
Makes complex ideas simple and clear.
Always positive and enthusiastic in class.
Inspires students to love learning.
Brings enthusiasm to every interaction.
Always patient and willing to help.
Kai Jun See is an Assistant Lecturer in the School of Information Technology at Monash University Malaysia, which falls under the Faculty of Information Technology. He completed his Doctorate by Research at the School of Information Technology, Monash University Malaysia in 2024. Earlier, he earned a Master of Data Science from Universiti Malaya from 2017 to 2019 and a Bachelor of Science with Honors in Statistics from Universiti Kebangsaan Malaysia from 2013 to 2016. Before joining Monash University Malaysia in 2022 for his doctoral studies, See worked for over five years as a data professional in the fintech, automation, media, and banking industries.
His research centers on deep multiplex modeling of human brain networks, with expertise in deep learning, statistical modeling, network science, time series, and connectomics, contributing to Sustainable Development Goal 3: Good Health and Well-being. Supervised by Associate Professor Chee-Ming Ting, Dr. Fuad Noman, and Professor Raphaël C.-W. Phan, his work advances understanding in these areas. See teaches FIT1043 Introduction to Data Science and FIT3152 Data Analytics. A key publication is 'Deep Multi-Graph Embedded Clustering for Community Detection in fMRI Functional Brain Networks Across Individuals' (2024), co-authored with C.-M. Ting, F. Noman, J. Y. Loo, Y. F. Tan, H. Ombao, and R. C.-W. Phan, published in the proceedings of the 2024 IEEE International Conference on Image Processing (ICIP), pages 2996-3002.