Sahar Behpour (’22 Ph.D), a recent doctoral graduate in information science, realized her strong interest in developing data-driven universal artificial intelligence after taking a machine learning course with Mark Albert, assistant professor of biomedical engineering. While studying the visual information processing in the brain, also known as efficient coding, Behpour wondered,“How can we use similar processes when making artificial neuron networks?” From this inquiry, she chose her dissertation topic – weight initialization of convolutional neural networking using unsupervised machine learning. Each new research discovery and focus leads Behpour to her ultimate goal of becoming a leader in the artificial intelligence field. She hopes to create different infostructures to analyze data and models to provide a diverse set of services for the greater community.