[Sds-seminars] Eric Jonas speaking on Thursday at 4pm "Exploiting computational scale for richer model-based inference"

[Dan Spielman]

ERIC JONAS
University of California, Berkeley Electrical Engineering and Computer
Science
*Exploiting computational scale for richer model-based inference*
Thursday, February 21, 4:00PM to 5:15PM
Mason Laboratory
9 Hillhouse Avenue, Room 211

Understanding the deluge of scientific data acquired from next-generation
technologies, from astronomy to neuroscience, requires advances in
translating our existing knowledge to useful models. Here I show how our
recent advances in scalable computing, from “serverless” cloud offerings to
deep function approximation, can let us capture and exploit this prior
knowledge. Examples include models derived from human intuition (for neural
connectomics), carefully-engineered physical systems (for imaging through
scattering media), and even direct simulation (for superresolution
microscopy). By expanding the space of models we can work with, we can
avoid common data science pitfalls while making computing at scale
accessible to the entire scientific community.

Bio: Eric Jonas is currently a postdoc in computer science at UC Berkeley
working with Ben Recht on machine learning for accelerating scientific
discovery. He earned his PhD in Computational Neuroscience, M. Eng in
Electrical Engineering, BS in Electrical Engineering and Computer Science,
and BS in Neurobiology, all from MIT. Prior to his return to academia, he
was founder and CEO of Prior Knowledge, a predictive database company which
was acquired in 2012 by Salesforce.com, where he was Chief Predictive
Scientist until 2014. In 2015 he was named one of the top rising stars in
bioengineering by the Defense Department’s Advanced Research Projects
Agency (DARPA).

Pre-talk tea, Dunham Lab., Suite 222, Room 228
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