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Eric Jonas
Neuroscience Research
Eric Jonas
is a research scientist in computational neuroscience and signal
-processing,-pursuing-two-main-lines-of-investigation.-The-first-focuses-on-how--learning-
and memory work: how does the brain take in new information, build internal
models, and do things with those models (such as save them to the neocortex). His
second line of research focuses on building new types of machine learning models
using signal processing technology to detect patterns in this kind of data that could
not be detected natively. In both endeavors, Jonas has to grapple with two distinct
sets-of-problems.-The-first-set-involves-finding-the-right-models-and-techniques-to-
interpret and work with incredibly noisy, high-throughput neural data. The other set
of problems involves bridging the gap between the scientists running the experi-
ments and those creating the mathematical models.
While Jonas has been continuously fascinated with the brain since he was seven
years old, he has alternated brain science with entrepreneurship. He co-founded two
start-ups, both of which were backed by
Peter-Thiel's-venture-capital-firm, Founders
Fund.-The-first,
Navia Systems, developed a new class of probabilistic computers to
make inferences under uncertainty. He was CEO of the second, Prior Knowledge,
which created a predictive database that learned the deep structure of the data it
contained, and then used that knowledge to generate predictions based on proba-
bilistic inference. Just three months after being featured as a TechCrunch Disrupt
Finalist, Prior
Knowledge
was-acquired-by-Salesforce.com.-Jonas-transitioned-with-
the company and went on to become Salesforce.com's
Chief Predictive Scientist. In
early 2014, Jonas returned to pure research, an environment he knows well from
his career at MIT, where he took four degrees: a BS in electrical engineering and
computer science, a BS in brain and cognitive sciences, a MEng in electrical engineer-
ing and computer science, and a PhD in brain and cognitive sciences. He is currently
a postdoctoral fellow in EECS at UC Berkeley.
