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/
About
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.
Professional Education and Training
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
Research Interest
· Brain functional and structural dynamics
· Brain and behavior association under naturalistic stimulation (videos & music)
Honors & Awards
· 2011 Fulbright PhD scholarship
· 2017 Schlumberger Faculty for the Future Postdoctoral fellowship
Course Teaching in CUHK
Biomedical Imaging
Selected Publications
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.