Ye Tao

Hi, I’m Ye Tao, a third year Ph.D. candidate studying at School of Information and Computer technology, Griffith University. My research interest is mainly on deep learning in modern recommendation system and natural language processing in recommendation system.


mizumo1988@gmail.com

Education

  • Griffith University 2019-2024
    Doctor of Philosophy, Artificial Intelligence
  • University of Electronic Science and Technology of China
    Master of Computer Science
  • University of Electronic Science and Technology of China
    Bachelor of Computer Science

Research Experience

  • Research assistant - Deep Learning on Recommendation System
    2022-2024
  • Research assistant - Deep Reinforcement Learning on IEEE802.11 rate adaption
    2022-2023

Teaching Experience

  • Data Mining Tutor, School of ICT 2020-2024
  • Python Programming Tutor, School of ICT 2022-2024

Award

  • Jeffrey Blee Memorial Prize - 2021

Publication

  • Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems, IEEE Transactions on Neural Networks and Learning Systems (CORE Rank A*, Q1, Impact Factor 14.2), 2024
  • Dynamic Weighted Ensemble Learning for Sequential Recommendation Systems: The AIRE Model, Future Generation Computer System (CORE Rank A, Q1, Impact Factor 7.3), 2023
  • Enhancing Ensembling Recommendation System In User-agnostic Scenarios, Computer and Electrical Engineering (Q1), 2022
  • TRec: Sequential recommender based on latent item trend information, 2020 International Joint Conference on Neural Networks (CORE Rank A)
  • Item trend learning for sequential recommendation system using gated graph neural network, Neural Computing and Applications (CORE Rank B, Q1), 2021
  • A Reinforcement Learning Approach to Wi-Fi Rate Adaptation Using the REINFORCE Algorithm, IEEE Wireless Communications and Networking Conference (CORE Rank B), 2024

Skills

  • Strong background in machine learning and deep learning, with expertise in developing and implementing recommendation algorithms using natural language processing techniques.
  • Proficient in programming languages such as Python and C#.
  • Experience using PyTorch and PyTorch Lightning for deep learning tasks.
  • Strong problem-solving and analytical skills.
  • Excellent communication and teamwork abilities, demonstrated through successful mentoring and tutoring of students in data mining and Python programming courses.
  • Experience with MLOps tools such as MLflow.