Hybrid Precoding/Combining (HPC) and Self-Interference Cancellation (SIC) for Full-Duplex (FD) Massive-Multiple-Input Multiple-Output (mMIMO) Wireless Communications Systems

The rapid advancement of wireless communications has drastically transformed the world over the last few decades. The next-generation wireless communication systems are expected to support a wide variety of requirements for diverse applications (e.g., the Internet of Things (IoT), industrial IoT (IIoT), autonomous driving, healthcare, virtual/augmented reality). Meeting such stringent requirements will bring significant challenges and require the exploration of new enabling technologies.

Robust Non-contact RF Sensing for Human Vital Sign and Activity Monitoring

Non-contact sensing for vital signs and human activities utilizing wireless signals has attracted a lot of attention in the last few years. Sensing using Ultra-wide-band or milli-meter wave radios is advantageous in its ability to penetrate garments or walls, operate under different lighting and weather conditions, and better preserve people’s privacy. Despite successful research demonstrations, there remain significant gaps in practical adoption and deployment. Many challenges need to be addressed to handle the presence of multiple subjects, subject diversity and environmental interferences.

Machine Learning for Practical and Scalable Regression Test Selection and Prioritization

In the context of systems with a large codebase, Continuous Integration (CI) significantly reduces integration problems, speed up development time, and shorten release time. While regression testing is widely practiced in the context of CI, it can be time-consuming and resource intensive for large codebases where the execution of test cases is time and resource intensive.

Photonic Cognitive Processor for Next Generation Artificial Intelligence Hardware

Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Traditional CPUs, GPUs, and brain-inspired electronics will not be powerful enough to train the neural networks of the near future. To radically impact the next generation of AI hardware, I propose to develop a fundamental technology: a photonic cognitive processor that uses light (instead of electrons).