T. M. Hong

98.5k total citations
13 papers, 75 citations indexed

About

T. M. Hong is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics and Biomedical Engineering. According to data from OpenAlex, T. M. Hong has authored 13 papers receiving a total of 75 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 4 papers in Nuclear and High Energy Physics and 3 papers in Biomedical Engineering. Recurrent topics in T. M. Hong's work include Particle physics theoretical and experimental studies (4 papers), Particle Detector Development and Performance (4 papers) and Computational Physics and Python Applications (3 papers). T. M. Hong is often cited by papers focused on Particle physics theoretical and experimental studies (4 papers), Particle Detector Development and Performance (4 papers) and Computational Physics and Python Applications (3 papers). T. M. Hong collaborates with scholars based in United States, South Korea and China. T. M. Hong's co-authors include B. T. Carlson, H. J. Stelzer, Seung Goo Lee, Jong Hee Kim, CheolGi Kim, Hyun‐Woo Lee, Han Seong Kim, Jung-Soon Lee, Ji Eun Lee and Sheng Chen and has published in prestigious journals such as Nature Communications, Physical Review A and IEEE Transactions on Computers.

In The Last Decade

T. M. Hong

11 papers receiving 75 citations

Peers

T. M. Hong

NHEP

AMPO

AI

BE

CNC

Stefano Cleva Italy

M.S. Emery United States

S. Kostoglou Switzerland

Y. Morita Japan

T. Andeen United States

H. Miyata Japan

N. Kazeev Russia

K. Zachariadou Greece

Vincenzo D’Auria Switzerland

Michael Fenner Germany

Stefano Cleva Italy

T. M. Hong

14 ×0.7

NHEP

14 ×0.7

AMPO

7 ×0.5

AI

19 ×1.6

BE

5 ×0.6

CNC

Citations per year, relative to T. M. Hong T. M. Hong (= 1×) peers Stefano Cleva

Countries citing papers authored by T. M. Hong

Since Specialization

Citations

This map shows the geographic impact of T. M. Hong'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 T. M. Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. M. Hong more than expected).

Fields of papers citing papers by T. M. Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T. M. Hong. 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 T. M. Hong. The network helps show where T. M. Hong may publish in the future.

Co-authorship network of co-authors of T. M. Hong

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

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Carlson, B. T., et al.. (2025). Nanosecond hardware regression trees in FPGA at the LHC. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 1072. 170209–170209. 1 indexed citations

2.

Chen, Sheng, et al.. (2025). Bandwidth on a Budget: Real-Time Configuration for Edge Video Analysis. IEEE Transactions on Computers. 75(2). 409–422.

3.

Hong, T. M., et al.. (2025). Intrinsic chiral optical vortex emission from the interactions between free electrons with bound states in the continuum. Physical Review Research. 7(4).

4.

Carlson, B. T., et al.. (2024). Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays. Nature Communications. 15(1). 3527–3527. 11 indexed citations

5.

Carlson, B. T., et al.. (2022). Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics. Journal of Instrumentation. 17(9). P09039–P09039. 8 indexed citations

6.

Hong, T. M., et al.. (2021). Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics. Journal of Instrumentation. 16(8). P08016–P08016. 18 indexed citations

7.

Hong, T. M., et al.. (2015). Preparation and Characterization of PVDF/PU Bicomponent Nanofiber by Electrospinning. Textile Science and Engineering. 52(2). 88–96. 2 indexed citations

8.

Lee, Sun-Young, et al.. (2015). Properties of aluminum deposited chemically recycled PET fabrics. Fibers and Polymers. 16(12). 2698–2703. 2 indexed citations

9.

Kim, Han Seong, et al.. (2014). Preparation and Characterization of Kenaf/Soy Protein Biocomposites. Journal of Biobased Materials and Bioenergy. 8(2). 221–229. 4 indexed citations

10.

Lee, Hyun‐Woo, et al.. (2014). Study on the TiO₂-Ag Nanoparticle Coated PET Fabric with an Atomizer. Textile Coloration and Finishing. 26(2). 99–105. 1 indexed citations

11.

Kim, Jong Hee, et al.. (2013). Viscosity of Magnetorheological Fluids Using Iron–Silicon Nanoparticles. Journal of Nanoscience and Nanotechnology. 13(9). 6055–6059. 9 indexed citations

12.

Hong, T. M., et al.. (1998). Temporal dark solitons in nonuniform Bose-Einstein condensates. Physical Review A. 58(4). 3128–3133. 17 indexed citations

13.

Chung, Sung Soo, Kug Sun Hong, Bong‐Soon Chang, et al.. (1998). Difference of Bonding Behavior between Four Different Kinds of Hydroxyapatite Plate and Rabbits's Bone. The Journal of the Korean Orthopaedic Association. 33(1). 158–158. 2 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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