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Analytics Everywhere Lab
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Analytics Everywhere is our proposed conceptual framework that is developed based on edge-fog-cloud continuum to handle an enormous volume of incoming data streams from IoT devices and perform a network of analytical tasks (different analytical capabilities such as descriptive, diagnostic, and predictive analytics) according to a data life-cycle. This major breakthrough framework provided us with an iterative learning experience on how to advance our research towards automated analytical tasks for the Cyber Physical Systems/Internet of Things.

Our Analytics Everywhere research laboratory is located within the Faculty of Computer Science, at University of New Brunswick in Fredericton, NB, Canada. UNB is the oldest English-language university in Canada, and among the oldest public universities in North America with a tradition of more than 230 years. UNB aims to be a university of influence through excellence and innovation in research and teaching to enable positive social change across our communities. To fullfil this vision, our lab focus on tackling scientific challenges, engaging with our academic and industry partners, and inspring our students to to become problem solvers and leaders in the world.


Exploring the practical challenges and connect them with the theoretical research gaps as well as undertaking instensive research that addresses societal and scientific challenges.


Collaborating with our partners to build prototypes for solving practical problems and transferring our research outcomes to Canadian businesses to strengthen their national and global competitive.


Extending the academic freedom and fostering our student's passion for discovering; preparing HQP for the evolving job market and connecting industrial needs to our research and teaching programs.

Recent News

  • Awards, AELab won big at The 42nd IEEE International Conference on Consumer Electronics (ICCE 2024). Atah Nuh Mih won the Best Session Presentation Award and Best Audience/Questioner Award. Asfia Kanine and Hung Nguyen won the Best Audience/Questioner Award. Congratulations!!!
  • Awards, Atah Nuh Mih and Asfia Kawnine won the Dr. Wu Yee-sun and Mrs. Wu Ho Man-yuen Memorial Graduate Bursary Award. Congratulations! You two are well deserved to receive this award.
  • Impact Story, "UNB lab uses AI to help grow local company that serves the manufacturing sector". Explore our story here.
  • Oct 17th 2023, Our article entitled "Fostering new vertical and horizontal IoT applications with Intelligence Everywhere" has been accepted to publish in Collective Intelligence Journal. This paper can be fully accessed here.
  • Oct 2023, Four papers have been accepted for Oral Prensentation at The 42nd IEEE International Conference on Consumer Electronics (ICCE 2024).
  • Oct 2023, Our research project has been received the funding from OFI Seed Fund.
  • Oct 2nd 2023, [Invited Talk] Dr. Cao will give a talk "Artificial Intelligence of Things (AIoT): A Synergy of Data, Network, and Learning" at UNB Research Institute of Data Science and Artificial Intelligence (RIDSAI).
  • Sep 2023, Dr. Cao is the recipient of Harrison McCain Young Scholars Awards 2023-2024.
  • May 15th 2023, Our paper entitled "TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques" has been accepted to publish in the 2023 IEEE International Conference on Omni-Layer Intelligent Systems (IEEE COINS 2023). Congratulations Atah!!!
  • Book, The proceeding book of DataCom 2022 has been published by Springer. Find out more Big Data Intelligence and Computing.
  • Sponsorship, The Analytics Everywhere Lab at UNB received 10 TinyML development kits sponsored by Edge Impulse.

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Research & Development

At Analytics Everywhere Lab, we believe that Artificial General Intelligence might be only achieved through the ensemble of different task-specific AI agents in which an interconnected and self-governing network of AI solution agents collaborate to solve the growing complex problems. Keeping this in mind, we envision that the available Analytics Everywhere (AE) can incorporate an additional pillar for learning capability to achieve Learning and Intelligence Everywhere. Our research portfolio bridges the area of Cyber-Physical Systems (CPS), Internet of Things (IoT), Machine learning, Data Science, Embedded AI, Edge Computing, Cloud Computing, Context-enriched Analytics, Decision Intelligence, Explainable AI (XAI), and TinyML . Our research outcomes are being applied to solve numerous practical challenges. Specifically, we categorized our outcome into 4 main application groups.


Social Good

Promoting the advances of Analytics Everwhere Framework in performing automated analytical tasks capable of providing higher-level intelligence from continuous IoT/CPS data streams and generating long-term insights from accumulated IoT/CPS data streams. Exploring the possibility to adapt our framework in building prototype to support multiple Social Good use cases.

Smart City

Developing and evaluating scalable and adaptive Data Analytics Platforms for the IoT/CPS. Proposing unique solution to capture, manage, process, analyze, and visualize data streams through streaming descriptive, diagnostic, and predictive analytics. Incooprating human-in-the-loop, facilitating the interaction between human and future IoT/CPS platform to deploy in different Smart City scenarios.

IoT/CPS for combatting Climate Change

Empowering current efforts to combat climate change by leveraging the most advanced AI/ML techniques to analyze the large scale of these spatial-temporal data. Solving technical challenges in connectivity, communications and protocols of IoT/CPS platforms and applying new technologies in software, analytics, architecture configurations and platform infrastructure to address Climate Change issues.

Industry 4.0

Investigating and analyzing of Industrial IoT/CPS Ecosystems. Explaining the unknown and visualizing the decision making process by post-hoc analysis and build-in realtime detection mechanisms in our AE Framework. Understanding the tight relationship between the complexity of the algorithms and the data life-cycle to support the evolution of Industry 4.0.

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Our work is funded by
Contact us

Analytics Everywhere Lab

University of New Brunswick
Computer Science Faculty
Room GC107,
550 Windsor St, Fredericton,
Canada, E3B 5A3

Email: hcao3[at]unb[dot]ca

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