[Sds-seminars] [Sds-announce] S&DS Talk by Gonzalo E. Mena, 2/14, 4pm, "What can statisticians learn from the analysis of C.elegans data?"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science Seminar 


GONZALO E. MENA, University of Oxford


Monday, February 14, 2022

4:00PM to 5:00PM

Zoom: https://yale.zoom.us/j/94307171328

 <https://gomena.github.io/> Website

 

 

 

Title: What can statisticians learn from the analysis of C.elegans data?

 

Information and Abstract: 

We live in revolutionary times for neuroscience; the recent advent of
technologies for the recording of entire brains at massive scales is
transforming our understanding of the mind. In this talk I argue that these
developments are also shaping the way we conceive statistics; the challenges
and bottlenecks that arise in these new regimes often reveal the brittleness
of our current tools, dictating the need for new methods and motivating new
questions.

I will focus on my contribution to NeuroPAL, a new breakthrough technology
that enables the colorful imaging of every single neuron in the brain of the
C.elegans worm. I will describe new statistical methods for two challenging
tasks arising in these datasets; neural segmentation and identification,
where classical methods fall short. Behind these new methods there is a key
statistical physics principle, the so-called Schrödinger bridge, a ‘thought
experiment’ that realizes the solution of an entropy-regularized optimal
transport problem. This thought experiment was proposed in 1932 but has not
yet percolated into the mainstream of statistics. I will show how it affords
us with new rationale for the design of better statistical methods.  

First, I will comment on the statistical (sample complexity) benefits of
entropic optimal transport and how a loss function based on this principle
is a better optimization objective than the log-likelihood for clustering,
reducing pathologies such as bad local optima and inconsistency. In
consequence, a new algorithm derived from this loss, Sinkhorn EM, attains
better, more robust neural segmentation performance. Then, I will comment on
an alternative perspective of the Schrödinger bridge, the challenging
problem of the inference of permutations: I will show how some approximate
inference methods can be used for identifying neurons in C.elegans, As a
result, we obtain meaningful uncertainty quantification in this hard
combinatorial setup. I will further comment on how these novel methods have
proven their usefulness in other contexts such as deep learning.

Finally, I will present some of my work on a recent pressing problem, the
analysis of the true impact of the ongoing pandemic. This problem also
raises relevant statistical questions regarding identifiability.

 

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