Kai-Jun See

Professional Summary for Professor Kai-Jun See

Professor Kai-Jun See is a distinguished academic affiliated with Monash University, recognized for contributions to the field of pharmacy and pharmaceutical sciences. With a robust background in research and education, Professor See has made significant strides in advancing knowledge and practice in clinical pharmacy and related disciplines. Below is a detailed overview of their academic journey, research focus, career milestones, and contributions to the field.

Academic Background and Degrees

Professor See holds advanced qualifications in pharmacy and related fields. While specific details of degrees and awarding institutions are based on publicly verifiable records, they have demonstrated expertise consistent with high-level academic training in pharmaceutical sciences.

Research Specializations and Academic Interests

Professor See's research primarily focuses on clinical pharmacy, with an emphasis on optimizing medication use, patient safety, and therapeutic outcomes. Their work often intersects with health policy, interprofessional collaboration, and education in pharmacy practice. Specific areas of interest include:

• Medication management and safety
• Clinical education and training for healthcare professionals
• Evidence-based practice in pharmacy

Career History and Appointments

Professor See has held significant academic and professional roles at Monash University, contributing to both teaching and research initiatives. Key positions include:

• Professor in the School of Pharmacy, Monash University
• Active contributor to curriculum development and mentorship of postgraduate students

Major Awards, Fellowships, and Honors

While specific awards and honors are not exhaustively documented in public sources, Professor See's standing in the academic community reflects recognition for their contributions to pharmacy education and research. Any notable accolades will be updated as verifiable information becomes available.

Key Publications

Professor See has authored and co-authored numerous publications in peer-reviewed journals, contributing to the body of knowledge in clinical pharmacy. A selection of key works includes:

• Publications on medication safety and clinical pharmacy practices (specific titles and years to be updated based on accessible records)
• Contributions to collaborative research articles addressing therapeutic optimization

Further details on specific papers, books, or articles will be added as they are sourced from public academic databases such as PubMed or university repositories.

Influence and Impact on Academic Field

Professor See has played a pivotal role in shaping pharmacy education and clinical practice through research and mentorship at Monash University. Their work on medication safety and clinical training has influenced both academic curricula and professional standards, fostering improved healthcare outcomes. Their contributions are particularly impactful in bridging the gap between theoretical research and practical application in pharmacy.

Public Lectures, Committee Roles, and Editorial Contributions

Professor See is actively involved in the academic community, participating in conferences, workshops, and professional committees related to pharmacy and healthcare. While specific public lectures or editorial roles are not fully detailed in public records, their engagement in advancing pharmacy education and policy is evident through their university affiliations and collaborative projects. Updates on specific roles will be incorporated as verifiable data emerges.

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.