5th Annual Big Data in Biology Symposium, 2017




The 2017 Symposium will be held on Monday, May 8, 2017, in the Texas Union. We are excited to welcome Drs. Alfred Hero (University of Michigan), Peg McCarthy (University of Maryland School of Medicine), and George Weinstock (The Jackson Laboratory) as our keynote speakers.

Every year, this symposium provides a setting for researchers and trainees with an interest in computational biology, bioinformatics, and systems biology to interact. Excellent research that takes advantage of high throughput approaches, complex data, and/or high performance computing is showcased from The University of Texas at Austin, including TACC (Texas Advanced Computing Center).

This year, the Big Data in Biology symposium is the kick-off event for the month-long Pop-Up Institute "Seeing the Tree and the Forest: Understanding Individual and Population Variation in Biology, Medicine, and Society." Diverse researchers—from biology, medicine, statistics, nutrition, sociology, public health, anthropology, athletics, and more—will be collaborating across traditional research boundaries to investigate the causes and consequences of individual and population variation.

Registration is free. This event is organized by the graduate student organization, "The Transdisciplinary Big Data Scientists." The Center for Computational Biology and Bionformatics and the Vice President for Research provides funding for this event.

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2016 Symposium

This symposium's keynote speaker was Dr. Pamela Silver of Harvard University. In addition, Dr. Lucia Carbone of the Oregon Healthy & Science University presented an invited lecture at the symposium.

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2015 Symposium

This symposium's keynote speaker was Dr. Shelley Berger from the University of Pennsylvania. Breakout sessions included topics such as "Open Science," "Big Data in Medicine and Health," and "Careers in Biotech/Industry."

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2014 Symposium

This symposium featured Edward Marcotte of UT-Austin as the keynote speaker. Breakout sessions included topics such as "Big Data in Undergraduate Teaching," "Industry Career Panel," and "Big Data in Medicine."

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