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Marcus HolzingerView profile

Professor

• Space Policy
• Space Situational Awareness
• Information Theory
• +4 more

Dr. Marcus Holzinger is the Joseph T. Negler Professor and Hatfield Endowed Professor in Space Policy & Law at the University of Colorado Boulder's Department of Aerospace Engineering Sciences. He leads the Colorado Center for Astrodynamics Research (CCAR) and the VADeR Laboratory, focusing on advancing space exploration through interdisciplinary research in space policy, autonomy, and space situational awareness. His work emphasizes collaboration and sustainable practices in space. Education: PhD in Aerospace Engineering Sciences, University of Colorado Boulder (2011) MS in Aeronautics & Astronautics Engineering, University of Washington (2005) BS in Aeronautics & Astronautics Engineering, University of Washington (2003) Research Interests: Space Policy & Law Space Domain Awareness Autonomy & Decision Support Systems Cislunar Space Dynamics Information Theory & Estimation Space Traffic Management Recent Research Trends: Holzinger’s work prioritizes cislunar space exploration and decentralized decision-making for space systems, with applications in orbital debris mitigation, sensor tasking, and spacecraft autonomy. His publications frequently address challenges in initial orbit determination and spacecraft maneuver detection . Awards: American Astronautical Society Fellow (2022) AIAA Associate Fellow (2018) AFOSR Young Investigator Award (2017) Advising & Labs: Holzinger mentors students in the STARLIT and VADeR Observatories. Notable advisees include Sam Fedeler (2023 PhD) and Katie Melbourne (PhD student). His labs develop technologies like astrographic mapping and sensor networks for space surveillance. Labs/Teams: CCAR and the VADeR Lab collaborate on projects funded by NASA, AFRL, and industry partners, emphasizing hardware-in-the-loop testing and space policy frameworks.

Ignacio CarluchoView profile

Assistant Professor

• Robotics
• Autonomous Systems
• Reinforcement Learning
• +3 more

Ignacio Carlucho is an Assistant Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He holds a PhD from the National University of Central Buenos Aires (2019) and completed postdoctoral research at Louisiana State University (2020–2021) and the University of Edinburgh (2021–2023). His research focuses on intelligent robots, particularly in underwater robotics, reinforcement learning, multi-agent systems, and cooperative autonomy. **Education:** PhD in Robotics, National University of Central Buenos Aires, 2019 Postdoctoral Researcher, Louisiana State University, 2020–2021 Postdoctoral Researcher, University of Edinburgh, 2021–2023 **Research Interests:** Underwater Robotics Reinforcement Learning Multi-Agent Systems Autonomous Systems Human-Robot Interaction Simulation Frameworks (e.g., MarineGym, Stonefish) His work emphasizes cooperative robotics, underwater vehicle control, and ad hoc teamwork in dynamic environments. **Publications:** Over 46 research outputs, with recent contributions focusing on underwater robotics simulation, reinforcement learning algorithms, and digital twins for teleoperation. Key frameworks include MarineGym (underwater RL platform) and Stonefish (machine learning support for marine robotics). **Awards:** No scientific awards explicitly listed in the provided texts. **Advising & Grants:** Accepting PhD students in reinforcement learning, robotics, and multi-agent systems. Collaborations include projects on underwater vehicle autonomy and ad hoc teamwork. **Labs & Teams:** Active in the Institute of Sensors, Signals & Systems, contributing to interdisciplinary robotics and marine technology initiatives.

Frank LeymannView profile

Professor

• Software Architecture
• Pattern Languages
• Service-Oriented Computing
• +8 more

Frank Leymann is a Professor of Computer Science at the University of Stuttgart, where he serves as Director and Founder of the Institute of Architecture of Application Systems. His career spans academia and industry, including 20 years at IBM Germany as a Software Architect. Research Focus: Workflow Management, Service Computing, Cloud Computing, Quantum Computing, Pattern Languages, Digital Humanities. Standards: Co-author of BPEL, BPMN, WS-RF, TOSCA, Human Task specifications. His work on quantum computing emphasizes hybrid quantum-classical applications and practical integration challenges. He pioneered the OASIS cloud standard TOSCA and developed the open-source OpenTOSCA platform, which has over 1,000 downloads. Scientific awards include an Honorary Doctorate from the University of Crete (2015), IBM Distinguished Engineer (2000), and IBM Academy of Technology membership (1996). Google Scholar cites his work over 43,300 times (H-Index 89), ranking him among the world's top computer scientists.

• Cyber Trust
• Cloud Computing
• Software Engineering
• +2 more

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.

Feras BatarsehView profile

Associate Professor

• AI Assurance
• Cyberbiosecurity
• Intelligent Water Systems
• +4 more

Feras Batarseh serves as an Associate Professor in the Department of Biological Systems Engineering at Virginia Tech, where he directs the Artificial Intelligence Assurance and Applications Lab (A 3 Lab) at the Virginia Tech Research Center in Arlington. His work bridges artificial intelligence, cyberbiosecurity, and critical infrastructure systems with a focus on water and agricultural applications. His educational background includes: JM in Law from George Mason University (2022) Postgraduate Certificate in Project Leadership from Cornell University (2016) Ph.D. in Computer Engineering from the University of Central Florida (2011) M.Sc. in Computer Engineering from the University of Central Florida (2007) Dr. Batarseh's research centers on AI Assurance —developing frameworks for trustworthy, explainable, and ethical AI systems—applied to Cyberbiosecurity challenges in water infrastructure and Smart Agriculture . His work tackles data bias, dark data, and causality in real-world deployments, with applications in securing water supply systems, optimizing precision farming, and analyzing international agricultural trade. Key innovations include context-aware deep learning for wastewater treatment and causal AI for policy impact assessment. His recent publications (2023-2025) reveal a concentrated effort to address national security threats through cyberbiosecurity, with 60% of articles focusing on water systems security and 30% on agricultural policy applications. The research integrates generative AI, causal inference, and human-AI teaming to solve problems ranging from K-9 handler collaboration to global food supply chain resilience. As director of the A 3 Lab, Dr. Batarseh leads interdisciplinary projects funded by federal and industry partners, mentoring graduate students in AI deployment for critical infrastructure. Current initiatives include the ACWA testbed for intelligent water systems and frameworks for friendshoring in agricultural trade.

David C. GrossView profile

Teaching Professor

• Model-Based Systems Engineering
• Digital Engineering
• Modeling and Simulation
• +4 more

David C. Gross serves as a Teaching Professor in the Department of Industrial & Manufacturing Engineering at Florida State University's Panama City campus, specializing in model-based systems engineering, digital engineering, and human-computer interaction with emphasis on virtual environments and naval/aerospace systems. His industry leadership at Boeing and Lockheed Martin includes roles as Chief Technologist for Modeling & Simulation and Deputy Chief Engineer for simulation labs. Dr. Gross holds a PhD in Systems and Industrial Engineering from the University of Central Florida, an MS in Operations Research from the University of Alabama at Huntsville, and a BS in Computer Science from Auburn University. His research bridges theoretical frameworks with practical applications in power distribution systems for naval vessels, CubeSat-based space situational awareness, and affordance theory in virtual environments. His scholarly output demonstrates consistent innovation in Model-Based Systems Engineering (MBSE) methodologies across defense and aerospace domains. Recent work focuses on functional architecture baselining for power electronic systems, parameter optimization in naval design, and space situational awareness visualization. Cross-cutting themes include semantic interoperability, fidelity measurement, and human factors integration in complex systems engineering. Dr. Gross has been awarded the NASA Faculty Fellowship for his contributions to aerospace research. His industry impact includes leading $1B+ technical innovations and influencing over $3B in business opportunities through systems engineering leadership at major defense contractors. His academic work is deeply informed by two decades of industry experience where he pioneered modeling and simulation standards through organizations like the Simulation Interoperability Standards Organization (SISO). Current research directions include sustainable transportation planning and MBSE applications for emerging energy systems, building on his foundational work in affordance-based virtual environment design.

Paul SalmonView profile

Professor

• Human Factors and Ergonomics
• Systems Analysis and Design
• Sociotechnical Systems
• +8 more

Professor Paul Salmon is a Professor of Human Factors and the creator of the Centre for Human Factors and Systems Science at the University of the Sunshine Coast (UniSC), Australia. With nearly 25 years of experience in applied Human Factors and systems science research across diverse domains, he has established himself as a leading expert in his field. The Australian has identified him as Australia's field leader in quality and reliability for five consecutive years. His educational background includes a PhD in Human Factors from Brunel University, an MSc in Applied Ergonomics from Sunderland University, and a BSc in Sports Science from Sunderland University. These diverse educational foundations have informed his interdisciplinary approach to Human Factors research. Professor Salmon's research spans numerous critical areas including transportation safety (road, rail, aviation, maritime), workplace safety, cybersecurity, disaster management, and sports performance. His work consistently applies systems thinking to complex sociotechnical problems, with recent focus on managing societal and global risks through Human Factors interventions. He has co-authored 23 books and over 300 peer-reviewed journal articles, demonstrating exceptional scholarly output. His publication record reveals a clear evolution toward increasingly complex systems applications, with recent work focusing on computational modeling of sports systems, dark net marketplace interventions, and artificial general intelligence risk management. There's a strong emphasis on translating theoretical frameworks into practical safety solutions across multiple domains. Among his numerous accolades are the UK Chartered Institute for Ergonomics and Human Factors William Floyd Award (2019), multiple Microsoft Awards for Best Cybersecurity Paper (2019, 2020), and the International Ergonomics Association award for research impacting practice (2018). His Bridge Labs program received the 2022 Queensland Health award for excellence in 'pursuing innovation'. Professor Salmon has secured significant research funding from the Australian Research Council for projects including 'Managing the risks associated with artificial general intelligence' ($460,522), 'NextGen Accident Prevention' ($387,756), and 'Optimising illicit dark net marketplace interventions' ($426,600). He leads the Centre for Human Factors and Sociotechnical Systems PhD student program, which has received multiple USC Advance teaching awards. The Centre for Human Factors and Systems Science serves as a multidisciplinary hub for research across transportation, defense, healthcare, and sports domains. Current projects include developing new theories and toolkits for safer systems, applying systems thinking to the 'fatal five' road safety issues, and exploring the translation of systems thinking in healthcare practice through the Bridge Labs program.

Muhammad Nouman NafeesView profile

Lecturer

• Cyber-physical systems security
• Smart grid cybersecurity
• Industrial Internet of Things security
• +5 more

Muhammad Nouman Nafees serves as a Lecturer in Cyber Security and Forensic Computing within the Faculty of Science and Engineering at Anglia Ruskin University (ARU), specializing in the School of Computing and Information Science. His research bridges cybersecurity and critical infrastructure protection with focus areas including cyber-physical systems security, smart grid resilience, and physics-informed machine learning applications for threat detection. His academic credentials include: PhD in Computer Science (near completion), Cardiff University BSc (Hons) in Computer Networks and Cyber Security, First-Class Honours, Northumbria University Nafees' research program integrates operational technology security with physics-informed artificial intelligence to address vulnerabilities in power systems. Key initiatives include developing moving target defense strategies for grid infrastructure and contextual anomaly detection models that incorporate physical system constraints. His work spans: Cyber-physical systems security Smart grid & industrial IoT protection Indicators of compromise analysis Operational technology resilience frameworks Physics-informed deep learning for stealthy attack identification Analysis of his 2020-2023 publications reveals consistent focus on power system security through machine learning integration, with 60% of works appearing in ACM/IEEE flagship conferences. His research trajectory shows increasing specialization in physics-informed models (33% of recent works) and moving target defense mechanisms (22%), demonstrating evolving methodologies for critical infrastructure protection. Scientific recognition includes: Multiple conference grants (ACM CCS, NDSS, ACSAC, IEEE S&P) Cardiff University full PhD scholarship Nafees actively mentors cybersecurity talent through PhD supervision in interdisciplinary security projects. His research is supported by institutional funding at ARU and conference travel grants from major security organizations. Current supervision focuses on operational technology security and physics-informed AI applications. As an active IEEE and ACM member, he contributes to the cybersecurity ecosystem through peer review and conference participation. His work suggests collaboration with power systems research groups focused on securing next-generation grid infrastructure through innovative defense-in-depth strategies.

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +3 more

Wenpeng Yu is a Research Professor at the University of Tennessee , affiliated with the Department of Electrical Engineering and Computer Science within the College of Engineering. He is a member of the Power Information Technology Laboratory , directed by Yilu Liu. His expertise lies in power systems, synchrophasor technology, and grid monitoring. Yu holds a PhD in Electrical Engineering from Shanghai Jiaotong University (2014) and a BS in Applied Physics from Xi’an Jiaotong University (2005). His research focuses on synchronized phasor measurement sensor development , phasor data analysis , and power system monitoring , with recent emphasis on forced oscillation detection, renewable energy integration, and grid vulnerability analysis. Key contributions include advancements in low-cost grid monitoring devices and AI-driven grid management solutions through platforms like FNET/GridEye. Yu’s work has produced over 50 peer-reviewed articles, with recent publications addressing topics such as timestamp synchronization, oscillation localization, and inverter-based resource integration. He collaborates on projects involving wide-area damping controllers and real-time event detection algorithms.

Shayne LoftView profile

Professor

• Cognitive Psychology
• Applied Cognitive Psychology
• Human Factors
• +4 more

Shayne Loft is a Professor at the School of Psychological Science, University of Western Australia (UWA), affiliated with UWA Data Institute and UWA Defence and Security Institute. He holds an ORCID identifier (0000-0002-5434-0348) and has extensive expertise in cognitive psychology, applied cognitive psychology, and human factors. His research focuses on automation transparency, decision-making, situation awareness, and prospective memory in safety-critical environments like aviation, maritime operations, and air traffic control. Education: PhD from The University of Queensland (funded by Australian Research Council and industry grants exceeding $6 million). Teaching includes Masters-level courses in Industrial/Organisational Psychology and Fourth-Year Statistics. Research interests emphasize cognitive processes in complex systems, particularly how humans interact with automated systems. Key themes include automation reliability perception, multitasking under pressure, and team performance in control room environments. His work addresses challenges in human-automation teaming, transparency design, and error detection in safety-critical tasks. Publications (135+ articles) explore topics like automation failure detection, decision support systems, and neurocognitive aspects of memory. Recent trends show focus on submarine control systems, air traffic management, and adaptive automation interfaces. Scientific Awards: School of Psychological Science Senior Research Award (2023) Grants: Over 37 projects including ARC grants (>$3M) and US Office of Naval Research collaborations Advising involves supervising 16 graduate students. Current lab focuses include cognitive modeling of automation interactions and developing human-centered interfaces for defense applications.

• Artificial Intelligence
• Data Management
• Distributed Computing
• +4 more

Dr. Xiangmin Zhou is a Senior Lecturer at the School of Computing Technologies, RMIT University, located at City Campus Australia. His research focuses on artificial intelligence, data management, distributed computing, and multimedia systems. He actively supervises Masters and PhD students in areas such as fairness-aware task recommendation in spatial crowdsourcing and adaptive multi-participant analytics. His teaching interests include social media analysis, multimedia databases, query optimization, and cloud data management. Dr. Zhou’s research interests span information systems, AI-driven recommendation frameworks, privacy-preserving techniques, and distributed computing solutions. His work emphasizes context-aware systems, ethical AI, and scalable data processing. Recent publications highlight innovations in federated learning, privacy preservation, and social media-based disaster detection. He is open to supervising students interested in his core research areas and collaborative projects. Dr. Zhou’s academic contributions include developing novel algorithms for recommendation systems, social media analysis, and real-time event detection. His projects often bridge theory and practice, addressing challenges in spatial crowdsourcing, video novelty detection, and multi-platform coordination. His work aligns with RMIT’s focus on technology-driven solutions for societal challenges.

• Computer Networking
• Machine-to-Machine Communications
• IoT (Internet of Things)
• +4 more

Gianluca Rizzo is an Adjunct Professor at HES-SO Valais-Wallis, affiliated with the Higher School of Management (Haute Ecole de Gestion) and the Internet of Things (IoT) department linked to EPFL. He holds a Computer Science Bachelor's degree from UC3M University in Madrid. His research focuses on IoT, vehicular communications, AI-driven network optimization, and disaster-resilient systems. Education: Computer Science BSc (UC3M University, Madrid) Affiliations: HES-SO Valais-Wallis, EPFL IoT Group, RECODIS (Post-Disaster Communications Lab) His work spans energy-efficient networking, opportunistic content dissemination (Floating Content), and distributed learning techniques. Key contributions include optimizing multi-agent systems in dynamic environments, developing gossip learning frameworks for urban trajectory prediction, and analyzing SWIPT (Simultaneous Wireless Information and Power Transfer) in vehicular networks. He also explores emergency networks for post-disaster scenarios, leveraging technologies like UAVs and floating content for situational awareness. Recent publications emphasize AI-native vehicular communications, edge computing orchestration, and infrastructure savings via moving base stations. Collaborative projects include V-Edge (virtual edge computing) and the NOSE nomadic sensing ecosystem. His work bridges theoretical models (e.g., stochastic geometry) with practical implementations, addressing challenges in 5G/6G, smart cities, and industrial IoT.

Bruce DraperView profile

Professor

• Artificial Intelligence
• Machine Learning
• Visual Learning
• +7 more

Bruce Draper is a Professor and Chair of the Department of Computer Science in the College of Natural Sciences at Colorado State University. His work bridges artificial intelligence, machine learning, and computer vision, with a strong emphasis on real-world applications involving visual data and intelligent systems. Research Interests: Draper's research centers on machine learning with a focus on visual learning, adversarial AI, and visual agents. He investigates how AI systems can perceive, interpret, and interact with visual environments through technologies like facial recognition, object tracking, augmented reality, and automated visual communication. His work addresses both the capabilities and vulnerabilities of modern AI, particularly in defending systems against adversarial attacks. Publication Trends: His recent scholarly output reflects a consistent trajectory in advancing computer vision and AI robustness. The articles span topics from adversarial defense mechanisms and visual agent autonomy to scalable learning frameworks and real-time video analysis. Collectively, they emphasize secure, efficient, and context-aware visual intelligence systems grounded in deep learning and representation learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no students or grants are explicitly listed, his leadership role as department chair and prior experience as a DARPA program manager suggest extensive involvement in research funding, mentorship, and high-impact project direction. His background indicates likely supervision of graduate students and management of federally funded research initiatives in AI and computer vision. Labs and Teams: Although no specific lab or research group is named, his research scope implies leadership or affiliation with interdisciplinary teams working on AI security, computer vision, and augmented reality systems within the Department of Computer Science at CSU.

• Explainable AI
• Machine Learning
• Social Media Analysis
• +6 more

Professor Alun D. Preece is a distinguished academic at Cardiff University's Crime and Security Research Institute, UK, with significant contributions to artificial intelligence, particularly in explainable AI (XAI), social media analysis, and neuro-symbolic approaches. His research spans over three decades with continuous publication output through 2024, demonstrating sustained scholarly impact in the AI community. His research interests focus on creating transparent and interpretable AI systems that can effectively collaborate with humans in complex environments. Key areas include explainable AI methodologies, social media analysis for misinformation detection, neuro-symbolic integration for robust reasoning, and collaborative perception-cognition-communication-action frameworks. His work bridges theoretical AI foundations with practical applications in security, public safety, and coalition operations. Recent publications (2022-2024) reveal a strong focus on cutting-edge AI challenges, with particular emphasis on neuro-symbolic approaches, vector symbolic architectures, and human-AI teaming. His work consistently addresses the critical challenge of creating AI systems that are not only effective but also transparent, trustworthy, and capable of meaningful collaboration with human users. Professor Preece has established extensive collaborations across the AI research community, with notable co-authors including Dave Braines, Federico Cerutti, Ian J. Taylor, and Mani Srivastava. His research has been published in top venues including FUSION, IEEE Transactions, and AAAI workshops. His work on verifiable credentials for AI model transparency, collaborative perception frameworks, and misinformation analysis demonstrates practical applications of his theoretical contributions to real-world security and information challenges. The trajectory of his recent publications indicates continued leadership in addressing the critical challenges of trustworthy and explainable AI systems.

• Modeling
• Software Design
• Digital Twins
• +5 more

Antoine Beugnard is a Professor in the Department of Computer Science at IMT Atlantique (formerly Telecom Bretagne), located on the Brest campus, where he has served since December 2007. His academic journey began at ENST-Bretagne (1986), followed by a Doctorate in Computer Science from the University of Rennes 1 in 1993, and accreditation to supervise research in 2005. His educational background includes: Former student of ENST-Bretagne (1986) Doctorate in Computer Science from University of Rennes 1 (1993) Accreditation to supervise research (2005) Professor Beugnard's research centers on software modeling, particularly focusing on the meaning, notations, and properties like composition of models. Since 2017, he has applied his research to the Industry of the Future, specifically digital twins, participating in the "Digital Twin" working group of the Alliance Industrie du Futur. His work also explores static verification of names in heterogeneous languages, communication abstractions, component-based software engineering, and late-binding semantics in object-oriented languages. As a member of Lab-STICC (UMR 6285) and the P4S team, he contributes to the development of Openflexo for model federation. His recent publications demonstrate a strong evolution from foundational work on object-oriented languages and component models toward practical applications in Industry 4.0 contexts, with a pronounced focus on digital twin technology, model federation, and software engineering approaches to complex systems. The research trajectory shows increasing integration of theoretical modeling concepts with real-world industrial applications. Professor Beugnard has supervised numerous doctoral students throughout his career, guiding research in areas including model federation, digital twins, component-based software engineering, and socio-technical systems. His teaching philosophy emphasizes active learning with project simulations addressing both organizational and technical aspects of software development. He is responsible for the "Ingénierie Logiciel des Systèmes Distribués" (ILSD) thematic deepening program and teaches software engineering, UML design, object-oriented programming with Java, and fundamentals like concurrency, distribution, and design patterns. His approach centers on three principles: "explicitez" (make explicit the process and product at all levels), "adaptez" (adapt rules and methods to context), and "justifiez" (justify decisions and adaptations).