Andrea Garulli is a Professor in Control Systems at the Department of Information Engineering and Mathematics (DIISM), University of Siena. He served as Dean of the Faculty of Engineering (2011-2012) and Director of DIISM (2015-2021). His research focuses on control systems, including system identification, state estimation, robust control, multi-agent systems, and aerospace applications. He co-authored the book *Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems* and developed the LFR_RAI toolbox for robustness analysis of uncertain models. Garulli has led projects like ACANTO (social robot navigation) and DALi (autonomous navigation for elderly care). His teaching includes courses on system dynamics, state estimation, and filtering. He is an Associate Editor for *Automatica* and has contributed to initiatives like the Automatic Control Telelab for remote experiments with mobile robots.
Claudia Roda is a Professor of Computer Science and Director of the Master’s program in Human Rights and Data Science at The American University of Paris (AUP). She co-founded the Technology and Cognition Lab and the Working Group on Human Rights and Digital Technology, and holds the UNESCO Chair in Artificial Intelligence and Human Rights with Prof. Perry. Her research focuses on digital technology’s impact on human behavior and social structures, including attention computing and multi-agent systems. Education: Bachelor’s in Computer Science from the University of Pisa, Italy; Master’s and PhD in Engineering from the University of London (Queen Mary and Westfield College). She has held leadership roles including Director of the Division of Arts and Science (2008–2011), Director of AUP’s collaboration with The New School (2010–2013), and Dean (2015–2018). Research interests include ethical AI, privacy by design, and the societal implications of digital technologies. Recent publications address AI governance, mental privacy, and regulatory frameworks. She has been awarded the AUP Board of Trustee Award for Research (2007), the Provost Award for Innovation (2021), and the Graduate Student Government Award (2023). Grants and collaborations include the €781K GaSP project (2015–2018) and the €1.1M PRIPARE project (2013–2015). She frequently speaks at global conferences on AI ethics, privacy, and human rights, including UNESCO’s Digital Learning Week (2024) and AI Governance Global (2024). Labs/Teams: Technology and Cognition Lab, UNESCO Chair collaboration, and the Working Group on Human Rights and Digital Technology. Current projects include exploring AI regulations and privacy challenges in digital environments.
Professor TAN Ah Hwee is a Full-time Faculty member and Lee Kong Chian Professor of Computer Science at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU). He serves as the Associate Dean (Research) in SCIS and leads research in Artificial Intelligence, Machine Learning, and Health Informatics. His work spans neural networks, multi-agent systems, and healthcare applications such as Mild Cognitive Impairment prediction. He holds a PhD from Boston University (1994). Research Focus: His research integrates adaptive resonance theory, federated learning, and spatial-temporal modeling. Key areas include knowledge graph refinement, episodic memory systems for Activity of Daily Living (ADL) prediction, and explainable AI in multi-agent reinforcement learning. He also develops technologies for aging-in-place support and social media analytics. Recent Contributions: Recent work emphasizes hierarchical multi-agent models (HiSOMA), federated learning frameworks (FedART), and AI-driven health monitoring systems. His publications address challenges in self-organizing neural networks, context-aware reinforcement learning, and medical diagnostics through ambient sensing. Advising & Impact: Advises students like TEH Seng Khoon and Cassandra TAN Hui Ming. His projects include the eHealthPortal for elderly support and Silver Assistants for aging-in-place solutions. Research outputs bridge theoretical advancements in AI with real-world applications in healthcare and smart environments.
Janarthanan Rajendran is an Assistant Professor and the Sexton Chair in Reinforcement Learning at the Faculty of Computer Science, Dalhousie University, in Halifax, Nova Scotia, Canada. He is actively involved in research, teaching, and mentoring, with a focus on deep reinforcement learning and its applications in complex, dynamic environments. Education: Postdoctoral Fellow, Mila Quebec AI Institute and University of Montreal, Canada (2023) PhD in Computer Science and Engineering (AI stream), University of Michigan, Ann Arbor, USA (2021) MTech and BTech in Electrical Engineering, Indian Institute of Technology Madras, India (2016) His research focuses on enabling machines to learn through interaction, with core interests in deep reinforcement learning, model-based RL, multi-agent systems, transfer learning, and applications in materials science and economics. He also explores the integration of large language models and foundation models into reinforcement learning frameworks. His work emphasizes adaptivity, lifelong learning, and societal implications of AI. The most recent publications show a strong trend in advancing cooperative multi-agent systems, developing adaptive and memory-efficient RL methods, and applying RL to real-world challenges such as crystal design and dynamic pricing. His research bridges theoretical innovation with practical application, often in interdisciplinary contexts. Scientific Awards: Sexton Chair in Reinforcement Learning Dr. Rajendran is actively involved in mentoring graduate students and fostering an inclusive research environment. He is currently recruiting PhD and MCS students at Dalhousie University. He has no formal grants listed in the text, but his research chair and active publication record suggest strong funding support. He is also engaged in the broader AI community, having organized and participated in major conferences such as the Atlantic Canada AI Summit and NeurIPS. Labs and Research Groups: He leads a research group focused on deep reinforcement learning at Dalhousie University, working on topics including model-based RL, off-policy learning, and leveraging external knowledge sources. The group emphasizes inclusivity and supports underrepresented groups in computer science research.
Xudong Chen is an Associate Professor in the Department of Electrical & Systems Engineering at Washington University in St. Louis, part of the McKelvey School of Engineering. Previously, he held an Assistant Professor position at the University of Colorado, Boulder. He earned a BS in Electronics Engineering from Tsinghua University (2009) and a PhD in Electrical Engineering from Harvard University (2014). His research focuses on control theory, decision theory, dynamical systems, stochastic processes, and network science, with a particular emphasis on large-scale multi-agent systems. Applications span quantum systems, smart materials, neuroscience, social science, robotics, drones, and spacecraft. His work develops advanced mathematical tools and engineering methods to address challenges in these complex systems. Notable Awards: 2023 A.V. Balakrishnan Early Career Award 2021 Donald P. Eckman Award NSF CAREER Award (2021) AFOSR Young Investigator Award (2020) Chen has secured grants including NSF support for graphon-based structural system theory and AFOSR funding. He leads a research group and maintains a lab website for collaborative projects. His work bridges theoretical foundations with practical applications across diverse engineering domains.
Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.
Houssam Abbas is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a Ph.D. in Electrical Engineering from Arizona State University and has professional experience in SoC verification at Intel and postdoctoral research at the University of Pennsylvania. His work focuses on computational ethics for AI agents, design/verification of cyber-physical systems, and autonomous systems like self-driving cars and drones. Education: Ph.D., Electrical Engineering, Arizona State University (2015) M.Sc., Electrical Engineering, Arizona State University (2006) B.Eng., Computer and Communications Engineering, American University of Beirut (2004) Abbas' research integrates deontic logic for ethical obligations in AI, distributed verification techniques for autonomous systems, and fair control algorithms for aerial missions. He co-leads the F1/10 autonomous racing initiative and teaches hands-on courses on self-driving cars. Awards: 2022 NSF CAREER Award 2022 Grainger Foundation Frontiers of Engineering Symposium Participant Grants & Projects: NSF CCRI Grant for F1/10 Racecar platforms (with Penn and Clemson) FAA ASSURE project on UAV cybersecurity Lab/Teams: His work involves the Autonomous Systems Lab , focusing on ethical AI, robotics, and formal verification tools like the F1/10 platform.
Denghui Zhang is an Assistant Professor in the School of Business at Stevens Institute of Technology. His research focuses on data science, large language models (LLMs), and business analytics, with particular emphasis on applications in financial systems, knowledge graphs, and spatio-temporal prediction. He is a member of the Stevens Institute for Artificial Intelligence and has held academic roles including reviewer positions for prestigious journals like Nature Communications and conferences such as AAAI and SIGKDD. Dr. Zhang holds a PhD in Information Systems from Rutgers University (2023) and an MS in Computer Science from the University of Chinese Academy of Sciences (2018). His educational background bridges computer science and business analytics, enabling his cross-disciplinary research. His research explores cutting-edge topics like federated learning optimization for LLMs, theory-of-mind reasoning mechanisms, and ethical AI governance. Notable contributions include turbulence forecasting models, traffic prediction frameworks, and venture capital investment strategies leveraging reinforcement learning. Dr. Zhang has received prestigious recognitions including the ICIS 2023 Best Student Paper Award and AAAI-23 Student Scholar distinction. His work frequently addresses practical challenges in AI ethics, financial decision-making systems, and scalable machine learning architectures. He actively contributes to academic communities through program committee roles for top conferences and has pioneered novel methodologies in multi-agent financial systems and graph neural network design.
Brendan Englot is the Anson Wood Burchard Endowed Professor and Director of the Stevens Institute for Artificial Intelligence (SIAI) at Stevens Institute of Technology. He holds a Ph.D., S.M., and S.B. in Mechanical Engineering from MIT. His research focuses on perception, navigation, and decision-making algorithms for mobile robots in complex environments, particularly underwater and autonomous systems. He has held roles such as Professor (2024–present), Associate Professor (2020–2024), and Assistant Professor (2014–2020) at Stevens, and previously worked at the United Technologies Research Center and Yale University. Education: Ph.D., Mechanical Engineering, MIT, 2012 S.M., Mechanical Engineering, MIT, 2009 S.B., Mechanical Engineering, MIT, 2007 Research Interests: Englot’s work emphasizes robust autonomy for unmanned vehicles in degraded conditions, leveraging AI to enhance situational awareness and decision-making under uncertainty. Key areas include navigation algorithms for underwater, aerial, and ground vehicles, sensor fusion, and multi-agent systems. Awards: AMiner’s Top 100 Robotics Scholars (2023, 2024) Provost’s Award for Research Excellence (2021) NSF CAREER Award (2017) ONR Young Investigator Award (2020) Grants & Advising: Englot has secured over $5M in grants as PI, including projects on underwater exploration, reinforcement learning for navigation, and robotic inspection systems. He mentors students in robotics, autonomy, and AI applications. Labs & Teams: Leads the SIAI and collaborates on projects involving autonomous vehicles, SLAM systems, and marine robotics through Stevens’ interdisciplinary initiatives.
Dr. Lei Fan is an Assistant Professor in the Department of Engineering Technology at the University of Houston, with a joint appointment in the Electrical and Computer Engineering (ECE) Department. His research focuses on power system operations, optimization algorithms, quantum computing, and energy storage systems. He holds a Ph.D. from the University of Florida and a B.S. from Hefei University of Technology. Education: Ph.D., University of Florida B.S., Hefei University of Technology Research Interests: Dr. Fan’s work bridges theoretical optimization and practical energy systems, including quantum algorithms for power grid management, battery storage planning, and distributed quantum computing architectures. His LORE (Learning & Operations Research & Energy) lab explores cutting-edge applications in teleoperation, satellite networks, and environmental monitoring. Publications: Recent work emphasizes quantum computing’s role in solving complex optimization problems, such as entanglement routing in satellite networks and distributed hydrogen-power systems. His research also integrates machine learning for methane plume detection and hyperspectral imaging. Labs/Teams: He leads the LORE lab, advancing interdisciplinary research in energy systems and quantum technologies.
Ji Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Stony Brook University. He is affiliated with the Department of Applied Mathematics and Statistics, the Institute for AI-Driven Discovery and Innovation, and the Institute for Engineering-Driven Medicine. He holds a Ph.D. from Yale University and completed postdoctoral research at the University of Illinois at Urbana-Champaign and Arizona State University. His research focuses on distributed control, multi-agent systems, distributed optimization, and cyber-physical systems, with applications to social networks, epidemic models, and quantum networks. Key areas of investigation include consensus algorithms, network topology design, and resilient reinforcement learning. Recent work explores topics like entanglement routing in quantum networks, decentralized multi-armed bandits, and bi-virus disease dynamics. His contributions span theoretical frameworks and practical implementations in domains such as smart grids, medical prosthetics, and secure microgrid control. Education: Ph.D. in Electrical Engineering, Yale University (201X) Postdoctoral Research: UIUC (Coordinated Science Lab), ASU (School of ECEE) Labs: Active in distributed systems, quantum control, and networked epidemic modeling
Jun Yan is an Associate Professor and Concordia University Research Chair in Artificial Intelligence in Cyber Security and Resilience at the Concordia Institute for Information Systems Engineering (Concordia University). His research focuses on cybersecurity, smart grid systems, and AI-driven solutions for energy and communication networks. He supervises graduate students in programs such as Information Systems Security (MASc), Computer Science (MCompSc), and Information and Systems Engineering (PhD). Research Highlights : Cybersecurity of distributed energy systems, AI penetration testing frameworks, and resilient transactive energy markets. Awards : Holds a prestigious university research chair in AI-driven cybersecurity. His work integrates machine learning with domain-specific challenges in smart grids, IoT security, and multi-agent systems. Notable contributions include frameworks for detecting adversarial attacks on power systems, optimizing renewable energy integration, and developing AI tools for penetration testing. His articles reflect a strong emphasis on interdisciplinary solutions blending cybersecurity, energy systems, and advanced computing. Yan’s research also addresses policy and infrastructure challenges in sustainable energy systems, including waste management policy analysis and optimal configuration of hybrid renewable systems. He has pioneered open-source co-simulation platforms like PEMT-CoSim and Quantum-Sim for secure energy trading and quantum communication in grids. He actively engages in grant-funded projects and advises on both academic and applied aspects of cybersecurity and intelligent systems.
Prof Noel O'Connor is a Full Professor at Dublin City University's School of Electronic Engineering, specializing in cutting-edge research at the intersection of artificial intelligence (AI), medical imaging, robotics, and smart city technologies. His work spans applications such as cardiac MRI reconstruction, robotic manipulation using reinforcement learning, and the development of the Smart DCU Digital Twin for autism-friendly university environments. Research interests include AI-driven medical diagnostics, multimodal data fusion, and adaptive systems. His contributions to cardiac MRI reconstruction and transformer-based medical imaging analysis reflect a strong focus on healthcare innovation. He also explores ethical AI practices to reduce social bias in foundation models. Recent work emphasizes smart infrastructure projects, such as optimizing parking recommendations for electric vehicles and enhancing accessibility through digital twin frameworks. His research often integrates real-time sensor data and multi-agent systems to address complex urban challenges. No scientific awards are listed. Collaborations include the ASU-DCU International Research Program on Sensors and Machine Learning. Advising details and grant information are not explicitly provided.
Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.
Georgios Andrikopoulos is an Assistant Professor at KTH Royal Institute of Technology's Department of Industrial and Environmental Management (ITM), affiliated with the Digital Futures Faculty. He serves as Co-Principal Investigator (Co-PI) on the 'Real-time exoskeleton control for human-in-the-loop optimization' project, focusing on advanced robotics and human-technology interaction. His research spans soft robotics, exoskeleton systems, and adaptive climbing robots, with applications in healthcare, aerospace, and education. Key projects include: "Connecting Bodies – Designing Shape-changing Wearables" (Postdoc Fellowship, 2023–2025) "Advancing real-time exoskeleton control for human-in-the-loop optimization" (Demonstrator Project) SEC scholar collaboration with Mohamed Elbadawi (May–June 2024) Research interests emphasize: Soft robotic actuators for safe human interaction Optimization of compliant mechanisms in robotics Vortex adhesion systems for autonomous inspection Design of child-friendly interactive robots His work integrates robotics with control systems, biomechanics, and human-centered design. Over 50 peer-reviewed articles demonstrate contributions to exoskeleton technology, climbing robot algorithms, and soft actuator modeling. He advises students like Laia Turmo Vidal (Postdoc) and Mohamed Elbadawi (SEC scholar). Affiliated with the cross-disciplinary Digital Futures initiative, he collaborates with Stockholm University and RISE Research Institutes to address societal challenges through digital innovation.
