Biography
Sharmila Majumdar, Ph.D., obtained her Ph.D. degree from Yale University in Engineering and Applied Sciences. After a short stay at Yale she joined UCSF as an Assistant Professor in 1989. Her research on machine and deep learning has a focus on imaging; prior work has focused on imaging technology development and translational, development of image processing, computer vision has been focused in the areas of osteoporosis, osteoarthritis, and lower back pain. She is the Executive and Scientific Director of the Center for Intelligent Imaging at UCSF. She was selected as a fellow of the American Institute of Medical and Biological Engineers in 2004, a fellow of the International Society of Magnetic Resonance in Medicine (ISMRM) in 2008, and awarded the ISMRM Gold medal in 2016. She is an active participant in the PMWC meetings. Her work over the last three decades has been extensively supported by the NIH and industry.

Biography
Jayashree Kalpathy-Cramer is the Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center for Biomedical Imaging and an Associate Professor of Radiology at MGH/Harvard Medical School. An electrical engineer by training, she worked in the semiconductor industry for a number of years. After returning to academia, she is now focused on the applications of machine learning and modeling in healthcare. Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology and ophthalmology. The work in her lab spans the spectrum from novel algorithm development to clinical deployment. She is passionate about the potential that these techniques have to improve access to healthcare in the US and worldwide. Dr. Kalpathy-Cramer has authored over 200 peer-reviewed publications and has written over 10 book chapters.

Biography
Dr. Mongan's research focuses on artificial intelligence in medical imaging. He was the senior author and primary investigator on a project that developed artificial intelligence for the detection of pneumothorax (collapsed lung); in partnership with General Electric, the algorithm developed in this project achieved FDA clearance and is currently commercially available on portable X-ray machines. He is the lead author on the Checklist for Artificial Intelligence in Medical Imaging (CLAIM), a guideline used by several journals to promote reproducibility in artificial intelligence publications, and is the lead author on a publication drawing lessons for the safe implementation of artificial intelligence in medicine from the 737 Max disasters. He chairs the Machine Learning Steering Committee of the Radiological Society of North America (RSNA, the world’s largest radiology specialty society) and serves on the editorial board of the journal Radiology: Artificial Intelligence.

Biography
Dr Lungren was an interventional radiologist and research faculty at Stanford University Medical School where he led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). He served as advisor for dozens of early stage startups and large fortune-500 companies on healthcare AI go-to-market strategy. His work has led to more than 100 scientific publications. Dr. Lungren is frequently featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare. Finally, Dr. Lungren is also a top rated instructor on Coursera where his AI in Healthcare course designed especially for learners with non-technical backgrounds has been completed by more than 10k students around the world.