Biography
Dr. Li Bai is the Chair and Professor in the Electrical and Computer Engineering Department at Temple University. He has extensive research experience and expertise in distributed software computing, wireless sensor networks, system and software integration using commercial-off-the-shelf products and computer network security. He published over 60 peer-reviewed international journals and conference papers in the related areas. He has been involved in several sponsored research projects using portable devices with 802.11 wireless communication protocols. In addition, he was a core organizer in the 8th International Conference on Information Fusion held in Philadelphia in July 2005.
Labs: Computer Fusion Lab
Research Interests
- Distributed Software Computing
- Wireless Sensor Networks
- System and Software Integration Using Commercial-Off-the-Shelf Products and Computer Network Security.
Courses Taught
Number | Name | Level |
|---|---|---|
ECE 2612 | Digital Circuit Design | Undergraduate |
ECE 2613 | Digital Circuit Design Laboratory | Undergraduate |
ECE 8110 | Special Topics In ECE: Robotic Design using AWS | Graduate |
Selected Publications
Recent
Li, Y., Xie, D., Cember, A., Nanga, R., Yang, H., Kumar, D., Hariharan, H., Bai, L., Detre, J., Reddy, R., & Wang, Z. (2020). Accelerating GluCEST imaging using deep learning for B
0 correction. Magnetic Resonance in Medicine, 84(4), 1724-1733. doi: 10.1002/mrm.28289.Xie, D., Li, Y., Yang, H., Bai, L., Wang, T., Zhou, F., Zhang, L., & Wang, Z. (2020). Denoising arterial spin labeling perfusion MRI with deep machine learning. Magnetic Resonance Imaging, 68, 95-105. doi: 10.1016/j.mri.2020.01.005.
Xie, D., Li, Y., Yang, H., Song, D., Shang, Y., Ge, Q., Bai, L., & Wang, Z. (2019). BOLD fMRI-Based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11861 LNCS, 373-381. doi: 10.1007/978-3-030-32692-0_43.
Kollmer, J., Biswas, S., Bai, L., Sarwat, A., & Saad, W. (2018). A hardware-in-the-loop experimental platform for power grid security. ASEE Annual Conference and Exposition, Conference Proceedings, 2018-June.
Rege, A., Biswas, S., Bai, L., Parker, E., & McJunkin, T. (2017). Using simulators to assess knowledge and behavior of "novice" operators of critical infrastructure under cyberattack events. Proceedings - 2017 Resilience Week, RWS 2017, 50-56. doi: 10.1109/RWEEK.2017.8088647.
Kollmer, J., Irwin, R., Biswas, S., Saad, W., Sarwat, A., & Bai, L. (2017). Development of an experimental platform for analysis of cyber attacks on the power grid. ASEE Annual Conference and Exposition, Conference Proceedings, 2017-June.
Xie, D., Zhang, L., & Bai, L. (2017). Deep Learning in Visual Computing and Signal Processing. Applied Computational Intelligence and Soft Computing, 2017. doi: 10.1155/2017/1320780.