Lei Lei

Education and Employment Background

Prof. Lei Lei received her PhD in Electrical Engineering from Beijing University of Posts and Telecommunications (BUPT) in Beijing, China. She has rich academic and industry experiences including working at China Mobile Research Institute (CMRI), Beijing Jiaotong University (BJTU) in China and James Cook University (JCU) in Australia. She is also an Adjunct Research Professor at Western University and University of Calgary. Lei joined the University of Guelph in 2020 and is now an Associate Professor.

Research Themes

Lei’s research is focused on applying artificial intelligence and machine learning methodologies to the optimal control of Internet-of-Things (IoT) and mobile cloud/edge computing for applications such as smart grid, autonomous driving, and cloud robotics. Cloud/Edge computing is a new paradigm of computing, while IoT is a vision for the next-generation Internet. The increased capacity of the fifth generation (5G) mobile networks means that the computing tasks of mobile devices can run at either the remote cloud or close edge servers. 5G provides a good opportunity to connect the IoT devices (e.g., sensors and wearable devices) to each other and the Internet under a unified framework. Key research themes include:

1. Reinforcement learning and optimal control of 3C functions. Traditional resource control methods in computer and communications systems and networks rely on methodology that is highly dependent on human intelligence (e.g. heuristic algorithms). Reinforcement learning enables the systems to learn the optimal control on their own based on the past experiences in a model-free style. Lei is interested in exploring how to optimize 3C functions—communications, computing, and control. Specifically, she is interested in applying reinforcement learning methods to enable energy management for smart grid, vehicle control for autonomous cars, and networked robots and the cloud to optimize control decisions.
2. Artificial neural networks (ANN) and IoT optimal control problems. ANN is an important branch of artificial intelligence and machine learning. The IoT network connects many devices to the Internet, and thus generates a huge amount of sensory data from the physical world. However, there are challenges when it comes to designing efficient machine learning algorithms to learn optimal policies directly from the massive amount of sensory input data for IoT applications. This data is processed to provide feedback to IoT devices and to optimize their control over the physical world. Lei is exploring how ANN can be integrated with the reinforcement learning to deal with the high-dimensional sensory input data from the massive number of IoT devices.

Our current research activities include:

1. Autonomous/Artificial Internet of Things
2. Dynamic Energy Management in Smart Grid
3. Intelligent Resource Control in Wireless Communications
4. Intelligent Resource Control in Mobile Cloud/Edge/Fog Computing
5. Autonomous driving, Intelligent Vehicular Networking, Vehicular Edge/Fog computing
6. Autonomous/Networked/Cloud Robot

Highlights 

1. Senior member of the Institute of Electrical and Electronics Engineers (IEEE), 2016.
2. Honor: 2021 N²Women: Stars in Computer Networking and Communications.
3. Best Paper Award: 2021 IEEE Internet of Things Journal, 2015 IEEE/CIC ICCC Conference, 2015 WASA Conference.
4. Executive Editor for Intelligence & Robotics
5. Associate Editor for Transactions on Emerging Telecommunications Technologies (ETT) and Peer-to-Peer Networking and Applications (PPNA).
6. Secretary for IEEE Communications Society Asia-Pacific Region Chapters Coordination Committee, 2015–2017.
7. Top 10 patent applicants at China Mobile Research Institute, 2009.