M. S. Neubauer
About
In The Last Decade
M. S. Neubauer
18 papers receiving 81 citations
Peers
Comparison fields: 5 of 26
- Nuclear and High Energy Physics 35
- Computer Networks and Communications 29
- Artificial Intelligence 19
- Information Systems 15
- Information Systems and Management 12
Countries citing papers authored by M. S. Neubauer
This map shows the geographic impact of M. S. Neubauer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. S. Neubauer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. S. Neubauer more than expected).
Fields of papers citing papers by M. S. Neubauer
This network shows the impact of papers produced by M. S. Neubauer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. S. Neubauer. The network helps show where M. S. Neubauer may publish in the future.
Co-authorship network of co-authors of M. S. Neubauer
This figure shows the co-authorship network connecting the top 25 collaborators of M. S. Neubauer. A scholar is included among the top collaborators of M. S. Neubauer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. S. Neubauer. M. S. Neubauer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Title | Journal | Authors | Indexed citations |
|---|---|---|---|---|
| 1 | Evidential deep learning for uncertainty quantification and out-of-distribution detection in jet identification using deep neural networks | Machine Learning Science and Technology | A. Roy, Volodymyr Kindratenko et al. | 2 |
| 2 | Software Citation in HEP: Current State and Recommendations for the Future | SHILAP Revista de lepidopterología | M. Feickert, Daniel S. Katz et al. | 0 |
| 3 | A detailed study of interpretability of deep neural network based top taggers | Machine Learning Science and Technology | M. S. Neubauer, A. Roy et al. | 10 |
| 4 | FAIR AI models in high energy physics | Machine Learning Science and Technology | J. Duarte, A. Roy et al. | 3 |
| 5 | Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs | Shi‐Yu Huang, Bo‐Cheng Lai et al. | 1 | |
| 6 | Codebase release 2.0 for UFOManager | OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) | M. S. Neubauer, A. Roy et al. | 1 |
| 7 | Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access | OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) | Wahid Bhimji, Eli Dart et al. | 0 |
| 8 | Making digital objects FAIR in high energy physics: An implementation for Universal FeynRules Output (UFO) models | OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) | M. S. Neubauer, A. Roy et al. | 1 |
| 9 | Graph Neural Networks for Charged Particle Tracking on FPGAs | Frontiers in Big Data | Shi‐Yu Huang, J. Duarte et al. | 22 |
| 10 | Deep Learning for the Matrix Element Method | Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022) | M. S. Neubauer, M. Feickert et al. | 1 |
| 11 | Interpretability of an Interaction Network for identifying $H \rightarrow b\bar{b}$ jets | Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022) | A. Roy, M. S. Neubauer | 2 |
| 12 | Towards Real-World Applications of ServiceX, an Analysis Data Transformation System | SHILAP Revista de lepidopterología | K. Choi, A. Eckart et al. | 2 |
| 13 | ServiceX A Distributed, Caching, Columnar Data Delivery Service | SHILAP Revista de lepidopterología | R. W. Gardner, L. Gray et al. | 6 |
| 14 | Supporting High-Performance and High-Throughput Computing for Experimental Science | arXiv (Cornell University) | E. A. Huerta, Roland Haas et al. | 7 |
| 15 | Container solutions for HPC Systems | arXiv (Cornell University) | Maxim Belkin, Roland Haas et al. | 10 |
| 16 | Diboson Production at Colliders | Annual Review of Nuclear and Particle Science | M. S. Neubauer | 5 |
| 17 | A fast hardware tracker for the ATLAS trigger system | M. S. Neubauer | 1 | |
| 18 | Globally Distributed User Analysis Computing at CDF | CERN Document Server (European Organization for Nuclear Research) | E. Lipeles, A. Sill et al. | 1 |
| 19 | The CDF central analysis farm | IEEE Transactions on Nuclear Science | Taehyun Kim, M. S. Neubauer et al. | 1 |
| 20 | Computing for Run II at CDF | Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment | M. S. Neubauer | 2 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.