The group's research interests in data science include

• AI & machine learning (e.g., self-supervised learning, iterative neural networks, diffusion models, sparse representation learning, and domain adaptation),

• optimization (e.g., non-convex optimization, block optimization, and proximal gradient methods), and 

• sampling (e.g., compressed sensing in multi-imager/sensor system and sampling optimization),

with past and current projects in imaging, image processing, and computer vision:

• medical imaging (e.g., X-ray CT, MRI, PET, and SPECT),

• computational photography (e.g., light-field photography, depth estimation, and 3D object tracking),

• vision-based autonomous systems (e.g., end-to-end autonomous driving, visual SLAM, inter-vehicle distance estimation, anomaly detection using drone imaging),

• biomedical image computing (e.g., abnormality detection on brain images and microscopic image segmentation), and

• digital healthcare (e.g., depression/ADHD diagnosis).

We are interested both in developing state-of-the-art AI & data science solutions for these problems, as well as improving fundamental understanding of these solutions.