Gavin Niendorf

Gavin Niendorf

Physics PhD Student

Cornell University

Biography

I am a Ph.D. candidate at Cornell University specializing in particle physics research and high-performance computing applications for CERN. My research focuses on parallel computing techniques for particle tracking, funded by Princeton’s Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP). Prior to Cornell, I graduated with a B.S. in physics and a minor in statistics from UC Santa Barbara with Highest Honors. My undergraduate research was primarily for the Light Dark Matter eXperiment (LDMX), an accelerator experiment designed to search for low-mass dark matter. Outside of physics, I spent a year as a Research Engineer at a defense company near Washington, D.C., developing software for research projects funded primarily by DARPA.

Interests

  • Data Science
  • Particle Physics
  • Parallel Computing

Education

  • PhD in Physics, 2021-Present

    Cornell University

  • BSc in Physics, 2020

    UC Santa Barbara

Projects

Line Segment Tracking (LST)

Highly parallelizable particle-tracking algorithm written in C++, now integrated into CMSSW, that reconstructs particle trajectories in the CMS detector at CERN. LST replaces the traditional Kalman Filter algorithm with a GPU-accelerated approach to handle the increased data throughput expected at the High Luminosity LHC (HL-LHC).
Line Segment Tracking (LST)

TracePy

TracePy is a sequential ray tracing package written in Python 3 for designing optical systems in the geometric optics regime. I also built a parallelizable version of TracePy in C++ capable of running on GPUs via CUDA. TracePy received over 65 stars on GitHub and is also available on PyPI for easy download.
TracePy

Recent Publications

See my Google Scholar for a full list of my publications.

Line Segment Tracking in the HL-LHC

The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks …
Gavin Niendorf, Tres Reid, Peter Wittich, Peter Elmer, Bei Wang, Philip Chang, Yanxi Gu, Vyacheslav Krutelyov, Balaji Venkat Sathia Narayanan, Matevz Tadel, Emmanouil Vourliotis, Avi Yagil
PDF

A High Efficiency Photon Veto for the Light Dark Matter eXperiment

Fixed-target experiments using primary electron beams can be powerful discovery tools for light dark matter in the sub-GeV mass range. …
Torsten Åkesson, Nikita Blinov, Lene Bryngemark, Owen Colegrove, Giulia Collura, Craig Dukes Valentina Dutta, Bertrand Echenard, Thomas Eichlersmith, Craig Group, Joshua Hiltbrand, David G. Hitlin, Joseph Incandela, Gordan Krnjaic, Juan Lazaro, Amina Li, Jeremiah Mans, Phillip Masterson, Jeremy McCormick, Omar Moreno, Geoffrey Mullier, Akshay Nagar, Timothy Nelson, Gavin Niendorf, James Oyang, Reese Petersen, Ruth Pöttgen, Philip Schuster, Harrison Siegel, Natalia Toro, Nhan Tran, Andrew Whitbeck
PDF

Light Dark Matter eXperiment (LDMX)

We present an initial design study for LDMX, the Light Dark Matter Experiment, a small-scale accelerator experiment having broad …
Torsten Åkesson, Asher Berlin, Nikita Blinov, Owen Colegrove, Giulia Collura, Valentina Dutta, Bertrand Echenard, Joshua Hiltbrand, David G. Hitlin, Joseph Incandela, John Jaros, Robert Johnson, Gordan Krnjaic, Jeremiah Mans, Takashi Maruyama, Jeremy McCormick, Omar Moreno, Timothy Nelson, Gavin Niendorf, Reese Petersen, Ruth Pöttgen, Philip Schuster, Natalia Toro, Nhan Tran, Andrew Whitbeck
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Posts

FFMA Speedup with CUDA const

Using __device__ const lets NVCC embed network weights directly into FFMA instructions, reducing memory accesses and speeding up DNN inference in Line Segment Tracking (LST).