Valentina Pedoia, PhD, is an Assistant Professor in the Center of Intelligent Imaging at the University of California, San Francisco.

She is an Imaging and data scientist with a primary interest in developing algorithms for advanced computer vision and machine learning for improving the usage of non-invasive imaging as diagnostic and prognostic tools.

Her main research focus is on exploring the role of machine learning in the extraction of contributors to osteoarthritis (OA).
She is studying analytics to model the complex interactions between morphological, biochemical and biomechanics aspects of the knee joint as a whole; deep learning convolutional neural network for musculoskeletal tissue segmentation, degenerative changing detection, and OA incidence and progression prediction. Het ultimate goal is to develop molt-molit-modal data-driven models that is able to extract features and use them to identify risk factors and predict outcomes.