Ye Tao

I am a researcher and data scientist passionate about bridging the gap between cutting-edge AI systems and real-world usability. My work centers on designing AI frameworks that not only achieve high performance but also provide human-understandable explanations

mizumo1988@gmail.com

I am a PhD in Artificial Intelligence from Griffith University, specializing in machine learning, deep reinforcement learning (DRL), and conversational recommender systems. My research focuses on enhancing AI interpretability and performance through knowledge graphs, pre-trained language models (LLMs), and ensemble learning techniques. With industry experience as a Data Scientist at RedX Technology and multiple research roles, I bridge cutting-edge AI advancements with real-world applications.

Education

  • Doctor of Philosophy (PhD) in Artificial Intelligence
    Griffith University (2019–2024)
  • Master of Computer Science
    University of Electronic Science and Technology of China (UESTC)
  • Bachelor of Computer Science
    University of Electronic Science and Technology of China (UESTC)

Professional Experience

  • Data Scientist @ RedX Technology (Australia)
    2023 - present
    Developed advanced AI-driven battery energy optimization platform for cost saving.
  • Research Assistant @ Griffith University
  • Deep Learning for Recommendation Systems (2022–2024)
  • Deep Reinforcement Learning for IEEE 802.11 Rate Adaptation (2022–2023)
  • Tutor (Data Mining & Python Programming) @ Griffith University (2020–2024)

Publications

Item trend learning for sequential recommendation system using gated graph neural network

Y Tao, C Wang, L Yao, W Li, Y Yu

Neural Computing and Applications, 2023

Knowledge graphs and pretrained language models enhanced representation learning for conversational recommender systems

Z Qiu, Y Tao, S Pan, AWC Liew

IEEE Transactions on Neural Networks and Learning Systems, 2024

TRec: Sequential recommender based on latent item trend information

Y Tao, C Wang, L Yao, W Li, Y Yu

2020 International Joint Conference on Neural Networks (IJCNN), 2020

A Reinforcement Learning Approach to Wi-Fi Rate Adaptation Using the REINFORCE Algorithm

Y Tao, WL Tan

2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024

Dynamic weighted ensemble learning for sequential recommendation systems: The AIRE model

Y Tao, C Wang, AWC Liew

Future Generation Computer Systems 149, 2023

Exploring New Frontiers in Modern Recommendation Systems: GNN-based Models, Ensemble Approaches, and Conversational Interfaces

Y Tao

Griffith University, 2023

Enhancing recommender ensemble by estimating input fitness

Y Tao, C Wang, AWC Liew, S Binnewies

Computers and Electrical Engineering 104