Pouyan Asgharzadeh

Pouyan's research lies on the interface of biophysics, computer vision and machine learning. He focuses on developing biophysics-aware machine learning models to investigate dynamics of biological processes. His main focus has been investigating structure-function relathionship in nano-scale of biopolymers in cellular proceeses and micro-scale of bone tissue. He has developed biomedical image analysis frameworks include deep learning models for resolving high resolution computer tomogrophy (CT) images and biopolymer structural feature-extraction models from laser confocal scanning microscopy (CLSM) images. Pouyan's current research focuses on developing bone maturation and adaptation deep learning models from time-resolved HR-pQCT and µCT images and simulation based data synthesis.

Surrogate machine learning model of structure-function relationship of biopolymers (Asgharzadeh et al. Acta Bio. 2018 and Asgharzadeh et al. BioRxiv 2020)

Bone aging assesment model (BAAM) deep neural network (Asgharzadeh et al. Acta Bio. 2020)