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<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif">For our next installation of the bi-monthly Smart Center Community of Interest meeting, Dr. Thomas Britton, a Staff Scientist in Scientific Computing from
<a href="https://www.jlab.org/">Jefferson Lab</a> has graciously offered to give a talk on the topic below. His bio has been provided as well. Please join us !<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><u><span style="font-family:"Aptos",sans-serif">Presentation Title:</span></u></b><span style="font-family:"Aptos",sans-serif"> Computer Vision for Real Time Data Quality Monitoring<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><u><span style="font-family:"Aptos",sans-serif">Abstract:
<o:p></o:p></span></u></b></p>
<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif">Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, Hydra has analyzed
over 2 million images and has extended its capabilities to offline monitoring and validation. Hydra utilizes computer vision to continually analyze sets of images of monitoring plots generated 24/7 during experiments. Generally, these sets of images are produced
at a rate and quantity that is exceedingly difficult for shift crews to effectively monitor. With Hydra, shift crews, usually composed of teams of two individuals in 8 hour shifts during the 24/7 periods of experimental running, can focus on the multitude
of other important tasks required of them. Significant effort has been devoted to enhancing Hydra's user interface and experience to ensure that it provides clear, data-driven, actionable insights for shift workers and detector experts. Gradient Weighted Class
Activation Maps (GradCAM) provide added interpretability, allowing users to visualize important regions of the image for classification. The problem of human-based monitoring will be discussed in the context of Jefferson Lab’s Nuclear Physics program as well
as the role Hydra plays in solving that problem.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><u><span style="font-family:"Aptos",sans-serif">Biographical Sketch:</span></u></b><span style="font-family:"Aptos",sans-serif"><br>
Thomas Britton received his PhD from Syracuse University in 2016 for his work on exotic searches from B meson decays with the LHCb group. After graduate school Thomas joined Jefferson Laboratory as a Postdoctoral Fellow with the GlueX experiment, located in
Hall-D. As a postdoc, Thomas built MCwrapper, an automated system for running and managing Monte Carlo simulations that enables users to run simulations at the Open Science Grid, at GlueX member institutions, and on their local machines. MCwrapper became
the de facto way simulation is performed at GlueX. In 2019, he transitioned to a new role in the Computational Science & Technology division at Jefferson Laboratory where he finished development of Hydra, an AI based data quality monitoring system and deployed
it in Hall D,. Since 2019 Thomas has focused on automation and applied AI to automate detector calibration and control as well as expand Hydra, with the help of a dedicated team, to be deployed in every Experimental Hall and beyond. In his spare time, Thomas
enjoys gaming and astrophotography where you can often find him in the backyard yelling at clouds.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal">Best, <o:p></o:p></p>
<p class="MsoNormal">Rodney<o:p></o:p></p>
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