Optics – the science of light that gives rise to extraordinary possibilities

1. Machine Learning for Atmospheric Boundary Layer Detection with Lidar Data

Dr. Chu’s research explores the integration of machine learning with atmospheric lidar data to improve the interpretation of complex environmental signals. He is particularly interested in developing physically-constrained neural network architectures for retrieving key atmospheric variables such as convective boundary layer height (CBLH), planetary boundary layer height (PBLH), eddy dissipation rates (EDR), and turbulence metrics. His work investigates how embedding physical principles into deep learning frameworks can enhance model accuracy and generalization across diverse atmospheric conditions, with applications in real-time weather forecasting, model data assimilation, and atmospheric science.

2. Spatiotemporal Variability of the Atmospheric Boundary Layer and Its Driving Mechanisms

Dr. Chu’s research explores the integration of machine learning with atmospheric lidar data to improve the interpretation of complex environmental signals. He is particularly interested in developing physically-constrained neural network architectures for retrieving key atmospheric variables such as convective boundary layer height (CBLH), planetary boundary layer height (PBLH), eddy dissipation rates (EDR), and turbulence metrics. His work investigates how embedding physical principles into deep learning frameworks can enhance model accuracy and generalization across diverse atmospheric conditions, with applications in real-time weather forecasting, model data assimilation, and atmospheric science.

3. Development of Next-Generation Lidar Systems

Dr. Chu’s research interests lie in advanced lidar hardware development, with a focus on the design and optimization of frequency comb-based lidar, solid-state Raman lidar, and heterodyne water vapor detection systems. He is particularly interested in the application of whispering gallery mode (WGM) resonators and frequency-stabilized optical techniques to enhance the performance of lidar systems under diverse environmental conditions. His work explores the deployment of lidar across various platforms, including ground-based, mobile, and airborne systems, aiming to expand their utility in atmospheric sensing, environmental monitoring, aviation safety, and disaster risk assessment.

4. Optical Fiber Ultrasonic Sensing

Dr. Chu’s research interests include the development of advanced fiber-optic ultrasonic sensing technologies for nondestructive evaluation (NDE) and structural health monitoring. He is particularly focused on designing multi-channel fiber-optic coil sensor systems with high phase sensitivity and robustness in high-noise industrial environments. His work explores interferometric calibration techniques, signal enhancement methods, and integration of these sensors into intelligent diagnostic platforms for aerospace, rail, and energy infrastructure. The goal is to enable next-generation sensing networks that offer high-resolution, low-interference performance for real-time monitoring of structural integrity.

5. Laser Ultrasonic Techniques for Nondestructive Evaluation (Laser UT)

Dr. Chu’s research interests include laser-based ultrasonic nondestructive evaluation (NDE), with a particular emphasis on hybrid multi-modal sensing systems for defect detection and localization in dynamic environments. He is focused on advancing optical beam shaping techniques and optimizing laser energy distribution to enhance the precision and reliability of laser excitation in ultrasonic testing. His work aims to support the development of real-time diagnostic technologies for fatigue damage monitoring in rail systems, composite structures, and other critical infrastructure—contributing to safer, smarter, and more resilient industrial systems.