Stjepan Groš is an Associate Professor at the Faculty of Electrical Engineering and Computing (FER) , University of Zagreb. His research focuses on cybersecurity , industrial automation , and network traffic analysis . Department of Electronics, Microelectronics, Computer and Intelligent Systems Expertise in machine learning applications for security and formal methods in SCADA systems Extensive work on anomaly detection , firewall logs , and reputation systems His recent publications examine usability of cybersecurity solutions in industrial settings, JavaScript obfuscation analysis , and synthetic log generation for security testing. Themes include network anomaly detection , endpoint security , and attack modeling . No scientific awards were explicitly mentioned in the provided text. His research also addresses real-time processor modeling and distributed intrusion detection , with practical implementations in Linux environments.
Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.
Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.
Eduard Baranov is a Visiting Lecturer and Research Assistant at the Université catholique de Louvain, affiliated with the Louvain Polytechnic School (EPL) through the Computer Engineering Center (INGI) under the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM). His work bridges academic research with industrial applications. Research Interests : Software engineering, formal methods, cybersecurity, autonomous systems, and model checking. His focus spans scalable coverage estimation, secure healthcare systems, and statistical validation of privacy protocols. Publications : Recent work includes t-wise coverage algorithms for software testing, formal verification of multi-agent autonomous systems, and privacy-preserving frameworks aligned with GDPR standards. His research often integrates symbolic execution and statistical model checking. Collaborations : Collaborates with researchers like Axel Legay, Kuldeep S. Meel, and Thomas Given-Wilson, contributing to journals such as IEEE Transactions on Software Engineering and ACM Transactions on Software Engineering and Methodology. Contact : Email: eduard.baranov@uclouvain.be | Office: INGI - Réaumur, L5.02.01, Place Sainte Barbe 2, 1348 Louvain-la-Neuve.
Sencun Zhu is an Associate Professor in the Department of Computer Science and Engineering, specializing in cybersecurity, network security, and privacy-preserving technologies. With over 183 research outputs and an h-index of 51, Zhu's work spans wireless sensor networks, Android security, federated learning, and intrusion detection systems. Key Research Areas: Cybersecurity, wireless sensor networks, adversarial machine learning, privacy-preserving analytics, and Android vulnerability detection. Projects: Led multiple NSF-funded initiatives including "Combating Worm Propagation in Emergent Networks" (2007-2013), "Obfuscation-Resilient Software Plagiarism Detection" (2013-2017), and "Reputation-Escalation-as-a-Service" (2016-2020). Recent Trends: Focus on backdoor attacks in AI models, edge computing privacy solutions, and honeypot technologies leveraging large language models. Collaborations: Active in cybersecurity and sensor networks, with partnerships across academia and industry.
Dr. Kuai Xu serves as a Professor of Computer Science at Arizona State University's School of Mathematical and Natural Sciences within the New College of Interdisciplinary Arts and Sciences. He maintains affiliations with the Center for Cybersecurity and Trusted Foundations, focusing on securing critical network infrastructures. His academic credentials include: Ph.D. in Computer Science, University of Minnesota (2006) M.S. in Computer Science, Peking University, China (2001) B.S. in Computer Science, Peking University, China (1998) Dr. Xu's research centers on network security , network measurement , and IoT systems , with significant contributions to smart home security, network behavior analysis, and social media epidemiology. His work bridges theoretical modeling and practical implementations for real-world network challenges. Recent publications (2022-2025) reveal a concentrated focus on IoT security frameworks, machine learning-driven activity inference from device traffic, and pandemic surveillance through social media analysis. Key themes include DNS security hardening, attack graph modeling, and privacy-preserving spatial analytics in smart environments. Through courses like ACO 331 (Network Forensics Analysis) and ACO 361 (Secure Coding Concepts), Dr. Xu mentors students in cybersecurity fundamentals while supervising research via ACO 399 and individualized instruction (ACO 499). His teaching integrates cutting-edge research into classroom applications. As an active affiliate of ASU's Center for Cybersecurity and Trusted Foundations, Dr. Xu contributes to interdisciplinary initiatives addressing emerging threats in home networks, IoT ecosystems, and social media platforms.
Nicola Zannone serves as Associate Professor and Chair of the Data Protection research group within the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He holds a PhD in Computer Science from the University of Trento (2007), where his dissertation focused on security requirements engineering during a research visit to the Center for ... Research Focus: His work centers on cybersecurity with emphasis on data protection, phishing defense mechanisms, and access control systems. Recent investigations explore emerging threats like quishing and LLM-generated phishing attacks, autonomous navigation security, and industrial control system vulnerabilities. His research uniquely bridges technical security solutions with human behavioral factors in organizational contexts. Publication Trends: Between 2024-2025, Zannone's output demonstrates growing attention to AI-powered threats and automated vulnerability mitigation. His systematic reviews and empirical studies consistently address practical security challenges in critical infrastructure and software supply chains, reflecting industry relevance through collaborations with security practitioners. Leadership Roles: As Chief Editor for Computer Security (Frontiers in Computer Science) and Associate Editor for Cybersecurity and Privacy (Frontiers in Big Data), he shapes discourse in security research. His editorial work on topics like "Generative AI for Cybersecurity" highlights forward-looking engagement with evolving threat landscapes.
John Grundy is a Professor of Software Engineering and Senior Deputy Dean at Monash University's Faculty of Information Technology in Melbourne, Australia. He is also an Australian Laureate Fellow (2020-2026) and leads the "Human-centric Software Engineering" (HumaniSE) research lab. With over 32 years of academic experience, Professor Grundy has held numerous leadership positions including Pro Vice-Chancellor at Deakin University and Dean roles at Swinburne University and the University of Auckland. BSc(Hons), MSc, PhD and DSc degrees in Computer Science from the University of Auckland IEEE Fellow, Fellow of Automated Software Engineering, Fellow of Engineers Australia Lero Parnas Fellow (2023) Recipient of the ACM SIGSOFT Distinguished Service Award (2023) and Dean's Award for Graduate Research Student Supervision (2024) Professor Grundy's research focuses on making "Software Engineering more like traditional Engineering disciplines" through human-centric visual modeling approaches. His primary research areas include model-driven engineering, software architecture, visual languages, software security engineering, and human factors in software development. He specifically investigates how personality, emotions, gender, age, and disability impact software usage, requirements engineering, design, and testing. His current projects include the Visual Wiki platform for knowledge engineering, Marama meta-tools, and Software Process and Product Improvement initiatives. His research has significant implications for accessibility, usability, and the alignment of software applications with diverse user needs. Professor Grundy has published extensively in top software engineering venues and has supervised numerous PhD students throughout his career. IEEE Technical Council on Software Engineering Distinguished Education Award (2014) ACM SIGSOFT Distinguished Service Award (2023) CORE Distinguished Service Award (2023) Lero Parnas Fellow (2023) Dean's Award for Graduate Research Student Supervision (2024) Professor Grundy has supervised numerous PhD students and has received funding for various research projects, most notably his 5-year Australian Laureate Fellowship (2020-2026) focused on human-centric software engineering. His HumaniSE research lab brings together interdisciplinary teams to address challenges in making software systems more responsive to human needs and contexts. His lab focuses on developing new conceptual foundations and modeling techniques that incorporate human factors throughout the software development lifecycle, with applications in smart homes, digital health, and smart city solutions.
