Jun Ding is an Assistant professor at the Department of Medicine, Faculty of Medicine and Health Sciences at McGill University. Alongside his team, he is dedicated to employing machine learning techniques to decipher the complex dynamics of cells in various diseases, such as developmental disorders, pulmonary diseases, and cancers. The diverse and intricate nature of these conditions necessitates innovative approaches, prompting the utilization of state-of-the-art single-cell technologies to meticulously profile individual cell states, providing a rich source of data for our machine learning models. These technologies presents unprecedented opportunities to advance the understanding, particularly in fields like developmental and cancer biology. However, they also pose challenges in developing computational models capable of linking this intricate biomedical data to potential discoveries.
His primary focus lies in the development and refinement of machine learning methodologies, especially probabilistic graphical models, to effectively analyze, model, and visualize both single-cell and bulk omics data, often featuring longitudinal or spatial dimensions. The goal of his research is to harness these advanced machine learning techniques to deepen the comprehension of cellular dynamics leading to groundbreaking diagnostic and therapeutic strategies that can significantly benefit public health.