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Constantinos AntoniouView profile

Professor

• Transportation Systems Engineering
• Intelligent Transport Systems (ITS)
• Behavioral Economics in Transport
• +5 more

Prof. Constantinos Antoniou is a Full Professor in the Chair of Transportation Systems Engineering at the Technical University of Munich (TUM), part of the TUM School of Engineering and Design. He holds a Diploma in Civil Engineering from the National Technical University of Athens (1995), an MS in Transportation (1997), and a PhD in Transportation Systems (2004) from MIT. His research focuses on transportation systems engineering, including modeling/simulation, intelligent transport systems (ITS), behavioral economics in transport, road safety, and sustainable transport systems. He has authored over 500 scientific publications, including over 210 peer-reviewed journal papers, and serves as Co-Editor-in-Chief of Transportation Research Part A . Awards include the Francquis Chair (2023), IEEE ITS Outstanding Application Award (2011), and Marie Curie Fellowship (2006–2008). He has secured over €4M in third-party funding, leading projects such as SCUDO, CulturalRoad, and PHOEBE. Currently supervising 17 PhD students, he has mentored seven graduates who now hold academic positions at institutions like UCL and UPC. Research interests span autonomous vehicles, traffic resilience, and policy-driven transport solutions. Key projects include analyzing urban air mobility, MaaS potential, and pandemic impact on transport networks. His work contributes to UN Sustainable Development Goals, emphasizing sustainable cities and communities.

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +6 more

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.

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +5 more

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

Charles GillanView profile

Senior Lecturer

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +4 more

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

Li LiView profile

Professor

• Environmental Engineering
• Water Quality
• Climate Change
• +5 more

Li Li is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. She leads the Li Reactive Water research group, focusing on the Critical Zone and its response to climate change and human activities. Her work examines interactions between water, soils, rocks, roots, and microbes, with implications for water quality and sustainability. Research interests include reactive transport modeling, stream chemistry dynamics, and the impacts of climate extremes on hydrological systems. She has contributed to high-profile studies such as the discovery of ancient carbon release via rivers and the effects of extreme rainfall on nutrient loss in American soils. Recent recognitions include dual awards from the Earth and Space Association (2024) and being named an Institute of Energy and the Environment Fellow. Her lab actively collaborates on projects like the BioRT-HBV model and applications of deep learning in hydrological data analysis. Key projects: Facilitating environmental investigations with single-column models, maximizing CO₂ trapping in geological formations Leadership roles: IEE Fellow, Principal Investigator of NSF-funded research

Kevin W. HamlenView profile

Professor

• Cybersecurity
• Software Security
• Formal Methods
• +6 more

Kevin W. Hamlen is the Louis A. Beecherl, Jr. Distinguished Professor in the Department of Computer Science at the University of Texas at Dallas. He serves as Executive Director of UT Dallas' Cyber Security Research and Education Institute. His research focuses on language-based security , binary software hardening , cyberdeception , and formal program verification . He has received multiple grants from agencies like AFOSR, NSF, DARPA, and industry partners including Lockheed Martin and Intel. PhD and MS from Cornell University BS from Carnegie Mellon University His research explores automated approaches to software security through techniques like binary disassembly , control-flow integrity , and honey-patching . He has pioneered methods for malware defense and cloud/web/mobile security . Recent work examines adaptive cyberdeception and GPU-based security frameworks . His publications span binary code manipulation , malware mitigation , and blockchain security . Key awards include the NSF IUCRC Technology Breakthrough Award and two CSAW Best Paper 2nd Prizes . He advises numerous PhD students, many of whom now work at Google, IBM, and Microsoft. His book Autonomous Cyber Deception (Springer, 2019) with Ehab Al-Shaer and Cliff Wang provides comprehensive coverage of adaptive cyberdeception strategies.

• Data Science
• Large Language Models
• Business Analytics
• +5 more

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.

Muhammad R. HajjView profile

Professor

• Nonlinear Dynamics
• Fluid Mechanics
• Structural Dynamics
• +12 more

Muhammad R. Hajj is the George Meade Bond Professor, Chair of the Department of Civil, Environmental and Ocean Engineering, and Director of the Davidson Laboratory at Stevens Institute of Technology. With a distinguished career spanning over three decades, his expertise lies in nonlinear dynamics , fluid-structure interactions , and energy harvesting , applying these to ship hydrodynamics , biomimetic flight/underwater vehicles , and coastal resilience . He has mentored 32 PhD students and authored over 170 journal publications. Education PhD (1990), MS (1985), Civil Engineering , University of Texas at Austin BE (1983), Civil Engineering (with Distinction) , American University of Beirut Research Interests Dr. Hajj’s work bridges nonlinear dynamics and fluid mechanics to address challenges in structural/aeroelastic stability , bio-inspired design , and renewable energy systems . His fluid-structure interaction studies focus on ship hydrodynamics , transonic flutter , and storm surge prediction , while his energy harvesting research explores piezoelectric systems , self-powered sensors , and biomimetic energy conversion . Recent Trends in Publications His 2022-2021 publications emphasize coastal extreme weather resilience via AI-driven storm surge modeling , bio-inspired robotic fish for underwater energy harvesting , and nonlinear aeroelastic systems to enhance renewable energy extraction . Themes include high-efficiency piezoelectric designs , vortex-induced vibration control , and ultrasonic contactless power transfer , reflecting his commitment to integrating nonlinear dynamics with practical engineering applications . Scientific Honors Fellow, Engineering Mechanics Institute, ASCE Distinguished Civil Engineering Alumni (AUB, 2019) Dean’s Award for Excellence in Research (VT, 2016) Distinguished Leader in Research (VT, 2016) Excellence in Research Award (VT, 2015) Advising and Grants Dr. Hajj has supervised 32 PhD students , many of whom hold prestigious roles in academia and industry. He has secured major grants, including $1.8M from the Department of Energy for floating wave energy converters , $4.94M from the Port Authority for storm surge forecasting , and $200K from the NSF for bio-inspired telemetry energy harvesting .

• Robotics
• Autonomous Systems
• Artificial Intelligence
• +3 more

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.

Mohamed IbnkahlaView profile

Professor

• Wireless Sensor Networks
• Internet of Things (IoT)
• Cognitive Radio Networks
• +5 more

Dr. Mohamed Ibnkahla is a Full Professor and NSERC/Cisco Senior Industrial Research Chair in Sensor Networks for the Internet of Things (IoT) at Carleton University's Department of Systems and Computer Engineering. He holds a Ph.D. and HDR from the National Polytechnic Institute of Toulouse (INP), France. His research focuses on IoT, wireless sensor networks, cognitive radio systems, and adaptive signal processing, with applications in smart cities, healthcare, energy, and transportation. He has led numerous industry and government-funded projects, including the Carleton-Cisco IoT Testbed. Education: Ph.D. and HDR (INP Toulouse, 1996/1998), Engineering and MSc in Electronics/Signal Processing (INP Toulouse, 1992). He previously served at Queen’s University (2000–2015) and INP Toulouse (1996–1999). Research Interests: IoT security, energy harvesting, cognitive radio networks, smart grid communication, and machine learning for wireless systems. His work spans theoretical contributions (e.g., 5 books on signal processing and IoT) and applied innovations (e.g., sensor network architectures for environmental monitoring). He has published over 150 peer-reviewed papers, 4 books, and supervised ~40 graduate students. Awards include the INP Leopold Escande Medal (1997) and Ontario’s Prime Minister’s Research Excellence Award (2000). His lab develops solutions for IoT trust management, edge computing, and decentralized access control using blockchain. Key Projects: Cisco-funded IoT sensor networks for smart cities, NSF-funded cognitive radio projects, and collaborations on eHealth systems. His research addresses IoT security challenges in healthcare, transportation, and energy grids, emphasizing scalable, energy-efficient solutions.

Girija ChettyView profile

Professor

• Multimodal Systems
• Medical Image Computing
• Computer Vision
• +7 more

Girija Chetty is a Full Professor in Computing and Information Technology at the University of Canberra's School of Information Technology and Systems. She holds a PhD in Information Sciences and Engineering and has over 35 years of experience in academia and research leadership roles, including Head of Software Engineering and Program Director of ITS courses. Her research focuses on multimodal systems, medical image computing, AI, and data science. She leads a dynamic research group comprising PhD students, postdocs, and international collaborators. Education: PhD in Information Sciences (Australia, 2007), MSc and BSc in Electrical Engineering/Computer Science (India). She has held visiting roles at Deakin University and CSIRO. Research interests span computer vision, pattern recognition, and medical diagnostics, with 200+ publications in top journals/conferences. Her work addresses global challenges via AI-driven solutions in healthcare (e.g., pain assessment systems, malaria diagnostics) and sustainability (SDG impact frameworks). Projects include AI for remote ultrasound imaging and smart farming systems. She actively collaborates with industry and global research institutions. Grants/Projects: 12 funded initiatives including AI for extreme environment healthcare, malaria pathogen detection, and big data-driven population health. Awards: Senior IEEE/Australian Computer Society membership, editorial roles in IEEE/Elsevier journals. Labs/Teams: Leads a multidisciplinary research group focused on medical AI and multimodal systems.

• Nanoelectronic Circuit Design
• Neuromorphic Computing
• Hardware Security
• +4 more

Garrett Rose is a Professor and Department Head in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK). He holds a B.S. in Computer Engineering from Virginia Tech (2001), and M.S. and Ph.D. in Electrical Engineering from the University of Virginia (2003/2006). Prior to UTK, he served as Assistant Professor at NYU Polytechnic (2006–2011) and Senior Electronics Engineer at the Air Force Research Lab (2011–2014). His research focuses on nanoelectronic circuit design, neuromorphic computing, hardware security, and memristor-based systems. He leads the SENECA Research Group and the TENNLab initiative, exploring applications in neuromorphic architectures, hardware security primitives (e.g., PUF devices), and device modeling. Recent work emphasizes memristor-driven neuromorphic systems, secure FPGA designs, and in-memory computing. Grants include projects on neuromorphic target detection and nanotechnology-based security solutions. Rose actively mentors students and collaborates on co-design methodologies for real-world neuromorphic applications. Education: Ph.D. Electrical Engineering, University of Virginia, 2006 M.S. Electrical Engineering, University of Virginia, 2003 B.S. Computer Engineering, Virginia Tech, 2001 Research Interests: Dr. Rose’s work spans neuromorphic hardware design, including memristor-based neural networks and spiking systems. He investigates hardware security through nanoscale devices like memristors for PUFs and side-channel resistant circuits. His team develops novel memristor models and explores applications in reconfigurable computing and energy-efficient architectures. Recent efforts focus on neuromorphic vision systems, robotic navigation, and neuromorphic processors with co-design frameworks. Grants & Projects: "Ground-roaming autonomous neuromorphic targeter" (2020) "Secure Backup and Restore for IoT using Nanotechnology" (2020) "Physically Unclonable Reconfigurable Computing System (PURCS)" (2020)

Jun YanView profile

Associate Professor

• Cybersecurity
• Artificial Intelligence
• Smart Grids
• +3 more

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.

Andrew IresonView profile

Professor

• Subsurface Hydrology
• Climate Change and Water Security
• Land-Water Management
• +3 more

Andrew Ireson is a Professor at the School of Environment and Sustainability, University of Saskatchewan, and a Member of the Global Institute for Water Security. His research focuses on subsurface hydrology, climate change impacts on water resources, land-water management, and sustainable natural resource development. He has contributed significantly to understanding soil moisture dynamics, infiltration processes in frozen soils, and groundwater-surface water interactions in boreal and prairie ecosystems. His recent work emphasizes advanced numerical modeling techniques for hydrological processes, including Richards’ Equation solutions and soil freezing dynamics. He has also developed frameworks for integrating water quality simulations with existing hydrological models (e.g., OpenWQ). His publications highlight themes like climate change adaptation, seasonal hydrology, and the role of ephemeral ponds in groundwater recharge. No scientific awards or grants are listed here, though his extensive publication record underscores his scholarly contributions. His advising roles are not detailed in the provided texts.

Md ArifuzzamanView profile

Assistant Professor

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +3 more

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.