Sharon Aviran

NIH K99/R00 Postdoctoral Fellow
Center for Computational Biology
Departments of Molecular and Cell Biology and Mathematics
UC Berkeley
email: saviran [at] berkeley [dot] edu

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I am a postdoctoral innovation fellow at the Center for Computational Biology at UC Berkeley,
working with Lior Pachter. I am broadly interested in statistical models and algorithms applied to problems
in functional genomics and in developing tools that integrate computation and experiment to advance our
understanding of RNA biology and to improve the predictable design of RNAs with novel functions.
My research is at the interface between applied mathematics, statistics, computer science, and biology,
and draws on tools from machine learning, information theory, and discrete optimization.

My current research interests are in developing computational and statistical methods for genome-wide
analysis of RNA structure mapping measurements and for utilizing these data to infer RNA structural
dynamics and structure-function relationships. Specifically, my research builds on a solution we recently
developed for analysis of chemical structure mapping measurements, and which we applied to data
obtained from a new protocol, called SHAPE-Seq, coupling the novel SHAPE chemistry to next-generation
sequencing technologies. I also work on methods for statistical inference of genomic data from other
high-throughput sequencing protocols, such as RNA-Seq. Previously, I have worked with the teams of
Adam Arkin and David Schaffer on analyzing HIV's population dynamics and evolution when the virus is
exposed to gene therapy treatments.

Before coming to Berkeley, I obtained my PhD in information theory and comminucation systems at
the Electrical and Computer Engineering department at UCSD, under the supervision of Paul Siegel
and Jack Wolf. My PhD work focused on the design and analysis of error-correcting codes and signal
processing methods for digital storage devices.

Prior to my PhD, I worked for two years in the telecommunications industry, developing machine learning
and signal processing applications, and obtained my Master's in discrete optimization from the Technion,
where I worked with Shmuel Onn.