Artificial Intelligence Accelerated Discoveries at the Large Hadron Collider
by
Online
PKU indico cross-reference:
https://indico.ihep.ac.cn/event/17348/
Join Zoom Meeting
https://cern.zoom.us/j/68615333583?pwd=V2ZMVnkwSGtWSzkrKy85ME1GS3ZMQT0
Zoom Meeting ID: 686 1533 3583
Passcode: 328799
Abstract:
Searches for new physics beyond the Standard Model at the Large Hadron Collider (LHC) require paradigm shifts in search strategies and advanced instrumentation. To harness the Proton-Proton collisions at the highest energy of unprecedented rate, innovative approaches must be explored and recent development in artificial intelligence (AI) offers such opportunities. In my talk, I will introduce essential elements in boosting the discovery potential with accelerated AI: science drivers at the LHC, interplay between Machine Learning (ML) and domain knowledge, as well as ML-specific compute systems. I will highlight a few studies in ML algorithms, in collaboration with experts in Purdue CS, that enable important science topics at the LHC. I will also discuss the challenges of realizing ML in scientific instruments and solutions explored in my previous work. At the end of my talk, I will introduce the multidisciplinary NSF A3D3 (accelerated AI algorithms for data driven discovery) HDR institution and how these explorations can benefit science domains broadly.
Resume:
Dr. Miaoyuan Liu completed her PhD at Duke University in 2015, her thesis work is on establishing the first evidence of triboson processes with W boson produced associated with two photons using ATLAS data. As a postdoc at Fermilab from 2015 to 2020, she performed searches for three heavy gauge bosons events that led to the first observation of the VVV process and evidences of WWW/WWZ with CMS 13 TeV proton collision data collected during LHC Run-2 operation. she also searched for SUSY particles such stop Pairs and electroweakinos using CMS early Run-2 data. She led the commissioning and testing of the CMS phase 1 forward pixel detector pre/post installation at Fermilab and is continuing to contribute to the cms phase 2 outer tracker upgrade as an assistant professor at Purdue University starting in 2020. Her recent work focuses on improving CMS physics sensitivities with machine learning and heterogeneous computing hardwares.
