Zhou, Xiuze (周秀泽)
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PhD Student, |
About me
I received the B.S. degree from the Zhejiang University of Science and Technology, in 2012, and the M.S. degree from Xiamen University, in 2016.
From 2019 to 2024, I served as a senior research scientist at the AI Research Institute, Hithink RoyalFlush, China.
I am currently pursuing a PhD at The Hong Kong University of Science and Technology (Guangzhou), China, and serving as a research fellow at the School of Smart Education, Jiangsu Normal University, China.
As a dedicated professional in the field of AI, I am committed to leveraging advanced technologies to address real-world challenges. My research has led to significant contributions in various publications, showcasing my commitment to advancing AI across practical applications such as recommendation systems, healthcare, and Battery Management Systems (BMS).
Research
Research interests
Artificial Intelligence (AI)
Machine Learning
Large Language Model
Recommendation Systems
Working papers
Align Large Language Models with Cross Attention Transformer for Explainable Recommendation
Large Language Models for Explainable Recommendation
Battery-GPT: A Large Language Model Predicting the State of Health and Remaining Useful Life of Lithium-ion Batteries
MambaRUL: State Space Models for Remaining Useful Life Prediction of Lithium-ion Battery
Under review
X. Zhou*, "Visualizing Transformer Collaborative Filtering".
K. Li, X. Zhou*, "Layer-wise Contrastive Learning BERT for Sentence Representation".
C. Zang, K.W. See, B. Arshad, Y. Sun, Z. Lu, G. Xu, S. Dai, Y. Wang, X. Zhou, and Y. Niu, "Machine Learning Approaches for Short-term and Long-term Prediction of Strata Pressure for Coal Mining Applications".
Recent publications
D. Chen, X. Zhou*, "AttMoE: Attention with Mixture of Experts for Remaining Useful Life Prediction of Lithium-Ion Batteries", Journal of Energy Storage, Apr. 2024. (IF = 9.4) [pdf][code]
Y. Lin, W. Zhang, F. Lin*, W. Zeng, X. Zhou*, and P. Wu, "Knowledge-aware Reasoning with Self-supervised Reinforcement Learning for Explainable Recommendation in MOOCs", Neural Computing and Applications, Dec. 2023. (IF = 6.0)[pdf]
Y. Ding, S. Jia, T. Ma*, B. Mao, X. Zhou, L. Liu, and D. Han, "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction", Workshop of IJCAI2023, 2023. [arxiv.org].
W. Zhang, Y. Lin, Y. Liu, P. Wu, F. Lin*, and X. Zhou*, "Self-Supervised Reinforcement Learning with Dual-reward for Knowledge-aware Recommendation", Applied Soft Computing, Oct. 2022. (IF = 8.263) [pdf][code]
X. Gu, K See, X. Zhou, Y. Wang, C. Zang, "Recent Advances in Data Preprocessing and Machine Learning Approaches for Battery's State of Charge and State of Health Estimation: A Review", 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2023. pp. 421-426
M. Chen, T. Ma, and X. Zhou*, "CoCNN: Co-occurrence CNN for Recommendation", Expert Systems with Applications, Jun. 2022, 195, pp. 116595. (IF = 8.665) [pdf][code]
M. Chen, Y. Li, X. Zhou*, "CoNet: Co-occurrence Neural Networks for Recommendation", Future Generation Computer Systems, Nov. 2021, 124, pp. 308-314. (IF = 7.307) [pdf][code]
M. Chen, X. Zhou*, "DeepRank: Learning to Rank with Neural Networks for Recommendation", Knowledge-Based Systems, Dec. 2020, 209, pp. 106478. (IF = 8.139) [pdf][code]
D. Chen, W. Hong, and X. Zhou*, "Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries", IEEE Access, 2022, 10, pp. 19621-19628. (IF = 3.367) [pdf][code]
X. Wu, W. Zeng, F. Lin*, and X. Zhou, "NeuRank: Learning to Ranking with Neural Networks for Drug-Target Interaction Prediction", BMC Bioinformatics, Nov. 2021, 22, pp. 567. (IF = 3.328) [pdf][code]
X. Zhou* and S. Wu, "Rating LDA Model for Collaborative Filtering", Knowledge-Based Systems, Oct. 2016, 110, pp. 135-143. (IF = 8.139) [pdf]
K. Li, X. Zhou, F. Lin*, W. Zeng, B. Wang, and G. Alterovitz, "Sparse Online Collaborative Filtering with Dynamic Regularization", Information Sciences, Dec. 2019, 505, pp. 535-548. (IF = 8.233) [pdf]
X. Zhou, W. Shu, F. Lin*, and B. Wang, "Confidence-Weighted Bias Model for Online Collaborative Filtering", Applied Soft Computing, Sep. 2018, 70, pp. 1042-1053. (IF = 8.263)
K. Li, X. Zhou, F. Lin*, W. Zeng, and G. Alterovitz, "Deep Probabilistic Matrix Factorization Framework for Online Collaborative Filtering", IEEE Access, Mar. 2019, 7, pp. 56117-56128. (IF = 3.367)
F. Lin, X. Zhou, and W. Zeng*, "Sparse Online Learning for Collaborative Filtering", International Journal of Computers Communications & Control, Apr. 2016, 11 (2), pp. 248-258. (IF = 2.093)
S. Lu, H. Chen, X. Zhou, B. Wang, H. Wang*, and Q. Hong, "Graph-Based Collaborative Filtering with MLP", Mathematical Problems in Engineering, Dec. 2018, 2018, pp. 1-10. (IF = 1.305)
X. Zhou, F. Lin*, L. Yang, J. Nie, Q. Tan, W. Zeng, and N. Zhang, "Load Balancing Prediction Method of Cloud Storage based on Analytic Hierarchy Process and Hybrid Hierarchical Genetic Algorithm", SpringerPlus, Nov. 2016, 5 (1), pp. 1989-2012. (IF = 1.780)
X. Zhou*, and S. Wu, "The Biterm Author Topic in the Sentences Model for E-Mail Analysis", IEICE Transactions on Information and Systems, Aug. 2017, E100.D (8), pp. 1852-1859. (IF = 0.449)
Note: * indicates the corresponding author.
Full list of publications in Google Scholar.
Academic service
Reviewer
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Industrial Informatics
ACM Transactions on Knowledge Discovery from Data
IEEE Access
Projects
Large Language Models-based Code Generation and Review, 03.2023-Present
Collect and process a large corpus of code snippets, programming tutorials, and relevant documentation
Pre-train LLMs and accelerate training and inference of the large model on multi-machine multi-GPUs
Iterate on the code generation and review process
Stable Diffusion-based AI Painting, 01.2022-12.2022
Explore and design prompt
Accelerate training and inference for diffusion process
Deploy trained models in the appropriate environment
Advertising Platform Development, 01.2021-12.2021
Provide advertising strategies and solutions for advertisers to maximize revenue
Provide automated advertising instead of manual selection
Use users' history information to build their profiles, and then select the target users
Campus Recommender System, 03.2021-12.2021
Built user profiles based on the data crawled from websites
Recommended information, such as courses from MOOC, and publications from Arxiv, to students
Recommended information from within and outside the university based on faculty research, courses taught, and interests
Online Education Explainable Recommender System, NSFC, 06.2018-12.2018
Summarized over 500,000 exercises and classified their knowledge points from all subjects
Applied matrix factorization for online learning and recommendation of exercises based on interaction of users
Added latent features learned by neural networks from exercises to online matrix factorization for better performance
Education
M.E., Pattern Recognition and Intelligent Systems, Xiamen University, 06.2016
Awards: Principal Level Scholarship (1st in admission)
Main Courses: Machine Learning, Design of Neural Networks, Digital Image Processing, Time Series Analysis, Pattern Recognition, Data Mining and Its Application, Artificial Intelligent: Theory and Application, Recommender System.
B.E., Automation, Zhejiang University of Science and Technology, 06.2012
Main Courses: C Programming, Embedded Systems, Computer Network and Communication, Computer Control System.
Work experience
Researcher, School of Smart Education, Jiangsu Normal University, 03.2023-Present
Instructed graduate students in scientific research
Make regular presentations and exchanges
Research Scientist, AI Research Institute, Hithink RoyalFlush, 06.2019-Present
Research the newest machine learning algorithms and recommender system technology on stocks and hot news
Apply neural network models to drug-target interaction prediction and evaluate the performance
Publish papers and apply for relevant patents for the corporation
Give lessons on Artificial Intelligence and Recommender Systems to the staff
Research Assistant, Big Data Lab, Xiamen University, 09.2016-02.2019
Instructed two undergraduate and three graduate students in scientific research
Tracked, studied, reproduced, and improved up-to-date machine learning methods
Published papers on machine learning and recommender systems
Software Engineer, Dragon SOFT, 07.2013-06.2014
Developed an electronic system of shooting game for an amusement park
Recorded the track of users' behavior from sensors in a database
Built a model analyzing users’ behavior concerning speed, acceleration and number of cylinders
Assistant Engineer, Gold Electronic, 03.2012-07.2012
Cooperated with motor companies, such as Zotye and BYD, on battery management system development
Developed a testing and analytics platform for performance of a lithium battery with C# (real-time data)
Used CAN bus to collect working data of batteries and analyzed the data for balance power