Big Data in Biomedicine Conference set for May 20–22

This year’s event will cover the intersection of disciplines as widespread as genomics, population health, neuroimaging, crowdsourcing, immunology, ethical and legal issues and “learning” health systems.

- By Bruce Goldman

The conference will focus on how best to apply today’s massive computational power to the mountains of data steadily accumulating in biomedical databases, Internet search engines, social-media archives and wearable sensors

Juergen Faelchle / Shutterstock

The 2015 Big Data in Biomedicine Conference, to be held May 20-22 at the School of Medicine’s Li Ka Shing Center for Learning & Knowledge, will bring together hundreds of participants from several continents. Their common focus: how best to apply today’s massive computational power to the mountains of data steadily accumulating in biomedical databases, Internet search engines, social-media archives and wearable sensors.

“The Big Data in Biomedicine Conference brings together the industry experts, thought leaders and researchers who will help transform the way we diagnose, treat and prevent disease in our own community and around the world,” said Lloyd Minor, MD, dean of the School of Medicine. “The ideas and connections generated at this event will be catalysts in widespread transformation in human health.”

The annual three-day conference debuted in 2013, thanks to a grant from the Li Ka Shing Foundation. Last year, nearly 500 participants, including dozens of speakers and panelists from academia, information-technology and biotechnology corporations, venture capital firms, the U.S. government, hospitals and foundations convened on campus to discuss ways of harnessing the power of big data to improve human health. More than 1,000 others watched the event online via live-streamed video.

Topics to be highlighted this year will include genomics, population health, mHealth (the application of data obtained from mobile devices to medical practice and public health), neuroimaging, crowdsourcing, statistics, ethical and legal issues, immunology and “learning” health systems.

The research of one of the keynote speakers, Michael Levitt, PhD, professor of structural biology at Stanford, exemplifies the revelations made possible through the application of computer power to biomedical problems. Levitt shared the 2013 Nobel Prize in Chemistry for his development of multi-scale models elucidating the conformations of huge, unwieldy biomolecules.

Other keynote speakers will be Sharon Terry, CEO of Genetic Alliance; Kathy Hudson, PhD, deputy director of the National Institutes of Health; France Cordova, PhD, director of the National Science Foundation; Brook Byers, PhD, partner at Kleiner Perkins Caufield and Byers; and William King, CEO of Zephyr Health. 

Stanford scientists who will speak at the conference include Laura Carstensen, PhD, professor psychology and director of the Stanford Center on Longevity; bioethicist Mildred Cho, PhD, professor of pediatrics; Mark Cullen, MD, professor of medicine and director of the Stanford Center for Population Health Sciences; crowdsourcing-game developer Rhiju Das, PhD, assistant professor of biochemistry; neuroimaging pioneer Michael Greicius, MD, MPH; and Lester Mackey, PhD, assistant professor of statistics.

The conference is part of Stanford Medicine’s Biomedical Data Science Initiative, which strives to make powerful transformations in human health and scientific discovery by fostering innovative collaborations among medical researchers, computer scientists, statisticians and physicians.

To learn more or register for the conference, visit http://bigdata.stanford.edu.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu.

2023 ISSUE 3

Exploring ways AI is applied to health care