Mahdi Fahmideh is an Associate Professor in Cyber Security at the School of Business, University of Southern Queensland (UniSQ), Australia. He holds a PhD in Information Systems from the University of New South Wales (UNSW) and MSc/BSc in Software Engineering from Azad University, Iran. His research focuses on Cyber Security, AI Ethics, Blockchain, and IoT, with a track record of securing ARC grants and international visiting fellowships. He has published in top-tier journals like European Journal of Information Systems (EJIS), IEEE Transactions on Software Engineering (TSE), and ACM Computing Surveys (CSUR). His career includes roles as Senior Lecturer at UniSQ, Lecturer at the University of Wollongong, and Postdoctoral Researcher at UTS. Awards include a Best Paper Award (2018) and multiple research excellence recognitions. He teaches courses in Cyber Security, Data Mining, and Information Assurance. His grants include ARC Linkage projects on Adversarial Machine Learning (AUS $445K) and Blockchain in Visual Arts (AUS $570K). Fahmideh’s recent work explores AI-Human collaboration (e.g., ChatGPT in software development) and trustworthy AI frameworks. He leads course development in Cyber Security and mentors students in HDR programs. His industry experience includes 8 years as an analyst programmer in software systems for publishing, insurance, and government sectors.
Le Guan is an Associate Professor at the School of Computing, University of Georgia. His research focuses on embedded systems security, IoT security, and software engineering. He holds a PhD from the Chinese Academy of Sciences and a BEng from the University of Science and Technology of China. His work includes groundbreaking contributions to firmware security, hardware-assisted defense mechanisms, and vulnerability detection in cyber-physical systems. Education: PhD in Information Technology, Chinese Academy of Sciences (2015) BEng in Computer Science, University of Science and Technology of China (2009) Research Interests: Embedded Systems Security Firmware Integrity and Analysis Cyber-Physical System Defense Hardware-Software Co-Security Automated Vulnerability Detection Recent Research Trends: Recent articles highlight innovations in firmware emulation, hybrid fuzzing for IoT devices, and hardware-assisted security mechanisms for microcontrollers. His work frequently bridges theoretical frameworks with practical implementations for real-world embedded systems. Awards: NSF Career Award (2023) Student Career Success Influencer Award (2022) Grants and Collaborations: Co-PI on the 2023 Presidential Interdisciplinary Seed Grant on Deep Fakes. His NSF Career Award supports research into data protection in embedded systems. Collaborates widely with industry and academia on firmware security and automotive systems.
Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.
John S. Galliano serves as an Adjunct Lecturer at the University of Maryland Global Campus, focusing on cybersecurity education. He has over a decade of experience teaching undergraduate cybersecurity courses, including stints at Western Governors University and Columbia Southern University. His professional roles include Cybersecurity Engineer and Digital Forensics Analyst at the Department of Defense (DoD). Certifications: Over 30 certifications including CNSP (2023), CCI (2022), eJPT (2022), DCITA Defense Digital Forensic Examiner (2022), GIAC certifications (GRID/GAWN 2018), Splunk Certified Core User (2018), and CISSP (2010). Research interests span cyber operations, insider threat mitigation, incident response, Linux systems, ethical hacking, and the sociotechnical dimensions of cybersecurity including data privacy and human rights in technology.
Thomas Neubauer is a PostDoc Researcher at the Department of Information Systems Engineering, Technische Universität Wien. His research focuses on Smart Farming, Explainable AI, and Digital Agriculture with applications in precision livestock farming and sustainable agricultural systems. He leads projects on Precision Livestock Farming (PLFDoc), Legume-cereal intercropping, and Agricultural Photovoltaics integration (PlusIQ). Key projects include: Austrian Competence Centre for Feed and Food Quality, Safety & Innovation (2025–2028) Austrian Science Fund (FWF)-funded work on legume-cereal intercropping (2023–2027) EU-funded ICT4DecisionMaking in Farming (2017–2021) His research spans Digital Twin development for agriculture, machine learning in crop rotation planning, and data security solutions for e-Health. He has supervised over a dozen PhD and Master’s students since 2007. Notable publications include work on reinforcement learning for crop optimization and digital twin frameworks in smart farming.
Kristan Stoddart is an Associate Professor of Cyber Threats at Swansea University's School of Social Sciences, with additional affiliation at the Hillary Rodham Clinton School of Law. He serves as a member of the Cyber Threats Research Centre (CYTREC) and has previously held positions as a Reader in International Politics at Aberystwyth University, where he also served as Deputy Director of the Centre for Intelligence and International Security Studies. His research spans cybersecurity, cyberwarfare, cyberespionage, cyber terrorism, cybercrime, international security, intelligence studies, and nuclear weapons. Stoddart's work bridges historical analysis of Cold War nuclear strategy with contemporary cyber threats, creating a unique interdisciplinary approach to security studies. His expertise is particularly focused on the intersection of cyber operations with critical national infrastructure protection and geopolitical conflicts. Stoddart's publications reveal a clear evolution from historical nuclear strategy research to contemporary cyber security challenges. His recent works focus on Russian cyber campaigns against Ukraine, Chinese offensive cyberespionage, and threats to critical infrastructure. This progression demonstrates his ability to apply historical understanding of strategic competition to emerging cyber domains, with particular emphasis on the 'gray zone' between peace and war where cyber operations increasingly occur. Professional Recognition: Fellow of the Higher Education Academy Fellow of the Royal Historical Society Fellow of the Royal Society of Arts (FRSA) since 2022 Research Leadership: Stoddart has secured significant research funding including a €30,000 EU grant for 'EU Resilience Against Hybrid Warfare' (2021) and served as Principal Investigator for a £1.2 million project on 'SCADA Systems and Cyber Security Lifecycles' (2014-2017) funded by Airbus Group and the Welsh Government. He has supervised multiple PhD students working on topics including social media evidence, voluntary sector regulation, and crypto-crimes. Professional Engagement: Stoddart is an active member of the Project on Nuclear Issues at the Center for Strategic and International Studies in Washington DC. He has spoken at major international conferences including NATO, GCHQ, and US Strategic Command, and has provided expert commentary for media outlets including the BBC. His public engagement includes keynote lectures such as 'Edward Snowden and You: Negotiating the Post 9/11 Surveillance State' broadcast on C-Span.
Dr. Su Nguyen is a Senior Lecturer in AI and Analytics at RMIT University's Department of Accounting, Information Systems & Supply Chain, located at the City Campus in Australia. His research focuses on integrating artificial intelligence, analytics, and operations research to address challenges in sustainability, safety, and critical domains like healthcare and logistics. Key areas include transparent AI systems, simulation models for dynamic environments, and optimization algorithms for energy and transportation. His work emphasizes making AI systems accountable to mitigate risks such as discrimination and ensures fairness in autonomous decision-making. Collaborations span Australia, New Zealand, and the Asia Pacific region, promoting AI applications in industry. Supervised projects include topics like personalized care plans, workforce rostering optimization, and AI-driven solutions for supply chains and cyber-risk evaluation. Dr. Nguyen's research bridges theoretical advancements with practical industry engagement, aiming to enhance decision-making through advanced analytics and business optimization frameworks.
Professor Zhiyong Chen is a faculty member at the University of Newcastle, affiliated with the School of Engineering and the Electrical and Computer Engineering Department. He holds the rank of Professor and specializes in control systems, robotics, and nonlinear dynamics. His research focuses on biological control systems, swarm intelligence, and adaptive control strategies for complex systems. Chen has authored influential textbooks such as Stabilization and Regulation of Nonlinear Systems: A Robust and Adaptive Approach (2015), which provides foundational knowledge in nonlinear control theory. Education: PhD from the Chinese University of Hong Kong. Research interests include multi-agent systems, fault-tolerant control, and reinforcement learning applications. His work emphasizes practical implementations in robotics, transportation systems, and aerospace engineering. He has contributed to over 212 journal articles and 119 conference papers, addressing challenges in distributed control, cybersecurity in cyber-physical systems, and autonomous synchronization. Chen's recent projects involve cooperative control of high-speed trains, resilient multi-agent systems under cyber attacks, and physics-informed reinforcement learning. His methodologies often integrate theoretical rigor with real-world applications, such as nanopositioning systems and fault diagnosis in mechanical systems. Grants and collaborations highlight his role in interdisciplinary research, combining control theory with machine learning and cryptography for secure networked systems. His lab focuses on advancing adaptive control techniques and their deployment in safety-critical environments.
Michael Devetsikiotis is a Professor and Chair of the Department of Electrical and Computer Engineering at the University of New Mexico (UNM), part of the School of Engineering. He holds a Ph.D. from North Carolina State University (1993). His career includes roles as an Assistant/Associate Professor at Carleton University (1996–1998), Associate/Professor at North Carolina State (2000–2016), and leadership in UNM's ECE Department since 2016. He specializes in telecommunication networks, smart grids, IoT, and quantum information science. Education: Ph.D. in Electrical Engineering, North Carolina State University, 1993 M.S. in Electrical Engineering, North Carolina State University, 1990 Dipl. Ing. in Electrical Engineering, Aristotle University of Thessaloniki, Greece, 1988 Research Interests: Focuses on network design, smart grid communications, cyber-physical systems, and quantum technologies. He has published over 180 refereed papers and secured funding from NSF, NSERC, Cisco, and IBM. Notable projects include leading UNM’s Quantum Information Science program and managing the $20M NSF EPSCoR “SMART” Grid initiative. Articles Trends: Recent work emphasizes AI-driven network management (e.g., LSTM models for 5G/6G), blockchain for secure IoT/satellite systems, and quantum computing. Earlier contributions addressed EV charging infrastructure and smart grid resilience. Scientific Awards: IEEE Fellow (2012) NC State ECE Alumni Hall of Fame (2017) Advising & Grants: Directed UNM’s NSF Quantum Computing Faculty Fellowship (2020), enabling hires in quantum engineering. Previously managed a 800-student ECE graduate program at NC State. Active in IEEE leadership roles, including Distinguished Lecturer (2008–2011) and Chair of flagship conference committees. Labs & Teams: Spearheaded UNM’s IBM Q-Hub affiliation (2020), advancing quantum research collaboration. Leads interdisciplinary teams for smart city defense, blockchain energy markets, and 6G network automation.
João Pedro Hespanha is a Distinguished Professor holding dual appointments in the Electrical and Computer Engineering and Mechanical Engineering departments at the University of California, Santa Barbara. He is affiliated with the Center for Control, Dynamical-Systems and Computation (CCDC) and the Institute for Collaborative Biotechnologies, where he leads research at the intersection of control theory, networked systems, and biological applications. Dr. Hespanha has established himself as a leading authority in hybrid systems and networked control with significant theoretical contributions and practical implementations. Dr. Hespanha received his Licenciatura and MS in Electrical and Computer Engineering from Instituto Superior Técnico in Lisbon, Portugal, before earning his PhD in Electrical Engineering and Applied Science from Yale University in 1998. After serving as an Assistant Professor at the University of Southern California from 1999-2001, he joined UC Santa Barbara in 2002 where he has remained ever since, rising to his current distinguished position. His educational background reflects a strong foundation in both theoretical mathematics and practical engineering applications. His research program spans multiple interconnected domains including hybrid and switched systems, networked control systems, cooperative control of autonomous agents, and systems biology. Dr. Hespanha's work on hybrid systems has fundamentally advanced the mathematical frameworks for modeling systems that combine continuous dynamics with discrete logic transitions. His research on networked control systems addresses critical challenges in communication-constrained environments, while his work in cooperative control tackles computational complexity and limited communication in multi-agent systems. His systems biology research applies control theory to model gene regulatory networks using stochastic hybrid systems. Dr. Hespanha's recent publications demonstrate consistent innovation across theoretical foundations and practical applications. His work shows a clear trajectory toward more complex networked systems, with increasing emphasis on security, resilience, and uncertainty quantification. The publications reveal strong interdisciplinary connections between control theory, computer science, and biology, with applications spanning autonomous vehicles, communication networks, and biological processes. Among his numerous accolades: Elevated to IEEE Fellow in 2008 for contributions to stability techniques for switched and hybrid systems Awarded the prestigious Ruberti Young Researcher Prize in 2009 Received the George S. Axelby Outstanding Paper Award in 2006 Honored with the Automatica Theory/Methodology best paper prize in 2005 Named IFAC Fellow in 2016 Received ACM SIGBED HSCC Best Paper Award in 2019 Dr. Hespanha has successfully mentored over 25 PhD students who have gone on to prominent positions in academia and industry. His research has been consistently supported by substantial funding from NSF, NIH, ONR, and other agencies, with current projects including pandemic management decision systems, precision drug delivery, and control of autonomous vehicle networks. He has taught numerous influential courses including Linear Systems Theory and Noncooperative Game Theory, authoring widely used lecture notes published by Princeton Press. Dr. Hespanha leads an active research group within the Center for Control, Dynamical-Systems and Computation, collaborating with researchers across engineering disciplines and biology. His lab maintains strong connections with industry partners working on autonomous systems, communication networks, and biological applications. He has organized major conferences including serving as General Chair for the 9th International Workshop on Hybrid Systems: Computation and Control in 2006, further establishing UCSB as a leading center for control systems research.
Michael Rosulek is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, focusing on cryptography and secure computation. He received his Ph.D. in Computer Science from the University of Illinois and a B.S. in Computer Science from Iowa State University. Professor, Oregon State University (2025-present) Associate Professor, Oregon State University (2013-2025) Assistant Professor, University of Montana Research Interests: Dr. Rosulek's research focuses on cryptographic protocols for secure computation, allowing parties to perform computations on private data without revealing the data itself. His work spans both theoretical foundations and practical implementations in areas like: Private set intersection protocols Garbled circuits optimization Secure computation against malicious adversaries Practical applications of cryptography Efficient oblivious transfer techniques His recent publications demonstrate a consistent focus on improving efficiency and security in cryptographic protocols, with particular attention to making these techniques more practical for real-world applications. Scientific Awards: 2022 College of Engineering Graduate Mentoring Award 2019 College of Engineering Engelbrecht Young Faculty Award 2012 NSF CAREER Award Research awards from Google, Facebook and Visa Research Advising: Dr. Rosulek has advised numerous Ph.D. and M.S. students, many of whom have continued to academic positions or research labs at institutions like UC Berkeley, MIT Lincoln Labs, and Aarhus University.
Stephen S. Yau is a Professor at Arizona State University's School of Computing and Augmented Intelligence, where he has been since 1994. Previously, he held roles including Professor and Chair of the Department of Computer and Information Sciences at the University of Florida (1988–1994) and Walter P. Murphy Professor at Northwestern University (1961–1994). He earned a Ph.D. in Electrical Engineering from the University of Illinois in 1961. His research focuses on Cyber Trust, Cloud Computing, Software Engineering, and Service-based Systems. Over 210 journal/conference papers and grants from NSF, AFRL, and industry partners like Hitachi and Fujitsu support his work. Notable awards include the IEEE Computer Society's Special Award (2006), Overseas Outstanding Contributions Award (2006), and Fellowships from IEEE and AAAS. He has contributed to editorial boards of IEEE Transactions and led conferences like COMPSAC and IEEE International Conference on Services Computing. Teaching includes courses on Information Assurance & Security, with active involvement in student advising and graduate supervision. His service roles span leadership in Computing Research Association, IEEE, and AFIPS.
Hong-Mei Chen is a Professor in the Department of Information Technology Management at the Shidler College of Business, University of Hawaii at Manoa. Her research bridges business strategy and information systems through architecture-centric approaches, focusing on transformative technologies like Big Data and blockchain. She maintains an active teaching load across undergraduate, master's, and doctoral programs while directing significant research initiatives. Her academic credentials include: PhD in Business Administration, University of Arizona MS in Management Information Systems, University of Arizona BS in Business Administration, National Taiwan University Professor Chen's research centers on Big Data System Design Methodology and Software Architecture , with pioneering work on the Neo-Metropolis Model for Big Data as a Service. She investigates technical debt reduction, DevOps integration, and cybersecurity applications while extending these frameworks to service systems, social CRM, and green information systems. Her approach combines empirical studies with practical industry implementations, particularly in aviation and energy sectors, to develop value engineering frameworks for business model innovation. Analysis of her 2015-2017 publications reveals three dominant trends: (1) Architecture-centric Big Data solutions for business innovation, (2) Proactive cybersecurity frameworks using predictive analytics, and (3) Service-oriented transformations through Service Dominant Logic. Her work consistently addresses the tension between agile development and architectural rigor in ultra-large-scale systems. She has received significant recognition including: Presidential Citation for Meritorious Teaching Award Dennis Ching Outstanding Teaching Excellence Award Fulbright Senior Specialist in IT Award CISE Global Information Infrastructure Healthcare award Professor Chen has secured over $6.5 million in competitive funding including a $1.1M NSF grant on software economics, $5M DARPA telemedicine projects, and $450K electrical vehicle data visualization research. She obtained substantial instructional hardware/software grants exceeding $2M from Oracle, Sun, Apple, and KPMG to establish the AIMS Lab. Her teaching development grants from IBM and CIBER have modernized ITM curricula with real-world data engineering components. She founded and directs the Advanced Information Management Solutions (AIMS) Lab, which serves as an industry-academic nexus for Big Data research. The lab partners with major technology providers to develop Neo-Metropolis architecture implementations while training students in DevOps and cybersecurity analytics through hands-on projects with aviation and healthcare organizations.
Dr. Zakwan Jaroucheh is a Lecturer at the School of Computing Engineering and the Built Environment , Edinburgh Napier University (ENU). With a PhD in Context-Aware Pervasive Computing from ENU (2012), he combines academic expertise with over seven years of industry experience as a Principal Security Engineer at Dell SecureWorks. Research focuses: Blockchain Technologies , Cybersecurity , Internet of Things , and Decentralized Systems Supervises PhD students in areas like DeFi compositions, blockchain authentication, and secure microservices Secured £485,000 in grants from The Data Lab, Scottish Enterprise, and CyberASAP Developed LastingAsset - a blockchain-based solution for combating phone call fraud His publications in IEEE , Personal and Ubiquitous Computing , and Springer address key challenges in decentralized authentication , privacy-preserving mechanisms , and blockchain security . Current work explores blockchain's transformative potential in value exchange and information sharing.
Bill Buchanan is a Professor at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, and a member of the Centre for Algorithms, Visualisation and Evolving Systems. His work spans Information Visualization , Software Systems , Cybersecurity , and AI , focusing on innovative solutions for complex data challenges. Research Interests: Information visualization for hierarchical data, cybersecurity frameworks, machine learning in industrial IoT, and post-quantum cryptographic systems. Projects: Supervised PhD projects on visualizing overlapping classification hierarchies and biological network inference. Currently leads research in AI-driven human-robot interaction and secure Dockerized architectures. Publications: 15+ recent works on quantum agents, GANs for anomaly detection, homomorphic encryption, and advanced cryptographic techniques. Collaborations: Engages with biologists, AI researchers, and cybersecurity experts across institutions.
