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<span style="line-height: normal;"></span><span style="font-family: ".SFUI-Regular";">Hi GAPS,</span></p>
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<span style="font-family: ".SFUI-Regular";">We are happy to invite you to the second talk of our monthly Early Career Scientist seminar series! The talk will take place on
</span><span style="font-family: ".SFUI-Semibold";"><b>Wednesday December 11</b></span><span style="font-family: ".SFUI-SemiboldItalic";"><b><i>th</i></b></span><span style="font-family: ".SFUI-Semibold";"><b> at 2 PM in the MOB Auditorium</b></span><span style="font-family: ".SFUI-Regular";"> for
those of you onsite (light snacks will be provided) and on Zoom for those that aren’t (information listed below). The seminar is structured as a 40 minute talk followed by 15 minutes for you to ask any and all questions you might have for the speaker. </span></p>
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<span style="font-family: ".SFUI-Regular";">The seminars will initially focus on machine learning and its connections with the physics we do here at TRIUMF, and we are happy to have Vinicius Mikuni as our speaker this week. Dr. Mikuni is a NESAP for Learning
Postdoctoral Fellow at NERSC. His current research focuses on machine learning development and application for the physical sciences, including experimental High Energy Physics, Nuclear Physics, and Astrophysics. He received his PhD in 2021 from the University
of Zurich, measuring the production cross-section of top quark pairs in association to b quarks and the search for new physics in signatures involving third-generation fermions using the data collected by the CMS Collaboration.</span></p>
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<span style="font-family: ".SFUI-Regular";">More information about the talk and zoom coordinates can be found below. Looking forward to seeing you on Wednesday!</span></p>
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<span style="font-family: ".SFUI-Regular";">-Your GAPS, on behalf of the Early Career Talks organizing committee</span></p>
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<span style="font-family: ".SFUI-Regular";"> ______________________________________</span></p>
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<span style="font-family: ".SFUI-Regular";">Speaker: Dr. Vinicius Mikuni (in person)</span></p>
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<span style="font-family: ".SFUI-Regular";">Title: Accelerating Discovery in High Energy Physics using AI</span></p>
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<span style="font-family: ".SFUI-Regular";">Abstract: The past decade was marked by an exponential increase in the availability of experimental data in high energy physics, leading to unprecedented precision in the description of particle interactions. However,
indirect evidence for new physics processes, such as the existence of dark matter, motivates the development of new methodologies to scrutinize the data in the search for new scientific discoveries. In this talk, I will introduce different applications of
how artificial intelligence (AI) has been transformative in the way to analyse data from collider experiments. These include the development of fast simulation routines, high-dimensional deconvolution algorithms, and alternative ways to search new particle
interactions. I will discuss future directions for each of these areas and potential synergies with other fields in the physical sciences.</span></p>
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<span style="font-family: ".SFUI-Regular";">Zoom: </span><span style="font-family: ".SFUI-Regular"; color: rgb(0, 0, 238);"><u><a href="https://ubc.zoom.us/j/62977946035?pwd=5VT5r5y3LYZwupU8RpUIa5rOPxC0rn.1" rel="noreferrer noopener" style="color: rgb(0, 0, 238); margin-top: 0px; margin-bottom: 0px;">https://ubc.zoom.us/j/62977946035?pwd=5VT5r5y3LYZwupU8RpUIa5rOPxC0rn.1</a></u></span></p>
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<span style="font-family: ".SFUI-Regular";">Meeting ID: 629 7794 6035</span></p>
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<span style="font-family: ".SFUI-Regular";">Passcode: 724459</span></p>
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