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<span style="margin: 0px; font-size: 15px; color: rgb(0, 0, 0); background-color: white;">The AI Lunch Series will be hosting Dr. Daniel Murnane (LBL) next Wednesday for a presentation on “Graph Neural Networks and Metric Learning for the Future of Particle
Physics”. Additional information can be found at: <a href="https://www.jlab.org/AI/lunch_series/public_events" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="0" style="margin:0px">https://www.jlab.org/AI/lunch_series/public_events</a></span><span style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); display: inline !important;"></span>
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12-1PM, Wednesday, June 2, 2021</div>
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Join with Bluejeans:<span style="margin:0px"> <a href="https://bluejeans.com/260264787" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="1" style="margin:0px">https://bluejeans.com/260264787</a></span></div>
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Abstract:</div>
<span style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); display: inline !important;">The discovery of the Higgs boson at the Large Hadron Collider was a colossal effort that brought together many advanced experimental, theoretical and computational
techniques. We are opening a door to a new generation of particle physics experiments that will try to answer questions about dark matter, dark energy and why the universe seems to be so perfectly tuned for complex structures, like life. Many Higgs-era techniques
may not be powerful enough for these new energy/intensity scales, and so we turn to machine learning (ML) techniques to help answer these questions. It turns out that not only does ML boost our ability to discover new physics, it also challenges us to think
about physics problems in new ways.</span>
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<span style="margin: 0px; background-color: rgb(255, 255, 255); display: inline !important;">If you have questions, contact tennant@jlab.org </span><br>
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