Artificial intelligence uses deep neural networks to learn from data and make predictions based on what it has learned.  Artificial intelligence (deep learning) relies on data to develop the structure of the mathematical model, uses non-linearity and benefits from advanced computing.  In this talk we will explore how deep learning is being used in MRI to decrease the time that a patient needs to be in the MRI scanner.  The talk will present commonly used methods for accelerating MRI, like collecting less data followed by neural network reconstructions, to generate images.  We will then explore active areas of research for evaluating image quality which include experiments of how well a human detects a lesion in an MRI image, statistical detection theory and how to mathematically predict the outcomes of these experiments.  The work presented in this talk was mostly done by undergraduate research students. The talk will include ideas for mentoring students, an overview of my career trajectory and advice for PhD students in applied mathematics.

About the speaker

Dr. Angel R. Pineda is a professor of mathematics at Hofstra University. He previously taught at California State University, Fullerton and Manhattan University. He completed his BS in chemical engineering from Lafayette College, his PhD in applied mathematics from the University of Arizona and his postdoctoral fellowship in the Radiology Department of Stanford University. His research studies the task performance in detection tasks in MRI reconstructions using deep learning, statistical detection theory and psychophysical experiments.  He is currently a PI of a research grant from NIH, was the PI of a mentoring grant for underrepresented students from NSF and has been a mathematical consultant for GE Healthcare. He served in the National Subcommittee on Research by Undergraduates of the Mathematical Association of America (MAA) and currently serves on the committee on Graduate Assistantships in Developing Countries (GRAID) of the International Mathematical Union (IMU).  In 2009, he received support from the Society for Industrial and Applied Mathematics (SIAM) as a volunteer lecturer in Cambodia.   In 2024, he received the Award for Distinguished Public Service from the American Mathematical Society (AMS).