Mark S. Neubauer

Orcid: 0000-0001-8434-9274

Affiliations:
  • University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, IL, USA


According to our database1, Mark S. Neubauer authored at least 26 papers between 2018 and 2024.

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Bibliography

2024
Corrigendum: Applications and techniques for fast machine learning in science.
Frontiers Big Data, 2024

2023
FAIR AI models in high energy physics.
Mach. Learn. Sci. Technol., December, 2023

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access.
Comput. Softw. Big Sci., December, 2023

A detailed study of interpretability of deep neural network based top taggers.
Mach. Learn. Sci. Technol., September, 2023

Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs.
Proceedings of the 33rd International Conference on Field-Programmable Logic and Applications, 2023

2022
Graph Neural Networks for Charged Particle Tracking on FPGAs.
Frontiers Big Data, 2022

Applications and Techniques for Fast Machine Learning in Science.
Frontiers Big Data, 2022

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective.
CoRR, 2022

Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access.
CoRR, 2022

Data Science and Machine Learning in Education.
CoRR, 2022

Explainable AI for High Energy Physics.
CoRR, 2022

Physics Community Needs, Tools, and Resources for Machine Learning.
CoRR, 2022

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges.
CoRR, 2022

2021
Charged Particle Tracking via Edge-Classifying Interaction Networks.
Comput. Softw. Big Sci., 2021

Graph Neural Networks for Charged Particle Tracking on FPGAs.
CoRR, 2021

Applications and Techniques for Fast Machine Learning in Science.
CoRR, 2021

A FAIR and AI-ready Higgs Boson Decay Dataset.
CoRR, 2021

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline.
CoRR, 2021

2020
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.
CoRR, 2020

Software Sustainability & High Energy Physics.
CoRR, 2020

The Scalable Systems Laboratory: a Platform for Software Innovation for HEP.
CoRR, 2020

2019
Supporting High-Performance and High-Throughput Computing for Experimental Science.
Comput. Softw. Big Sci., December, 2019

Enabling real-time multi-messenger astrophysics discoveries with deep learning.
CoRR, 2019

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era.
CoRR, 2019

2018
Machine Learning in High Energy Physics Community White Paper.
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CoRR, 2018

Container solutions for HPC Systems: A Case Study of Using Shifter on Blue Waters.
Proceedings of the Practice and Experience on Advanced Research Computing, 2018


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