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Dr. Hung Cao is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick and the Founder and Director of the Analytics Everywhere Lab. His research develops intelligent systems that are distributed, resource-efficient, trustworthy, and human-centred. He established the Analytics Everywhere framework, which transforms continuous Internet of Things data streams into real-time and long-term intelligence across the edge–fog–cloud continuum. Building on this foundation, his current research program in Collective Edge Intelligence investigates how connected devices, digital twins, AI agents, domain experts, and communities can learn, reason, and make decisions collaboratively.
His expertise spans Edge AI, federated learning, the Internet of Things, explainable and contestable AI (XAI/CAI), multimodal sensing, streaming analytics, digital twins, and intelligent cyber-physical-social systems. His scientific contributions include edge–fog–cloud architectures for streaming analytics, IoT-GIS platforms for automated analytical tasks, privacy-aware learning at the edge, mechanisms that enable people to challenge and refine AI-supported decisions, and digital-twin platforms integrating sensing, simulation, artificial intelligence, visualization, and decision support. His research group has released more than 20 open-source tools, public software artifacts, prototypes, benchmarks, demonstrations, and datasets, reflecting a commitment to reproducible research and technology translation. Dr. Cao has published more than 60 peer-reviewed journal and conference papers. His work has appeared in leading venues spanning machine learning, artificial intelligence, engineering, healthcare computing, multimedia, and intelligent systems. Representative journals include Information Fusion, Applied Energy, and ACM Transactions on Computing for Healthcare. His conference publications include papers at highly ranked venues such as IJCAI, ACM Multimedia, and PAKDD. He also co-edited a Springer volume on big data intelligence.
Dr. Cao has contributed to collaborative research initiatives valued at more than CAD $5 million, with approximately CAD $1.5 million attributed to his share. His research has been supported by NSERC, CFI, NRC, ResearchNB, Mitacs, ACOA, and industry partners including Cisco, Eigen Innovation, Vimsoft, NB Power, and General Data IT Professionals. Major initiatives include the CAD $2.29-million Maritime 3D Digital Twin project, an NSERC Discovery Grant on edge–cloud data extraction for underwater IoT, NRC-supported digital geriatric assessment for aging-in-place, a CFI-funded Machine Learning Health Vault, and Edge AI for proactive heart-failure symptom monitoring. His industry-engaged research also addresses manufacturing inspection, mobility analytics, workforce scheduling, rare-event prediction, climate adaptation, and retrieval-augmented AI systems.
Dr. Cao holds a PhD in Geomatics Engineering, specializing in Data Science, and a Diploma in University Teaching from the University of New Brunswick; an MSc in Computer Science with First Class Honours from University College Dublin; and a BEng in Computer Engineering from VNUHCM-University of Information Technology (VNU-UIT). Before joining the Faculty of Computer Science, he was a Postdoctoral Fellow and Data Scientist with UNB’s People in Motion Lab, where he extended Analytics Everywhere toward Intelligence Everywhere. Earlier in his career, he served as a Lecturer and Researcher in Computer Engineering at VNU-UIT.
His honours include the Government of Ireland’s Irish Aid IDEAS Scholarship and the Harrison McCain Young Scholar Award. Research led by his students has received a SMARTGREENS Best Student Paper Award and two IEEE ICCE Best Session Presentation Awards. Dr. Cao serves as an Associate Editor for IET Smart Cities, the International Journal of Pervasive Computing and Communications, and Discover Artificial Intelligence, from which he received the 2026 Editorial Contribution Award and Author Service Award.
The Analytics Everywhere Lab welcomes interdisciplinary collaborations with researchers, healthcare professionals, governments, communities, and technology companies working in responsible AI, Edge AI, digital twins, intelligent agents, and connected intelligent systems.
