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/

Sadia joined the Department of Biomedical Engineering, The Chinese University of Hong Kong (CUHK) as Research Assistant Professor in 2023. Before joining CUHK, she was working as a Senior Researcher with Cognitive and Neural Engineering (CANE) group at the Brno University of Technology, Czech Republic. Before that she worked as an Assistant Professor of Electrical Engineering at the Institute of Space Technology (IST), Pakistan for six years. At IST she successfully supervised one PhD thesis, four MS theses, and more than thirty undergraduate final year projects. She was also the founding director of 'Biosingal Processing and Computational Neuroscience (BiCoNeS)’ lab at IST. Sadia was also an Adjunct Research Associate for two years with the Turner Institute of Brain and Mental Health, Monash University, Australia.

Sadia has two master's degrees from Pakistan and one from USA. She did her PhD in Electrical and Computer Engineering from Georgia Institute of Technology, USA after securing Fulbright scholarship. Sadia did her postdoc from Rotman Research Institute, Baycrest Health Sciences, Canada after securing Schlumberger Faculty for the future fellowship. Her primary research areas are bio-signal processing and computational neuroscience with a focus on real-work applications. Her research is interdisciplinary encompassing various domains such as engineering, neuroscience, psychology, multimedia and computer science.

2022-2023 Senior Researcher, Brno University of Technology, Czech Republic
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

1. Hafeez, M.A., Shakil, S. EEG-based stress identification and classification using deep learning. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17111-0

2. Mahrukh, R., Shakil, S. & Malik, A.S. Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm. Sci Rep 13, 7267 (2023). https://doi.org/10.1038/s41598-023-33734-7

3. Nazir, M., Ali, M. J., Tufail, H. Z., Shahid, A. R., Raza, B., Shakil, S., & Khurshidb, K. (2022). Multi-task learning architecture for brain tumor detection and segmentation in MRI images. Journal of Electronic Imaging, 51606(1).

4. Shahzaib, M., Shakil, S., Ghuffar, S., Maqsood, M., & Bhatti, F. A. (2021). Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations. Computer Methods in Biomechanics and Biomedical Engineering, 24(9), 945-955.

5. 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.

6. 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.

7. 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.

8. 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.

9. Shakil, S., Keilholz, S. D., & Lee, C. H. (2015, November). On frequency dependencies of sliding window correlation. In 2015 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 363-368). IEEE