[Statlist] Friday November 15 at 14:00 - Talk by Caroline Uhler - From Causal Inference to Autoencoders and Gene Regulation

[Guillaume Obozinski]

SDSC Talk
Friday, November 15th @14:00 at EPFL in room BC 420 (see map
<http://plan.epfl.ch/?lang=fr&room=bc420>)
From Causal Inference to Autoencoders and Gene Regulation

*By **Caroline Uhler *



*Abstract:*

Recent progress in genomics makes it possible to perform perturbation
experiments at a very large scale. This motivates the development of a
causal inference framework that is based on observational and
interventional data. We characterize the causal relationships that are
identifiable and present the first provably consistent algorithm for
learning a causal network from such data. I will then couple gene
expression with the 3D genome organization. In particular, we will discuss
approaches for integrating different data modalities such as sequencing or
imaging via autoencoders. We end by a theoretical analysis of autoencoders
linking overparameterization to memorization. In particular, we will show
that overparameterized autoencoders trained using standard optimization
methods implement associative memory and provide a mechanism for
memorization and retrieval of real-valued data.

*Bio:*

*Caroline Uhler* recently joined ETH Zürich as Full Professor of Machine
Learning, Statistics and Genomics. Prior to joining ETH Zürich, she was
Associate Professor at MIT. SHe holds a PhD in statistics from UC Berkeley,
spent a semester in the “Big Data” program at the Simons Institute at UC
Berkeley, held postdoctoral positions at the IMA and at ETH Zurich, was
assistant professor for 3 years at IST Austria. Her research focuses in
particular on graphical models, causal inference, algebraic statistics and
applications to genomics, for example on linking the spatial organization
of the DNA with gene regulation.

*Host*: Swiss Data Science Center, Guillaume Obozinski

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