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 R&D Initiative Researchers

Duong Nguyen
University at Buffalo

Tape data quality of service and tape data carousel

Jan - Dec 2025

Jin Zhou
University of Notre Dame

Scalable Data Analysis Applications for High Energy Physics

Aug 2024 - Sep 2025

Mohamed Darwish
Baylor University

Developing heterogeneous particle flow reconstruction for the CMS Phase 2 detector

Mar 2024 - Nov 2025

Jethro Gaglione
Vanderbilt University

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

Jan 2024 - Sep 2025

Yao Yao
Purdue University

Automating algorithm loading and executing on GPUs for SONIC

Oct 2023 - Mar 2026

Kelci Mohrman
University of Florida

Benchmarking current capabilities and exploring the acceleration of columnar processing via heterogeneous architectures (2025) and Deploying GPU algorithms through SONIC (2023)

Sep 2023 - Aug 2025

Carlos Erice Cid
Boston University

Accelerating Pixel Unpacking and Vertex Reconstruction with GPUs

Oct 2021 - Sep 2025

Former U.S. CMS R&D Initiative Researchers

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

Oz Amram
Fermilab

AI Denoising to Accelerate Detector Simulation

Sep 2022 - Sep 2024

Ruchi Chudasama
The University of Alabama

Accelerating Machine Learning Reconstruction in CMS

Sep 2022 - Sep 2023

Nick Smith
Fermilab

Object Storage for CMS in the HL-LHC era

Jan 2022 - Jan 2024

Matteo Cremonesi
University of Notre Dame

Cost-Benefit Analysis of Strategies for Using GPUs

Jul - Jun 2021

Patrick McCormack
Massachusetts Institute of Technology

Accelerating offline computing with the Fast Machine Learning Lab

Jun 2021 - Jun 2023

Mark Saunders
Baylor University

Heterogenous Particle Flow Reconstruction

Feb 2021 - Feb 2023

Jan-Frederik Schulte
Purdue University

Accelerating muon reconstruction in the CMS HLT using GPUs

Jan 2021 - Jan 2022

Di Croce Davide
The University of Alabama

Accelerating Machine Learning Reconstruction in CMS

Sep 2020 - Sep 2022

Savannah Thais
Princeton University

GNN Track Reconstruction on FPGAs Accelerators at CMS

Sep 2020 - Sep 2022