Mark S. Neubauer

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 31 papers between 2018 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

In proceedings

Bibliography

Building Machine Learning Challenges for Anomaly Detection in Science.

CoRR, March, 2025

Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks.

CoRR, January, 2025

From Signals to Features to Insights: Multi-Level Novelty Detection for Fast Scientific Discovery.

Proceedings of the 43rd IEEE VLSI Test Symposium, 2025

HiGTR: High-Performance FPGA Implementation of Complete GNN-based Trajectory Reconstruction for HEP.

Proceedings of the 2025 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, 2025

Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning.

Mach. Learn. Sci. Technol., 2024

Corrigendum: Applications and techniques for fast machine learning in science.

Frontiers Big Data, 2024

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

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.

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

Data Science and Machine Learning in Education.

Explainable AI for High Energy Physics.

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

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

Charged Particle Tracking via Edge-Classifying Interaction Networks.

Comput. Softw. Big Sci., 2021

Graph Neural Networks for Charged Particle Tracking on FPGAs.

Applications and Techniques for Fast Machine Learning in Science.

A FAIR and AI-ready Higgs Boson Decay Dataset.

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline.

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.

Software Sustainability & High Energy Physics.

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

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.

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

Machine Learning in High Energy Physics Community White Paper.

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

Mark S. Neubauer

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Mark S. Neubauer

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