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(650) 299-5260

2008 - Jason Ernst

The human genome sequence contains over three billion letters.  Understanding what the vast majority of these letters encode for has long been a mystery.  Emerging technologies are now providing a variety of data on the human genome, but can produce tens of millions of data points—and that’s where computational biologists step in.

Jason Ernst uses computational methodologies to integrate different data sources to analyze the human genome with the ultimate goal of better understanding and treating disease.   He develops and applies computational methods to the data, which can then be used to provide insights into different cell phenotypes and disease-associated DNA variations.

Jason was originally trained as a computer scientist, but became motivated by questions in biology, while a Ph.D student at Carnegie Mellon University studying machine learning.  

The technology and data resulting from the Human Genome Project prompted Jason to recognize that this was a situation in which he could apply machine learning.  He enjoys the computational challenges and opportunity to collaborate with experimental biologists, and hopes genomic work will eventually have a greater effect on personalized medicine and human health. 

Jason was a postdoctoral fellow at MIT during which time he was affiliated with the Broad Institute—a genomic medicine research center in Cambridge, Massachusetts.  He holds his undergraduate degrees in Computer Science and Mathematics from the University of Maryland, College Park, and is currently an Assistant Professor at the University of California, Los Angeles.  In his spare time, he enjoys running, Ultimate Frisbee, and soccer.