Wearable and epidermal bioelectronics
Conformal and drawn-on-skin electrodes for unobtrusive, high-quality electrophysiology in real-world environments.
Muhammad Zubair — Research Website
Muhammad Zubair is a PhD candidate in the Department of Biomedical Engineering at The Pennsylvania State University and a visiting PhD student at the University of Illinois Urbana-Champaign, advised by Dr. Cunjiang Yu. I will join the Department of Computer Science at The University of Texas at Austin as a Postdoctoral Fellow in January 2027.
His research lies at the intersection of wearable bioelectronics, neural interfaces, biomedical signal processing, and artificial intelligence. He uses conformal physiological sensors and integrates them with signal-processing and AI models to translate electrophysiological signals into reliable information for human–machine interfaces and health monitoring.
His recent work spans epidermal sensing, ambulatory stress monitoring, at-home sleep monitoring, drowsiness detection, physiological signal decoding, and intelligent assistive systems, with publications in venues including Science Advances, National Science Review, and IEEE journals.
Google Scholar · LinkedIn · Curriculum Vitae · Email
PhD Candidate, Penn State BME · Visiting PhD Student, University of Illinois Urbana-Champaign
News
- Accepted a Postdoctoral Fellow position in the Department of Computer Science at UT Austin, beginning January 2027.
- Our work on bioprinted optoelectronically active cardiac tissues was featured by the National Institute of Biomedical Imaging and Bioengineering (NIBIB).
- Our work on bioprinted optoelectronically active cardiac tissues was published in Science Advances.
- My work on sleep apnea detection from ECG signals using sliding singular spectrum based subpattern principal component analysis was published in IEEE Transactions on Artificial Intelligence.
- My book chapter titled localization and classification of heart sounds in PCG signals was published in Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing.
- Passed my PhD comprehensive examination. I am grateful to my doctoral committee members—Dr. Xiao Liu, Dr. Xiaogang Hu, Dr. Sri-Rajasekhar Kothapalli, and my advisor, Dr. Cunjiang Yu—for their guidance and support.
- Passed my PhD candidacy examination. I am grateful to my committee members—Dr. Xiao Liu, Dr. Xiaogang Hu, Dr. Sri-Rajasekhar Kothapalli, Dr. Thomas Neuberger, and my advisor, Dr. Cunjiang Yu—for their guidance and support.
Research
Neural and physiological signal analysis
Signal processing and machine learning for EEG, EOG, EMG, and ECG, including sleep, drowsiness, and neurological applications.
Human-machine interfaces
Intelligent systems that translate physiological signals and human intent into responsive assistive technologies.
Selected Publications
View all on Google Scholar- 2025Bioprinted optoelectronically active cardiac tissues
F. Ershad, Z. Rao, …, M. Zubair, et al.
Science Advances 11(4), eadt7210
- 2024Displacement-pressure biparametrically regulated softness sensory system for intraocular pressure monitoring
Y. Cheng, Y. Zhan, …, M. Zubair, C. Yu, and C. Guo
National Science Review, nwae050
- 2024Detection of Sleep Apnea From ECG Signals Using Sliding Singular Spectrum Based Subpattern Principal Component Analysis
M. Zubair, U. K. Naik M, R. K. Tripathy, et al.
IEEE Transactions on Artificial Intelligence 5
- 2021Detection of Epileptic Seizures From EEG Signals by Combining Dimensionality Reduction Algorithms With Machine Learning Models
M. Zubair, M. V. Belykh, U. K. Naik M, et al.
IEEE Sensors Journal 21(15)
- 2021Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis
S. Agarwal and M. Zubair
IEEE Sensors Journal 21(23)