Thomas J. Ashby

659 total citations
13 papers, 348 citations indexed

About

Thomas J. Ashby is a scholar working on Computer Networks and Communications, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Thomas J. Ashby has authored 13 papers receiving a total of 348 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Networks and Communications, 4 papers in Hardware and Architecture and 4 papers in Artificial Intelligence. Recurrent topics in Thomas J. Ashby's work include Parallel Computing and Optimization Techniques (4 papers), Embedded Systems Design Techniques (3 papers) and Machine Learning in Materials Science (2 papers). Thomas J. Ashby is often cited by papers focused on Parallel Computing and Optimization Techniques (4 papers), Embedded Systems Design Techniques (3 papers) and Machine Learning in Materials Science (2 papers). Thomas J. Ashby collaborates with scholars based in Belgium, United States and Sweden. Thomas J. Ashby's co-authors include Pieter Ghysels, Wim Vanroose, Karl Meerbergen, Vladimir Chupakhin, Nina Jeliazkova, Ola Engkvist, Hugo Ceulemans, Jörg K. Wegner, Hongming Chen and Ivan Georgiev and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Computers and SIAM Journal on Scientific Computing.

In The Last Decade

Thomas J. Ashby

12 papers receiving 328 citations

Peers

Thomas J. Ashby

CTM

CNC

Jyothish Soman India

Wen Su China

Gili Rosenberg United States

Amos Waterland United States

Ramarathnam Venkatesan United States

Ian Karlin United States

Daniele Paolo Scarpazza United States

Ján Plavka Slovakia

Isidoro Gitler Mexico

Jyothish Soman India

Thomas J. Ashby

80 ×0.4

CTM

76 ×0.7

44 ×0.5

69 ×0.9

CNC

43 ×0.8

Citations per year, relative to Thomas J. Ashby Thomas J. Ashby (= 1×) peers Jyothish Soman

Countries citing papers authored by Thomas J. Ashby

Since Specialization

Citations

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

Fields of papers citing papers by Thomas J. Ashby

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas J. Ashby. 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 Thomas J. Ashby. The network helps show where Thomas J. Ashby may publish in the future.

Co-authorship network of co-authors of Thomas J. Ashby

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

All Works

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13 of 13 papers shown

Ashby, Thomas J., et al.. (2023). Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties. IEEE Transactions on Semiconductor Manufacturing. 37(1). 46–58.

D’Hondt, Ellie, Thomas J. Ashby, Imen Chakroun, Thomas Koninckx, & Roel Wuyts. (2022). Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit. SHILAP Revista de lepidopterología. 2(1). 162–162. 12 indexed citations

Sturm, Noé, Andreas Mayr, Vladimir Chupakhin, et al.. (2020). Industry-scale application and evaluation of deep learning for drug target prediction. Journal of Cheminformatics. 12(1). 26–26. 29 indexed citations

Chakroun, Imen, et al.. (2020). Using Unsupervised Machine Learning for Plasma Etching Endpoint Detection. 273–279. 3 indexed citations

Böhm, Stanislav, Jan Martinovič, Jiří Dvorský, et al.. (2018). HyperLoom. DSpace VŠB-TUO (VŠB-TUO). 1–6. 7 indexed citations

Sun, Jiangming, Nina Jeliazkova, Vladimir Chupakhin, et al.. (2017). ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. Journal of Cheminformatics. 9(1). 17–17. 133 indexed citations

Chakroun, Imen, Tom Haber, & Thomas J. Ashby. (2017). SW-SGD: The Sliding Window Stochastic Gradient Descent Algorithm. Procedia Computer Science. 108. 2318–2322. 22 indexed citations

Haber, Tom, et al.. (2017). Improving Operational Intensity in Data Bound Markov Chain Monte Carlo. Procedia Computer Science. 108. 2348–2352. 1 indexed citations

Ghysels, Pieter, Thomas J. Ashby, Karl Meerbergen, & Wim Vanroose. (2013). Hiding Global Communication Latency in the GMRES Algorithm on Massively Parallel Machines. SIAM Journal on Scientific Computing. 35(1). C48–C71. 72 indexed citations

10.

Ashby, Thomas J., et al.. (2010). Software-Based Cache Coherence with Hardware-Assisted Selective Self-Invalidations Using Bloom Filters. IEEE Transactions on Computers. 60(4). 472–483. 23 indexed citations

11.

Baert, Rogier, Erik Brockmeyer, Sven Wuytack, & Thomas J. Ashby. (2009). Exploring parallelizations of applications for MPSoC platforms using MPA. Design, Automation, and Test in Europe. 1148–1153. 12 indexed citations

12.

Mignolet, J.-Y., et al.. (2009). MPA: Parallelizing an Application onto a Multicore Platform Made Easy. IEEE Micro. 29(3). 31–39. 24 indexed citations

13.

Baert, Rogier, Erik Brockmeyer, Sven Wuytack, & Thomas J. Ashby. (2009). Exploring parallelizations of applications for MPSoC platforms using MPA. 1148–1153. 10 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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