Malachi Schram

829 total citations · 1 hit paper
42 papers, 390 citations indexed

About

Malachi Schram is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Malachi Schram has authored 42 papers receiving a total of 390 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Nuclear and High Energy Physics, 11 papers in Computer Networks and Communications and 10 papers in Artificial Intelligence. Recurrent topics in Malachi Schram's work include Particle Detector Development and Performance (9 papers), Distributed and Parallel Computing Systems (9 papers) and Particle physics theoretical and experimental studies (7 papers). Malachi Schram is often cited by papers focused on Particle Detector Development and Performance (9 papers), Distributed and Parallel Computing Systems (9 papers) and Particle physics theoretical and experimental studies (7 papers). Malachi Schram collaborates with scholars based in United States, Japan and Poland. Malachi Schram's co-authors include A. Boehnlein, W. Nazarewicz, Markus Diefenthaler, Kostas Orginos, M. S. Smith, T. Horn, Xin-Nian Wang, Michelle Kuchera, Dean Lee and N. Sato and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Reviews of Modern Physics.

In The Last Decade

Malachi Schram

36 papers receiving 379 citations

Hit Papers

Colloquium: Machine learning in nuclear physics 2022 2026 2023 2024 2022 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Colloquium: Machine learning in nuclear physics
    2022 140 citations A. Boehnlein, Markus Diefenthaler et al. Reviews of Modern Physics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Malachi Schram United States 9 162 79 73 54 53 42 390
A. Pereira Spain 12 179 1.1× 26 0.3× 83 1.1× 45 0.8× 118 2.2× 50 462
Alexey Svyatkovskiy United States 5 136 0.8× 20 0.3× 51 0.7× 27 0.5× 68 1.3× 8 279
E. Peluso Italy 14 281 1.7× 75 0.9× 135 1.8× 28 0.5× 153 2.9× 68 582
Kelli Humbird United States 7 196 1.2× 73 0.9× 55 0.8× 19 0.4× 59 1.1× 24 375
Julian Kates‐Harbeck United States 5 138 0.9× 20 0.3× 51 0.7× 29 0.5× 57 1.1× 10 303
J. Stillerman United States 14 351 2.2× 26 0.3× 110 1.5× 67 1.2× 30 0.6× 71 587
Kevin Montes United States 9 272 1.7× 24 0.3× 119 1.6× 18 0.3× 92 1.7× 11 378
Keith Erickson United States 13 325 2.0× 33 0.4× 154 2.1× 78 1.4× 114 2.2× 39 566
Dalong Chen China 13 327 2.0× 14 0.2× 127 1.7× 61 1.1× 99 1.9× 62 493
G. Ososkov Russia 11 141 0.9× 57 0.7× 31 0.4× 14 0.3× 72 1.4× 89 424

Countries citing papers authored by Malachi Schram

Since Specialization
Citations

This map shows the geographic impact of Malachi Schram'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 Malachi Schram with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Malachi Schram more than expected).

Fields of papers citing papers by Malachi Schram

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Malachi Schram. 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 Malachi Schram. The network helps show where Malachi Schram may publish in the future.

Co-authorship network of co-authors of Malachi Schram

This figure shows the co-authorship network connecting the top 25 collaborators of Malachi Schram. A scholar is included among the top collaborators of Malachi Schram 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 Malachi Schram. Malachi Schram is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Goodall, Jonathan L., et al.. (2025). Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City. Journal of Hydrology. 656. 132697–132697. 7 indexed citations
2.
Lin, Sen, et al.. (2025). Outlook towards deployable continual learning for particle accelerators. Machine Learning Science and Technology. 6(3). 31001–31001.
3.
Schram, Malachi, et al.. (2025). SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver. Machine Learning Science and Technology. 6(2). 25017–25017.
4.
Schram, Malachi, et al.. (2025). Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators. Machine Learning Science and Technology. 6(2). 25018–25018. 1 indexed citations
5.
6.
Schram, Malachi, T. Britton, Chris H. Pappas, et al.. (2024). Distance preserving machine learning for uncertainty aware accelerator capacitance predictions. Machine Learning Science and Technology. 5(4). 45009–45009. 1 indexed citations
7.
Schram, Malachi, et al.. (2024). Robust errant beam prognostics with conditional modeling for particle accelerators. Machine Learning Science and Technology. 5(1). 15044–15044. 7 indexed citations
8.
Schram, Malachi, Steven Goldenberg, Lasitha Vidyaratne, et al.. (2023). Multi-module-based CVAE to predict HVCM faults in the SNS accelerator. SHILAP Revista de lepidopterología. 13. 100484–100484. 5 indexed citations
9.
Schram, Malachi, et al.. (2023). Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex. Physical Review Accelerators and Beams. 26(4). 7 indexed citations
10.
Drgoňa, Ján, et al.. (2023). AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 10244–10252. 1 indexed citations
11.
Ren, Jie, et al.. (2023). Investigating Anomalies in Compute Clusters: An Unsupervised Learning Approach. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
12.
Boehnlein, A., Markus Diefenthaler, N. Sato, et al.. (2022). Colloquium: Machine learning in nuclear physics. Reviews of Modern Physics. 94(3). 140 indexed citations breakdown →
13.
Schram, Malachi, et al.. (2022). Uncertainty Aware Deep Learning for Particle Accelerators. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
14.
Herwig, T. C., et al.. (2020). Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster. arXiv (Cornell University). 28 indexed citations
15.
Strube, J., et al.. (2019). Scaling studies for deep learning in Liquid Argon Time Projection Chamber event classification. SHILAP Revista de lepidopterología. 214. 6016–6016.
16.
Altıntaş, İlkay, et al.. (2018). Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows. 3905–3914. 3 indexed citations
17.
Ritter, M., L. Wood, T. Kuhr, et al.. (2018). Belle II Conditions Database. Journal of Physics Conference Series. 1085. 32032–32032.
18.
Schram, Malachi, Vikas Bansal, Nathan R. Tallent, et al.. (2017). Integrating prediction, provenance, and optimization into high energy workflows. Journal of Physics Conference Series. 898. 62052–62052. 1 indexed citations
19.
Stephan, Eric, Todd Elsethagen, Malachi Schram, et al.. (2016). Leveraging large sensor streams for robust cloud control. lxii. 2115–2120. 3 indexed citations
20.
Schram, Malachi, et al.. (1982). Smoking in the workplace: a review of human and operating costs.. PubMed. 27(8). 29–33, 83. 4 indexed citations

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by A. Pereira Breakdown of academic impact, for papers by Alexey Svyatkovskiy Breakdown of academic impact, for papers by E. Peluso Breakdown of academic impact, for papers by Kelli Humbird Breakdown of academic impact, for papers by Julian Kates‐Harbeck Breakdown of academic impact, for papers by J. Stillerman Breakdown of academic impact, for papers by Kevin Montes Breakdown of academic impact, for papers by Keith Erickson Breakdown of academic impact, for papers by Dalong Chen Breakdown of academic impact, for papers by G. Ososkov
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