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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal style='background:white'><span style='color:black'><a href="https://statistics.yale.edu/" title="Department of Statistics and Data Science
"><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0;text-decoration:none'><img border=0 width=150 height=49 style='width:1.5625in;height:.5069in' id=logo src="cid:image001.jpg@01D81E60.1BDCF9B0" alt="Department of Statistics and Data Science
"></span></a></span><span style='color:black'> <a href="https://statistics.yale.edu/" title=Home><b><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0'>Department of Statistics and Data Science Seminar </span></b></a></span><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#222222'><o:p></o:p></span></p><h1 style='mso-margin-top-alt:.1in;margin-right:0in;margin-bottom:0in;margin-left:0in;background:white'><span style='font-size:23.0pt;font-family:Mallory;color:black;text-transform:uppercase;font-weight:normal'>GONZALO E. MENA</span><span style='font-size:13.0pt;font-family:Mallory;color:black'>, </span><span class=odd><span style='font-size:13.0pt;font-family:Mallory;color:black'>University of Oxford</span></span><span style='font-size:23.0pt;font-family:Mallory;text-transform:uppercase;font-weight:normal'><o:p></o:p></span></h1><p class=MsoNormal style='background:white'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">
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</v:shape><![endif]--><![if !vml]><img width=135 height=161 style='width:1.4027in;height:1.6805in' src="cid:image003.jpg@01D81E60.1BDCF9B0" align=left hspace=12 v:shapes="Picture_x0020_2"><![endif]><span class=date-display-single><span style='font-size:13.5pt;font-family:Mallory;color:black'>Monday, February 14, 2022</span></span><span class=date-display-single><span style='font-size:13.5pt;font-family:Mallory'><o:p></o:p></span></span></p><p class=MsoNormal style='background:white'><span class=date-display-start><span style='font-size:13.5pt;font-family:Mallory;color:black'>4:00PM</span></span><span class=date-display-range><span style='font-size:13.5pt;font-family:Mallory;color:black'> to </span></span><span class=date-display-end><span style='font-size:13.5pt;font-family:Mallory;color:black'>5:00PM</span></span><span style='font-size:13.0pt;font-family:Mallory'><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Zoom: <a href="https://yale.zoom.us/j/94307171328">https://yale.zoom.us/j/94307171328</a><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><a href="https://gomena.github.io/"><span style='font-size:12.0pt;color:#003C76'>Website</span></a><o:p></o:p></span></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:15.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:15.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:15.0pt;font-family:Mallory;color:#222222'>Title: What can statisticians learn from the analysis of C.elegans data?<o:p></o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Information and Abstract: <o:p></o:p></span></b></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white;box-sizing: inherit'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>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.<o:p></o:p></span></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white;box-sizing: inherit'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>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. <o:p></o:p></span></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white;box-sizing: inherit'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>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.<o:p></o:p></span></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white;box-sizing: inherit'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>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.<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>