We are interested in cutting-edge deep learning technologies to study various computer vision problems in bio-medicine, including but not limited to:

• Learning features from 2D/3D imaging data
• Learning shape descriptors from 3D models (point clouds, meshes, etc.)
• Learning motion (deformation) characteristics from 3D dynamic models

In this project, we develop a number of algorithms and software tools to reconstruct 3D models from a series of scanning or transmission electron microscopy images:

• GPU-accelerated 3D surface reconstruction from 2D SEM imaging data
• 3D surface characteristics analysis of SEM
• Automated particle detection from 2D cryo-EM images
• 3D atomic modeling from reconstructed cryo-EM volumes of molecular complexes

We are interested in a broad range of image processing and analysis problems. Of our particular interest are:

• Image interpolation and registration on serial sectioning data
• Mesh generation from 3D images
• Image segmentation (graph-based, level sets, and deep learning methods)
• 3D surface reconstruction from multi-view 2D images

This project involves a number of mesh generation and processing problems:

• Feature-preserving surface mesh smoothing (reducing mesh bumpiness)
• Quality-guaranteed surface mesh smoothing (improving angle distribution)
• High-quality tetrahedral mesh generation from surface meshes
• 3D shape descriptor and shape retrieval

In this project we aim to explore the following specific problems:

• High-quality surface and volumetric mesh generation from molecules
• Effective geometric measurements and analysis of molecular surfaces
• Non-rigid molecular shape matching

We are interested in scientific computing and 3D visualization, including:

• Radial basis functions in interpolation and eigenanalysis of 3D point clouds
• GPU-based parallel computing in biomedical simulations
• Efficient visualization of 3D images and simulated data