Dr. Shepherd has a 30-year background in quantitative imaging. Most of his academic research involves developing novel biomarkers for a variety of imaging modalities including dual-energy X-ray Absorptiometry (DXA) scan for bone density and body composition, mammography for breast density and tissue textural measures as risk factors for breast cancer, and 3-D optical whole body scanning for quantifying body shape as a risk factor for metabolic diseases. Using these modalities, he uses advanced statistical methods to extract more information from images. The methods include principal component analysis, shape and appearance modeling, deep learning, and artificial intelligence approaches. While at UCSF he accumulated over 4 million mammograms for the study of breast cancer risk from imaging and was one of the first to show that volumetric breast density measures are a stronger risk predictor than areal density measures. His active research questions include the following: • Can deep learning models for reading mammograms reduce recall rates and unnecessary biopsies for women of the Pacific region? • What is the best way to describe frailty and function using quantitative composition measures? • Can 3-D optical scans of the whole body accurately and precisely quantify body composition and other health risk factors, and improve compliance of nutrition and exercise interventions? He also holds four patents in quantitative imaging. His group has been expert consultants for the NHANES study DXA component for 20 years. He has published over 150 peer-reviewed papers, is a Fulbright Scholar, and a past president of the International Society for Clinical Densitometry.