg Previous roles: University of Münster (2007-2017) Key research areas: Business Process Management, Process Mining, Smart Service Systems, Predictive Analytics His recent publications emphasize predictive process monitoring using machine learning, transfer learning for cross-domain applications, explainable AI in business processes, and platform ecosystem governance . He has pioneered methods for adaptive AI control in manufacturing and context-aware process analytics , with applications spanning logistics, healthcare (ICU admission prediction), and human resources. His work bridges technical process mining with sociotechnical perspectives , particularly in algorithm adoption and ethical implications. Prof. Matzner's research has produced 15+ recent publications (2025-2024) in journals like International Journal of Production Research , Computers in Industry , and conferences including ECIS and ICIS. Topics demonstrate a progression from process efficiency optimization to human-AI collaboration and regulated AI risk assessment . His methodological toolkit spans LSTM networks , graph-based neural models , and LRP explanation techniques .
Professor Shigeru Fujimura is affiliated with Waseda University as a faculty member of the Faculty of Science and Engineering , specifically in the Graduate School of Information, Production, and Systems . With a PhD in Engineering from Waseda University, his research focuses on intelligent informatics and system engineering , particularly in scheduling, optimization, and human-robot collaboration. His research spans multiple domains including: Deep Reinforcement Learning for combinatorial optimization Energy-efficient manufacturing systems Multi-agent collaboration frameworks Augmented Reality interfaces IoT business modeling Recent publications demonstrate expertise in graph neural networks , particle swarm optimization , and generative adversarial networks applied to industrial problems. He has received multiple awards including the Invention Encouragement Prize (2003) and IEEJ Paper Presentation Award (1994) .
Abhinav Bhatele is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, and directs the Parallel Software and Systems Group. He holds affiliate roles in the AIM Institute and AMSC Program. His research focuses on high-performance computing, systems, networking, and AI, particularly in parallel computing, distributed AI, and machine learning applications. He has contributed to frameworks like AxoNN and Hatchet, and his work spans performance modeling, network simulation, and scalable deep learning. Education: Ph.D. (2010), M.S. (2007), and B.Tech. (2005) in Computer Science from the University of Illinois at Urbana-Champaign and IIT Kanpur, respectively. Prior to UMD, he was at Lawrence Livermore National Laboratory (2011–2019). Research interests include parallel systems, network design, performance analysis tools, and applying machine learning to optimize HPC workflows. He has received notable awards, including the IEEE TCSC Award (2023), NSF CAREER (2021), and UIUC Early Career Alumni Award (2024). Key achievements include developing open-source tools like Hatchet and AxoNN, optimizing large-scale LLM training, and advancing GPU-based supercomputing. He advises over 10 students and has secured grants totaling millions, focusing on HPC software ecosystems and AI integration. Professional service includes roles at SC, ISC conferences, and editorial work for IEEE TPDS. His lab collaborates on projects like the Exascale Computing Project and DOE INCITE allocations.
Bahman Gharesifard is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada. He holds a Ph.D. from Queen's University (2009) and advanced degrees from Shiraz University (B.Sc., 2002; M.Sc., 2005). His research focuses on systems and control theory, with emphasis on distributed control, optimization, geometric control, and their intersections with network sciences, machine learning, and game theory. He has been recognized with the First Year Instructor Teaching Award in Engineering & Applied Science (2014 & 2016). His academic journey includes postdoctoral research at the University of California, San Diego (2009–2012) and the University of Illinois, Urbana-Champaign (2012–2013). His work bridges theoretical foundations of control systems with practical applications in distributed optimization, neural networks, and contagion models on networks. Recent research trends include advancing Lyapunov-based methods for reinforcement learning, analyzing structural controllability in sparse systems, and developing models for network dynamics using Pólya urn frameworks. His articles explore topics like averaged controllability, flexible-step MPC, and stability in distributed algorithms. Education: Ph.D., Queen's University (2009) M.Sc., Shiraz University (2005) B.Sc., Shiraz University (2002) Awards: Engineering & Applied Science First Year Instructor Teaching Award (2014) Engineering & Applied Science First Year Instructor Teaching Award (2016) He collaborates on projects involving secure distributed optimization, epidemic modeling via Pólya contagion networks, and neural network approximation guarantees. His lab contributes to theoretical control advancements with practical implications in robotics, energy systems, and AI.
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