Marzyeh Ghassemi, "Designing Machine Learning Processes for Equitable Health Systems" | JHU IAA скачать в хорошем качестве

Marzyeh Ghassemi, "Designing Machine Learning Processes for Equitable Health Systems" | JHU IAA

Colleagues of the Applied Physics Laboratory and the Whiting School of Engineering are invited to the June talk in a speaker series co-presented by the Johns Hopkins Institute for Assured Autonomy (IAA) and the Computer Science Department, featuring national scholars presenting new research and development at the intersection of autonomy and assurance. This talk will be “Designing Machine Learning Processes for Equitable Health Systems” featuring speaker Marzyeh Ghassemi, MIT Assistant Professor in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), presenting virtually on Tuesday, June 21st at 11 a.m. Dr. Ghassemi’s abstract and bio are attached. This event is open to all APL and JHU staff, faculty, and students; please share! ABSTRACT: Dr. Marzyeh Ghassemi focuses on creating and applying machine learning to understand and improve health in ways that are robust, private and fair. Health is important, and improvements in health improve lives. However, we still don’t fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted. Dr. Ghassemi will talk about her work trying to train models that do not learn biased rules or recommendations that harm minorities or minoritized populations. The Healthy ML group tackles the many novel technical opportunities for machine learning in health, and works to make important progress with careful application to this domain. About the Johns Hopkins Institute for Assured Autonomy: Led by APL and the Whiting School of Engineering, the IAA is becoming a nationally recognized center of excellence in autonomous systems, showcasing the robust portfolio of research and work from two premier divisions of JHU and creating strategic external partnerships. The IAA seeks to ensure the safe, secure, and reliable integration of autonomous systems and artificial intelligence (AI) in society. As autonomous systems proliferate, both physically and virtually, the institute seeks to ensure the systems will be trusted and safe in their operations, will withstand corruption by adversaries, and will integrate seamlessly into ecosystems and communities. In this burgeoning field, JHU strives to advance a clear vision for an autonomous future.