Cleaning up messy data at the intersection of machine learning and healthcare #WiDS2017 - SiliconANGLE

There are two fields with seemingly endless career opportunities: healthcare and computer science. And when medical care intersects with technology, the possibilities are life changing. Machine learning introduces new insights to healthcare professionals through compiling big data to improve the way doctors and clinicians can diagnose, treat and even predict outcomes for their patients, according to Finale Doshi-Velez (pictured), assistant professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences. Doshi-Velez is on the front lines of educating and researching tangible ways to improve mental health through machine learning. She is working with students in several areas, but her focus is on machine learning for healthcare applications focused on dissecting the autism spectrum and helping to alleviate depression. Doshi-Velez spoke with Lisa Martin (@Luccazara), co-host of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the Stanford Global Women in Data Science (WiDS) Conference in Stanford, CA, about her work in these developing fields and how she is preparing the next generation by teaching students the emerging skills they will need in a new workplace.