Liang Dong

Liang Dong

Room 301B, Rogers Engineering and Computer Science Building
One Bear Place #97356
Waco, Texas 76798-7356
Phone: +1-254-710-4589
Fax: +1-254-710-3010
liang_dong@baylor.edu

Liang Dong is an associate professor of Electrical & Computer Engineering at Baylor University. He specializes in digital signal processing, wireless communications and networking, deep learning for signal processing and communications, and cyber-physical systems. His research is sponsored by Intel, L3, TARDEC, MDOT, DENSO, and Baylor VP for Research.

Liang is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the American Physical Society (APS). He served on the Executive Board of IEEE West Michigan Section from 2006 to 2011. He served as a Technical Program Committee member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, as well as a session chair for IEEE WCNC 2013, IEEE GlobalSIP 2016 and IEEE WCNC 2018. Liang is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu. He is a faculty advisor of Baylor Unite InterVarsity.

Education

Teaching

Research

  • 5G Mobile Communications

  • The 5th generation (5G) mobile communication is the next generation of wireless broadband connection. To establish a ubiquitous connection of people and things, 5G has three specifications: enhanced mobile broadband, ultra-reliable low-latency communications, and massive machine-type communications. Our research focuses on dynamic spectrum sharing, orthogonal frequency-division multiple access, and multiple-input multiple-output technique for 5G.
  • Internet of Things

  • The Internet of Things (IoT) allows users to gather data from the physical environment. New information and communication technologies make the IoT scalable and reliable to support the emerging demand for smart cities, active health, and connected cars. Our research focuses on energy-efficient communications and networking, big data analysis, and security and trustworthiness of the IoT.
  • Deep Learning

  • Deep neural networks use algorithms, big data, and computing power of digital systems to enable machines to learn quickly, accurately, and on a large scale. Our research explores convolutional neural network, recurrent neural network, deep reinforcement learning, generative adversarial networks, and other advanced methods that drive artificial intelligence.

© Liang Dong, 2021