Philip Harris

1.1k total citations
21 papers, 357 citations indexed

About

Philip Harris is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics and Astronomy and Astrophysics. According to data from OpenAlex, Philip Harris has authored 21 papers receiving a total of 357 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 7 papers in Nuclear and High Energy Physics and 5 papers in Astronomy and Astrophysics. Recurrent topics in Philip Harris's work include Particle physics theoretical and experimental studies (6 papers), Particle Detector Development and Performance (5 papers) and Pulsars and Gravitational Waves Research (5 papers). Philip Harris is often cited by papers focused on Particle physics theoretical and experimental studies (6 papers), Particle Detector Development and Performance (5 papers) and Pulsars and Gravitational Waves Research (5 papers). Philip Harris collaborates with scholars based in United States, Switzerland and United Kingdom. Philip Harris's co-authors include Nhan Viet Tran, Dylan Rankin, Christoph Englert, C. Vernieri, Michael Spannowsky, L. Asquith, A. Hinzmann, M. Campanelli, M. Vos and Benjamin Nachman and has published in prestigious journals such as Reviews of Modern Physics, Journal of High Energy Physics and Physical review. D.

In The Last Decade

Philip Harris

20 papers receiving 342 citations

Peers

Philip Harris

NHEP

CVPR

EEE

Dylan Rankin United States

Ramon Winterhalder Germany

V. M. Mikuni United States

Alexey Svyatkovskiy United States

S.‐C. Hsu United States

Sascha Diefenbacher Germany

Juan Pavez Chile

Frank Gaede Germany

T. Golling Switzerland

H. Qu China

Dylan Rankin United States

Philip Harris

81 ×0.4

NHEP

76 ×0.7

39 ×1.0

CVPR

28 ×0.7

32 ×1.1

EEE

Citations per year, relative to Philip Harris Philip Harris (= 1×) peers Dylan Rankin

Countries citing papers authored by Philip Harris

Since Specialization

Citations

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

Fields of papers citing papers by Philip Harris

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philip Harris

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

All Works

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20 of 20 papers shown

Tsoi, Ho Fung, Vladimir Lončar, Sridhara Dasu, & Philip Harris. (2025). SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression. Machine Learning Science and Technology. 6(1). 15021–15021. 6 indexed citations

Chen, Yi-Hui, E. E. Khoda, Scott Hauck, et al.. (2025). Low latency transformer inference on FPGAs for physics applications with hls4ml. Journal of Instrumentation. 20(4). P04014–P04014.

Benoit, William, Deep Chatterjee, M. Saleem, et al.. (2025). Machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences. Physical review. D. 111(4). 5 indexed citations

Govorkova, Ekaterina, William Benoit, Deep Chatterjee, et al.. (2024). GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders. Machine Learning Science and Technology. 5(2). 25020–25020. 8 indexed citations

Saleem, M., S.-W. Yeh, R. M. Magee, et al.. (2024). Demonstration of machine learning-assisted low-latency noise regression in gravitational wave detectors. Classical and Quantum Gravity. 41(19). 195024–195024. 2 indexed citations

Chatterjee, Deep, et al.. (2024). Rapid likelihood free inference of compact binary coalescences using accelerated hardware. Machine Learning Science and Technology. 5(4). 45030–45030. 5 indexed citations

Duarte, J., A. Roy, E. A. Huerta, et al.. (2023). FAIR AI models in high energy physics. Machine Learning Science and Technology. 4(4). 45062–45062. 3 indexed citations

Govorkova, Ekaterina, N. Chernyavskaya, Philip Harris, et al.. (2023). Knowledge Distillation for Anomaly Detection. 641–646. 1 indexed citations

Khoda, E. E., Dylan Rankin, R. Teixeira De Lima, et al.. (2023). Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. Machine Learning Science and Technology. 4(2). 25004–25004. 8 indexed citations

10.

Harris, Philip, et al.. (2023). Neural embedding: learning the embedding of the manifold of physics data. Journal of High Energy Physics. 2023(7). 8 indexed citations

11.

Lončar, Vladimir, M. Pierini, S. Summers, et al.. (2022). Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. Machine Learning Science and Technology. 3(4). 45011–45011. 17 indexed citations

12.

Rankin, Dylan, Philip Harris, E. Katsavounidis, et al.. (2022). A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics. 9–17. 3 indexed citations

13.

Rankin, Dylan, J. Krupa, M. Saleem, et al.. (2022). Hardware-accelerated inference for real-time gravitational-wave astronomy. Nature Astronomy. 6(5). 529–536. 12 indexed citations

14.

Tarafdar, Naif, Giuseppe Di Guglielmo, Philip Harris, et al.. (2021). AIgean : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters. ACM Transactions on Reconfigurable Technology and Systems. 15(3). 1–32. 7 indexed citations

15.

Ngadiuba, J., Vladimir Lončar, M. Pierini, et al.. (2020). Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. DSpace@MIT (Massachusetts Institute of Technology). 44 indexed citations

16.

Tarafdar, Naif, Giuseppe Di Guglielmo, Philip Harris, et al.. (2020). AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters. CERN Document Server (European Organization for Nuclear Research). 239–239. 4 indexed citations

17.

Englert, Christoph, et al.. (2019). Machine learning uncertainties with adversarial neural networks. The European Physical Journal C. 79(1). 4–4. 38 indexed citations

18.

Duarte, J., Song Han, Philip Harris, et al.. (2019). Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications. 305–305. 5 indexed citations

19.

Kogler, R., Benjamin Nachman, A. Schmidt, et al.. (2019). Jet substructure at the Large Hadron Collider. Reviews of Modern Physics. 91(4). 145 indexed citations

20.

Adams, Niall M., et al.. (1996). A review of parallel processing for statistical computation. Statistics and Computing. 6(1). 37–49. 11 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.

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