Chee-Ming Ting

Assoc Professor

Accepting PhD Students

PhD projects

I am looking for a motivated full-time PhD student in Computer Science based
at the School of Information Technology, Monash University Malaysia.

Research Topics
Deep learning, statistical modeling, computational neuroimaging, computed-aided detection and prediction, network neuroscience

Requirements
(1) A Master degree or Bachelor degree (1st class honours with research
experience) with computer science, statistics, electronic and computer engineering related background
(2) A good publication records and experience in machine learning and Python, R and/or Matlab programming will be an advantage
(3) Proficiency in English

Deadline: No deadline, open until the position is filled

Interested candidates can email your CV, academic transcripts, publication list
to Assoc. Prof Dr Ting Chee Ming ([email protected]).

20052024

Research activity per year

Personal profile

Biography

Dr Chee-Ming Ting is Associate Professor in the School of Information Technology, Monash University Malaysia, specializing in machine learning and data science. He was a Senior Lecturer with the School of Biomedical Engineering and Health Sciences, University Teknologi Malaysia from 2014 to 2020, and a Research Scientist with the Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology from 2017 to 2018. He was an Honorary Senior Research Fellow at the Division of Psychology and Language Sciences, University College London from 2020 to 2023.

Dr Ting has published over 26 journal papers, 43 refereed conference papers and 6 book chapters in signal processing, medical imaging, biomedical informatics and biomedical engineering. He has served as principal investigator (PI) and co-PI for research grants from university and goverment of over RM2.5 millions in total. He has graduated 4 PhD and 5 Masters students, and is now supervising 10 PhD Masters students. His research interests include signal & image processing, deep neural networks, computational statistics and statistical models for networks with applications to neuroimaging and other biomedical data for automatic prediction of diseases and patient monitoring.

Dr Ting received Research Excellence Award from the IEEE Signal Processing Society Malaysia for the Best IEEE Journal Paper in 2019 and 2022, respectively. He also won several national and international innovations awards. He has served as referee for over 30 journals and IEEE flagship conferences, and as member in technical and organizing committees for international conferences.

Research interests

  • Biomedical signal and image analysis
  • Deep learning
  • Computational statistics
  • Spatio-temporal modeling
  • Network science
  • Neuroimaging (fMRI & EEG)
  • Brain connectivity analysis
  • Computer-aided detection

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Education/Academic qualification

Mathematics - Statistics, Doctor of Philosophy

Electrical Engineering, Master of Engineering

Electrical & Electronics Engineering, Bachelor of Engineering (Hons.)

External positions

Honorary Senior Research Fellow, University College London

12 Jul 202012 Jul 2023

Research Scientist, King Abdullah University of Science and Technology

1 Jul 201731 Aug 2018

Senior Lecturer, Universiti Teknologi Malaysia (University of Technology Malaysia)

1 Jul 201431 Aug 2020

Research area keywords

  • Biomedical signal and image analysis
  • Deep learning
  • Computational statistics
  • Spatio-temporal modeling
  • Network science
  • Neuroimaging (fMRI & EEG)
  • Brain connectivity analysis
  • Computer-aided detection

Collaborations and top research areas from the last five years

Recent external collaboration on country/territory level. Dive into details by clicking on the dots or