U.S. CMS Software and Computing Research Initiative

The Software and Computing Research Initiative provides partial funding for physicists working in areas where R&D are needed to meet the goals of Software and Computing for the HL-LHC. Projects span the different R&D focus areas, including advanced algorithms, analysis systems, and underlying infrastructure. The overall goal is to make computation of all types feasible and efficient at HL-LHC scale.

Current U.S. CMS Post Doctoral Researchers

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Mohamed Darwish
Baylor University

Developing heterogeneous particle flow reconstruction for the CMS Phase 2 detector

Mar 2024 - Feb 2025

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Jethro Gaglione
Vanderbilt University

Development of a Distributed GPU Machine Learning Training Facility at Vanderbilt's ACCRE Cluster

Oct 2023 - Sep 2024

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Yao Yao
Purdue University

Automating algorithm loading and executing on GPUs for SONIC

Oct 2023 - Sep 2024

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Kelci Mohrman
University of Florida

Deploying GPU algorithms through SONIC

Sep 2023 - Aug 2024

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Nick Manganelli
University of Colorado Boulder

On Demand Column Joining with ServiceX (2024) and Advancing Machine Learning Inference with Columnar Analysis at CMS Analysis Facilities (2023)

Jan 2023 - Jan 2025

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Oz Amram
Fermilab

AI Denoising to Accelerate Detector Simulation

Sep 2022 - Sep 2024

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Nick Smith
Fermilab

Object Storage for CMS in the HL-LHC era

Jan 2022 - Jan 2024

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Carlos Erice Cid
Boston University

Accelerating Pixel Unpacking and Vertex Reconstruction with GPUs

Oct 2021 - Oct 2024


Former U.S. CMS Post Doctoral Researchers

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Ruchi Chudasama
The University of Alabama

Accelerating Machine Learning Reconstruction in CMS

Sep 2022 - Sep 2023

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Matteo Cremonesi
University of Notre Dame

Cost-Benefit Analysis of Strategies for Using GPUs

Jul - Jun 2021

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Patrick McCormack
Massachusetts Institute of Technology

Accelerating offline computing with the Fast Machine Learning Lab

Jun 2021 - Jun 2023

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Mark Saunders
Baylor University

Heterogenous Particle Flow Reconstruction

Feb 2021 - Feb 2023

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Jan-Frederik Schulte
Purdue University

Accelerating muon reconstruction in the CMS HLT using GPUs

Jan 2021 - Jan 2022

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Di Croce Davide
The University of Alabama

Accelerating Machine Learning Reconstruction in CMS

Sep 2020 - Sep 2022

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Savannah Thais
Princeton University

GNN Track Reconstruction on FPGAs Accelerators at CMS

Sep 2020 - Sep 2022