Core Faculty Members

Prof. Sadia Shakil
Research Assistant Professor, Department of Biomedical Engineering, CUHK

sadiashakil@cuhk.edu.hk
Tel: 3943 5591
Location: SHB112E

Lab webpage:
http://www.bme.cuhk.edu.hk/kytong/researchteam/
https://www.sadia-shakil.com/

Dr. Shakil joined the Department of Biomedical Engineering at the Chinese University of Hong Kong (CUHK) as Research Assistant Professor in 2023. Before joining CUHK, she worked as a Senior Researcher with Cognitive and Neural Engineering (CANE) group at the Brno University of Technology, Czech Republic. Dr. Shakil also she worked as an Assistant Professor of Electrical Engineering at the Institute of Space Technology (IST), Pakistan for six years. At IST, she was also the founding director of 'Biosingal Processing and Computational Neuroscience (BiCoNeS)’ lab at IST under which she established significant national and international academic and research collaborations. Dr. Shakil was also an Adjunct Research Associate for two years with the Turner Institute of Brain and Mental Health at Monash University, Australia.

Dr. Shakil has two master's degrees in Electronics and Computer Engineering from Pakistan and one in Electrical and Computer Engineering from USA. She did her PhD in Electrical and Computer Engineering from Georgia Institute of Technology, USA after securing Fulbright Scholarship award. Dr. Shakil did her postdoc from Rotman Research Institute, Baycrest Health Sciences, Canada after securing Schlumberger Faculty for the Future Fellowship award. Her primary research areas are computational neuroscience, neural engineering, AI, and bio-signal processing with a focus on their real-work applications in healthcare. Her research is highly inter- and multi-disciplinary encompassing various domains such as engineering, neuroscience, psychology, mathematics, statistics, multimedia, and computer science.

Dr. Shakil has more than two decades of experience in teaching and research internationally. She has taught at undergrad and grad levels and has successfully supervised more than 40 theses while working in Pakistan, Czech Republic, and Hong Kong. Furthermore, her research collaborations include industrial and academic partners from USA, Canada, Australia, New Zealand, Pakistan, Saudi Arabia, UAE, China, and Hong Kong. She delivers invited talks internationally and is working on projects for cross-cultural data acquisition from patients’ population.

2018-2019 Postdoctoral Fellow, Rotman Research Institute, Baycrest Health Sciences, Canada
2011-2016 PhD, Electrical and Computer Engineering, Georgia Institute of Technology, USA
2011-2015 MS, Electrical and Computer Engineering, Georgia Institute of Technology, USA
2007-2009 MS, Computer Engineering, University of Engineering and Technology, Pakistan

· Brain functional and structural dynamics
· Brain and behavior association under naturalistic stimulation (videos & music)

· 2011 Fulbright PhD scholarship
· 2017 Schlumberger Faculty for the Future Postdoctoral fellowship

Biomedical Imaging

1. Abbasi, S. A., Mei, D., Wei, Y., Xu, C., Abbasi, S. M. T., Shakil, S., & Yuan, W. (2025). Deconvolution Techniques in Optical Coherence Tomography: Advancements, Challenges, and Future Prospects. Laser & Photonics Reviews, 2401394 Ren, Z, Zhou, M., Shakil, S.*, & Tong, R. K. Y (2025). Alzheimer's Disease Recognition via Long-range State Space Model Using Multi-modal Brain Images. Frontiers in Neuroscience, 19, 1576931

2. Wu, X., Xu, Z., Lu, D., Sun, J., Liu, H., Shakil S., Ma, J., Zheng, Y., Tong, K.Y.R.,” Conservative Radical Complementary Learning for Class-incremental Medical Image Analysis with Pre-trained Foundation Models”, MICCAI 2025

3. Chen, X., Yao, W., Li, Y., Liang, D., Zheng, H., Shakil, S ... & Sun, T. (2024). “IG-GCN: Empowering e-Health Services for Alzheimer’s Disease Prediction”. IEEE Transactions on Consumer Electronics.

4. Nazir, M., , S.*, & Khurshid, K. (2024). “End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images”. Journal of Imaging Informatics in Medicine, 1-24.

5. Hafeez, M. A., & Shakil, S. (2024). “EEG-based stress identification and classification using deep learning”. Multimedia Tools and Applications, 83(14), 42703-42719.

6. Shaikh, U. Q., Shahzaib, M., Shakil, S.*, Bhatti, F. A., & Saeed, M. A. (2023). Robust and adaptive terrain classification and gait event detection system. Heliyon, 9(11).

7. Mahrukh, R., Shakil, S.*, & Malik, A. S. (2023). “Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm”, Scientific Reports, 13(1), 7267.

8. Nazir, M., Ali, M. J., Tufail, H. Z., Shahid, A. R., Raza, B., Shakil, S., & Khurshid, K. (2022). “Multi task learning architecture for brain tumor detection and segmentation in MRI images”. Journal of Electronic Imaging, 31(5), 051606-051606.

9. Nazir, M., Shakil, S., & Khurshid, K. (2021). “Role of deep learning in brain tumor detection and classification (2015 to 2020): A review”. Computerized medical imaging and graphics, 91, 101940.

10. Adamson, M. M., Shakil, S., Sultana, T., Hasan, M. A., Mubarak, F., Enam, S. A., ... & Razi, A. (2020). “Brain injury and dementia in Pakistan: current perspectives”. Frontiers in neurology, 11, 299.

11. Shakil, S., Billings, J. C., Keilholz, S. D., & Lee, C. H. (2017). “Parametric dependencies of sliding window correlation”. IEEE Transactions on Biomedical Engineering, 65(2), 254-263.

12. Billings, J. C., Medda, A., Shakil, S., Shen, X., Kashyap, A., Chen, S., ... & Keilholz, S. D. (2017). “Instantaneous brain dynamics mapped to a continuous state space”. Neuroimage, 162, 344-352.

13. Keilholz, S. D., Pan, W. J., Billings, J., Nezafati, M., & Shakil, S.(2017). “Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies”. Neuroimage, 154, 267-281

14. Shakil, S., Lee, C. H., & Keilholz, S. D. (2016). “Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states”. Neuroimage, 133, 111-128.