Le received his Bachelor's degree in Bioengineering from Southeast University (Nanjing, China) in 2018, and his Master's degree from the University of Bristol in Biomedical Engineering in 2019, awarded Distinction Top 1 Student and Departmental Prize.

His current research includes both hardware and software for the next-generation wearable EEG and Brain-Computer Interface technology. 

1. 3D-printed EEG electrodes for low-cost and personalized EEG monitoring, for different head shapes, hair types, and skin properties, for inclusive EEG.

2. Deep Learning algorithms for EEG artefacts removal (EOG, motion, EMG artefacts) for mobile EEG. The target is to create novel algorithms for computers and more importantly, on smartphones by using hardware acceleration.

3. Phase-Locked Loop (PLL) algorithm for real-time EEG phase tracking and targeting for non-invasive brain stimulation.

4. Smarphone-based brain stimulation App creation for visual and acoustic stimulation for sleep engineering and chronic pain treatment for people's home use.

All of the parts above can be used to form a closed-loop brain-computer interface for non-invasive brain sensing and actuating.