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 and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.

Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.

He received the Research Development Award and Faculty Research and Creative Activities Award from Western Michigan University in 2008, the Faculty Scholars Award from Western Michigan University in 2011, and the Baylor Research Committee Award from Baylor University in 2015. His research is sponsored by L-3, TARDEC, MDOT, and DENSO.

Education

Research

  • Sustainable Internet of Things

  • The Internet of Things (IoT) allows users to gather data from the physical environment. The new information and communication technology makes 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, device energy harvesting, and trust and security of the IoT.
  • Deep Learning

  • Deep neural networks use algorithms, big data, and the computational power of the graphics processing unit (GPU) to allow machines learn at speed, accuracy, and scale that drive artificial intelligence. Our research uses deep learning to help solve big-data problems in e-health and life science applications.

Teaching

© Liang Dong, Baylor University 2017