cv

This is a description of the page.

Basics

Name Quanwei (Leo) Liu
Label PhD student
Email quanwei.liu@my.jcu.edu.au
Url https://quanweiliu.github.io/
Summary A optimistic boy

Volunteer

  • Henan, China

    Member
    Volunteer association of Henan University of Engineering
    Going to Zhengzhou University of Technology to play basketball with children with disabilities.
    • The children are very happy to chat with us.
  • Henan, China

    Member
    Track and field team of Resources and Environment College of Henan University of Engineering
    Chatting with the elderly at the Longhu Home for the Elderly and celebrating their birthdays.
    • The elders are very happy to chat with us.

Education

  • 2023 - 2027

    Cairns, Australia

    PhD student
    James Cook University
    College of Science and Engineering
    • Engineering and Related Technologies
  • 2020.09 - 2023.06

    Wuhan, China

    Master student
    China University of Geosciences, Wuhan, China
    School of Geophysics and Geomatics
    • Resources and Environment
  • 2016.09 - 2020.06

    Henan, China

    Bachelor student
    Henan University of Engineering
    College of Resources and Environment
    • Exploration Technology and Engineering

Awards

Certificates

Publications

Skills

Remote sensing image processing
Machine learning
Deep learning
Image fusion
Classification
Segmentation

Languages

Chinese (Mandarin)
Native speaker
English
Fluent

Interests

Deep learning
CNN
Transformer

References

Professor Yanni Dong
My Master primary supervisor.
Professor Tao (Kevin) Huang
My PhD primary supervisor.

Projects

  • 2023 - Present
    Low-cost Sensing Methods and Hybrid Learning Models
    This project aims to revolutionize the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine-learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.
    • Australian Research Council Discovery Project