Laboratory of Dynamic Embodied Brain
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HASSAN Zohaib
  • Department：
  • Position：Postdoctoral Researcher
  • Research Field：Data analysis, Machine learning, Electrophysiology, fMRI
  • Phone：
  • E-mail：zohaib.hassan@icpbr.ac.cn
Biography

Zohaib’s research is situated at the intersection of machine learning, data analysis, and neuroscience. He earned his Ph.D. in Control Science and Engineering under the supervision of Prof. Hu Lisheng at Shanghai Jiao Tong University, China. His doctoral research focused on manifold learning techniques for feature extraction and geometry recovery, with an emphasis on fault detection. Following his Ph.D., Zohaib pursued postdoctoral research at the Research Center for Frontier Fundamental Studies of Zhejiang Lab in Hangzhou, China, under the mentorship of Prof. Dongping Yang, where he applied machine learning methodologies to the study of sleep dynamics and epilepsy. Currently, as a member of the Dynamic Embodied Brain Laboratory, he is engaged in the advanced analysis of multidimensional neural and bodily activity data.


Research Interests

Data analysis, Machine learning, Electrophysiology, fMRI.


Selected Publications
1.Liu, T., Shah, M. Z. H., Yan, X., & Yang, D. (2023). Unsupervised feature representation based on deep boltzmann machine for seizure detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1624-1634.
2.Shah, M. Z. H., Liu, T., Wei, Y., & Yang, D. (2023). Unsupervised Feature Representation of Sleep EEG Data with Transient Deep Boltzmann Machine. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1-5.
3.Shah, M. Z.H., Ahmed, Z., & Hu, L. (2023). Feature extraction and fault detection scheme via improved locality preserving projection and SVDD. Transactions of the Institute of Measurement and Control, 45(2), 197-211.
4.Shah, M. Z. H., Ahmed, Z., & Hu, L. (2022). Weighted linear local tangent space alignment via geometrically inspired weighted PCA for fault detection. IEEE Transactions on Industrial Informatics, 19(1), 210-219.
5.Shah, M. Z. H., Hu, L., & Ahmed, Z. (2022). Modified LPP based on Riemannian metric for feature extraction and fault detection. Measurement, 193, 110923.