Chee-Ming Ting

Professional Summary: Professor Chee-Ming Ting

Professor Chee-Ming Ting is a distinguished academic affiliated with Monash University, recognized for his contributions to the fields of signal processing, machine learning, and biomedical engineering. With a robust academic background and a commitment to advancing research in data science and health informatics, he has made significant strides in both theoretical and applied research domains.

Academic Background and Degrees

Professor Ting holds advanced degrees in engineering and related disciplines, reflecting his deep expertise in technical and computational fields. While specific details of his degrees and awarding institutions are based on publicly available records, he is known to have completed rigorous academic training that underpins his research career at Monash University.

Research Specializations and Academic Interests

Professor Ting's research primarily focuses on:

• Signal processing and time-series analysis
• Machine learning and data analytics
• Biomedical engineering, with applications in health monitoring and diagnostics
• Statistical modeling for brain and physiological data

His work often bridges theoretical advancements with practical applications, contributing to innovations in healthcare technology and data-driven decision-making.

Career History and Appointments

Professor Ting has held significant academic positions, with his current role at Monash University marking a key phase in his career. His professional journey includes:

• Current position as a faculty member in the School of Information Technology at Monash University, where he contributes to both teaching and research.
• Previous academic and research roles that have shaped his expertise in computational methods and interdisciplinary collaboration.

Major Awards, Fellowships, and Honors

While specific awards and honors are not exhaustively documented in public sources, Professor Ting's standing in the academic community and his contributions to high-impact research suggest recognition within his field. Any notable accolades would likely pertain to his advancements in signal processing and biomedical applications.

Key Publications

Professor Ting has authored and co-authored numerous peer-reviewed papers in reputable journals and conferences. Some of his notable works include:

• Ting, C.-M., et al. (2015). 'Estimating Effective Connectivity from fMRI Data Using Time-Varying Vector Autoregressive Models.' Brain Connectivity.
• Ting, C.-M., et al. (2019). 'Short-Segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks.' Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
• Ting, C.-M., et al. (2021). 'Modeling and Estimation of Dynamic Effective Connectivity in Brain Networks.' NeuroImage.

These publications reflect his focus on innovative methodologies for analyzing complex data in biomedical contexts.

Influence and Impact on Academic Field

Professor Ting's research has contributed to advancements in understanding brain connectivity and developing machine learning tools for health diagnostics. His work is frequently cited in studies related to signal processing and biomedical engineering, indicating a measurable impact on both academic research and practical applications in healthcare technology. His interdisciplinary approach fosters collaboration across engineering, data science, and medical fields.

Public Lectures, Committee Roles, and Editorial Contributions

While specific details of public lectures or editorial roles are not widely documented in accessible sources, Professor Ting is likely involved in academic committees and peer-review processes given his expertise and standing at Monash University. He may also participate in conferences and workshops as a speaker or organizer, contributing to the dissemination of knowledge in his areas of specialization.