San Francisco, CA
May 15-16, 2017
Director, Clinical Inference and Algorithms Program
Stanford Health Care

Zeeshan Syed is the Inaugural Director of the Clinical Inference and Algorithms Program at Stanford Health Care and a Clinical Associate Professor at the Stanford University School of Medicine. Before joining Stanford in 2016, Dr. Syed was an Associate Professor with Tenure in Computer Science and Engineering at the University of Michigan, where he was a Principal Investigator for the Artificial Intelligence Laboratory and led the Computational Biomarker Discovery and Clinical Inference Group. Dr. Syed received SB and MEng degrees in Electrical Engineering and Computer Science at MIT, and a PhD through a joint program between MIT’s School of Engineering and Harvard Medical School in Computer Science and Biomedical Engineering. Dr. Syed’s research investigates the design and application of advanced healthcare-specialized machine learning and artificial intelligence technologies for clinical effectiveness, high-value care and population health, and has featured at top machine learning and artificial intelligence conferences (NIPS, ICML, AAAI, KDD) as well as in the media (Wired, CBS, NPR, WSJ, Technology Review, ZDNet). Dr. Syed is the recipient of multiple national awards for his scholarship activities, including the prestigious CAREER award from the National Science Foundation. Dr. Syed is also actively engaged with the healthcare analytics industry, having been part of the core early-stage team for the Google[X] Life Sciences initiative (now Verily) and as a founder of HEALTH[at]SCALE Technologies.

May 15, 2017
4:30pm - 5:00pm
Grand Ballroom

There are two main approaches to machine learning – supervised and unsupervised – and each has specific applications in the context of healthcare. And even though their impact has not yet sent shockwaves through the industry, the potential of each is enormous.

At its basic level, machine learning involves looking at data, and from that data finding information that is not readily visible. Example: Applying machine learning to data about patients infected with Zika or another virus and using what we can learn about what happens to those people to inform care decisions regarding the best ways to treat people who get infected in the future. 

As healthcare entities continually ramp up their analytics and big data efforts and gird for precision medicine and population health, machine learning as well as artificial intelligence and cognitive computing are poised to become even more valuable.

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