Mojtaba S. Fazli, a Ph.D. candidate in computer science, focuses on developing computational tools for tracking and modeling biological systems on multiple scales. In one project, he uses cutting-edge machine learning and computer vision techniques to observe dynamic motion patterns of Toxoplasma gondii, one of the most common parasites and the causal agent of toxoplasmosis, which is estimated to infect over a billion people worldwide. As the parasite’s motion is linked to its pathogenicity, Fazli’s novel algorithms automatically track and model its motion patterns, helping to identify and develop therapeutic countermeasures. In a second project, he is studying morphological changes in the mitochondria of lung cells in response to infection by Mycobacterium tuberculosis, the causative agent of tuberculosis. Through the development of innovative machine learning tools, Fazli is measuring changes in mitochondrial shape and quantity, which could provide insights into the pathogenicity of different bacterial mutants and lead to treatment strategies.