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.

Research Interests

  • Distributed Software Computing
  • Wireless Sensor Networks
  • System and Software Integration Using Commercial-Off-the-Shelf Products and Computer Network Security.

Courses Taught




ECE 1111

Engineering Computation I


ECE 2612

Digital Circuit Design


ECE 2613

Digital Circuit Design Laboratory


ECE 3432

Robotic Control using Raspberry Pi Microcontroller


ECE 4422

Digital Control Systems


Selected Publications

  • Li, Y., Xie, D., Cember, A., Nanga, R., Yang, H., Kumar, D., Hariharan, H., Bai, L., Detre, J.A., Reddy, R., & Wang, Z. (2020). Accelerating GluCEST imaging using deep learning for B0 correction. Magnetic Resonance in Medicine, 84(4), pp. 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, pp. 95-105. doi: 10.1016/j.mri.2020.01.005

  • Xie, D., Li, Y., Yang, H.L., 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, pp. 373-381. doi: 10.1007/978-3-030-32692-0_43

  • Kollmer, J.D., Biswas, S.K., Bai, L., Sarwat, A.I., & 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.R. (2017). Using simulators to assess knowledge and behavior of "novice" operators of critical infrastructure under cyberattack events. Proceedings - 2017 Resilience Week, RWS 2017, pp. 50-56. doi: 10.1109/RWEEK.2017.8088647

  • Kollmer, J.D., Irwin, R.S., Biswas, S.K., Saad, W., Sarwat, A.I., & 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

  • Ren, Q., Bai, L., Biswas, S.K., & Ferrese, F. (2016). Energy saving in microgrid with tree configurations using nash bargaining solution. Proceedings - 2016 Resilience Week, RWS 2016, pp. 77-82. doi: 10.1109/RWEEK.2016.7573311

  • Gong, N., Biswas, S.K., Bai, L., & Butz, B.P. (2016). An intelligent tutoring system for multimedia virtual power laboratory. ASEE Annual Conference and Exposition, Conference Proceedings, 2016-June.

  • Xie, D. & Bai, L. (2016). A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, pp. 745-748. doi: 10.1109/ICMLA.2015.208

  • Gong, N., Korostelev, M., Bai, L., Biswas, S., & Ferrese, F. (2015). Consensus power agent model and stability analysis for (¿,k)-star power grids. Proceedings - 2015 Resilience Week, RSW 2015, pp. 29-34. doi: 10.1109/RWEEK.2015.7287414

  • Ren, Q., Bai, L., Biswas, S., Ferrese, F., & Dong, Q. (2015). Demand-supply balancing using multi-agent system for bus-oriented microgrids. Proceedings - 2015 Resilience Week, RSW 2015, pp. 35-42. doi: 10.1109/RWEEK.2015.7287415