<div dir="ltr">Happening now! </div><br><div class="gmail_quote gmail_quote_container"><div dir="ltr" class="gmail_attr">On Sun, Feb 8, 2026 at 5:52 PM Pooyan Goodarzi <<a href="mailto:pooyan.goodarzi@email.ucr.edu">pooyan.goodarzi@email.ucr.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><u></u>
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<p>Hi all,</p>
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<p>We will have a PASS talk <b>Tomorrow, February 9</b>. Please
join us at noon (<b>from 12:00 to 1:00 pm</b>) in the <b>Conference
Room</b> (PHYS 3051) to hear about his interesting research. Apologies
for the late notice.</p>
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<p align="center"><b>Ryan Milton, PhD Candidate</b><br>
Monday, 12:00 pm, Conference Room (PHYS 3051)</p>
<p align="center"><b>Probing nucleon structure with deep inelastic
scattering and ML methods</b></p>
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<div align="center">Collider experiments are essential to
understanding the structure of nucleons and their behavior in
nuclei. Measurements of particles at these experiments are made
with sophisticated detector systems. Reconstructing particles
from detector signals and forming physics conclusions without
biases from the detectors is a complicated task. In this talk I
will discuss novel ML-based reconstruction and unfolding methods
used during this process, and their application to data from the
CLAS experiment at Jefferson Lab and to simulations for the
future Electron-Ion Collider at Brookhaven National Lab.</div>
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<div dir="ltr"><b>If you’re interested in sharing your work as
a speaker, please feel free to add your name to this
spreadsheet [<a href="https://docs.google.com/spreadsheets/d/1N3ncf43jdB6aHYHhyWmyHMycxDg4_phHZXLkQvviO0o/edit?usp=sharing" target="_blank">Google Sheet</a>]. Recordings of all the
previous talks are available on our website: <a href="https://ucrpass.arxiv.social/" target="_blank">https://ucrpass.arxiv.social</a></b>
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Best,<br>
Pooyan<br>
Physics and Astronomy Student Seminar (<span><span>PASS</span></span>)
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<a href="https://ucrpass.arxiv.social/" target="_blank">https://ucrpass.arxiv.social</a></div>
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