Yongbin Feng

About Me

I am Yongbin Feng (冯永彬 in Chinese), an assistant professor at Department of Physics and Astronomy of Texas Tech University studying experimental particle physics (HEP-ex). More information can be found in my CV [link].

For potential graduate students and postdocs interested in any of the topics of CMS data analyses, advanced calorimeter R&D (especially on the simulation and machine learning assisting reconstruction), heterogeneous computing with or without machine learning, and search for dark sector and dark matter at small fixed-target experiments, please feel free to contact me!

Work Experience

• Assistant Professor of Physics at Texas Tech University
  Lubbock, Texas, USA, Aug. 2024 - present
• Postdoctoral Research Associate at Fermi National Accelerator Laboratory
  Batavia, Illinois, USA, Nov. 2020 - Jul. 2024

Education

• Physics Ph.D.: University of Maryland, College Park, Maryland, USA, Aug. 2015 - Oct. 2020
• Physics B.S.: University of Science and Technology of China, Hefei, Anhui, China, Aug. 2011 - Jun. 2015

Research Interests

Inside the CMS experiment:

• High Precision Electroweak Measurements: W and Z cross sections, differential cross sections, etc [Pub]
• SONIC: Services of Network Inferences on Coprocessors, to improve the performance of (machine learning) inference using coprocessors [Pub] [Tutorial]
• HGCal Module Assembly and Performance Studies: Module assembly for the CMS High Granularity Calorimeter and performance studies with simulations [More info]
• Machine Learning Application in HEP: pileup mitigation, missing transverse momentum regression, calorimeter clustering and energy regression, etc [Pub]

Outside the CMS experiment:

• Advanced (Hadron) Calorimeter R&D: simulation, reconstruction, and machine learning for (hadron) calorimeter developments [More info]
• DarkQuest: proton fixed-target experiment to search for dark sector and light dark matter [Pub]

Talks & Seminars

• Introduction to Graph Neural Networks in High Energy Physics
  Discussion led at the Fermilab Lab-wide AI Meeting, Batavia, 11/2022 [Slides].
• DarkQuest: Probing dark sector with a proton fixed-target experiment at Fermilab
  Talk presented at the SYSU-PKU Particle Physics Forum, Virtual, 05/2022 [Slides].
• Semi-supervised graph neural network for pileup noise removal
  Talk presented at the University of Washington Machine Learning Forum, Virtual, 05/2022 [Slides].
• DarkQuest - Searching for light dark matter with a proton fixed-target experiment at Fermilab
  Talk presented at the 2022 Phenomenology Symposium, Pittsburg, Pennsylvania, USA, 05/2022 [Slides].
• Semi-supervised machine learning for pileup per particle identification with graph neural networks
  Talk presented at the 2021 BOOST workshop, Virtual, 08/2021 [Slides].
• Searching for light dark matter at Fermilab’s proton fixed-target experiment: DarkQuest
  Talk presented at the 2021 Particle Physics and Cosmology workshop, Virtual, 05/2021 [Slides].

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