Ken R. Duffy

4.5k total citations · 1 hit paper
150 papers, 2.7k citations indexed

About

Ken R. Duffy is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Ken R. Duffy has authored 150 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Computer Networks and Communications, 64 papers in Electrical and Electronic Engineering and 29 papers in Artificial Intelligence. Recurrent topics in Ken R. Duffy's work include Error Correcting Code Techniques (31 papers), Advanced Wireless Communication Techniques (31 papers) and Wireless Networks and Protocols (21 papers). Ken R. Duffy is often cited by papers focused on Error Correcting Code Techniques (31 papers), Advanced Wireless Communication Techniques (31 papers) and Wireless Networks and Protocols (21 papers). Ken R. Duffy collaborates with scholars based in Ireland, United States and Australia. Ken R. Duffy's co-authors include David Malone, Douglas J. Leith, Muriel Médard, Philip D. Hodgkin, Cameron Wellard, Leïla Perié, Mark R. Dowling, Catherine M. Grgicak, Rob J. de Boer and Ton N. Schumacher and has published in prestigious journals such as Science, Cell and Nature Communications.

In The Last Decade

Ken R. Duffy

137 papers receiving 2.7k citations

Hit Papers

align trajectories

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.

Modeling the 802.11 Distributed Coordination Function in Nonsaturated Heterogeneous Conditions

2007 474 citations David Malone, Ken R. Duffy et al. profile →

Peers

Ken R. Duffy

CNC

EEE

IMMUN

MB

AI

Matthew Baker United Kingdom

Youhui Zhang China

Gordon Royle Australia

Brian S. White United States

Michael D. Bond United States

Michael D. Linderman United States

David Akopian United States

Jacob Appelbaum United States

Xiaoying Jia United States

Peipei Zhou United States

Matthew Baker United Kingdom

Ken R. Duffy

1.3k ×1.0

CNC

2.0k ×1.9

EEE

688 ×1.3

IMMUN

834 ×1.6

MB

95 ×0.3

AI

Citations per year, relative to Ken R. Duffy Ken R. Duffy (= 1×) peers Matthew Baker

Countries citing papers authored by Ken R. Duffy

Since Specialization

Citations

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

Fields of papers citing papers by Ken R. Duffy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ken R. Duffy. 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 Ken R. Duffy. The network helps show where Ken R. Duffy may publish in the future.

Co-authorship network of co-authors of Ken R. Duffy

This figure shows the co-authorship network connecting the top 25 collaborators of Ken R. Duffy. A scholar is included among the top collaborators of Ken R. Duffy 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 Ken R. Duffy. Ken R. Duffy 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:

20 of 20 papers shown

1.

Médard, Muriel, et al.. (2025). GRAND-Assisted Demodulation. IEEE Journal on Selected Areas in Communications. 43(4). 1200–1213.

2.

Bansal, V., et al.. (2025). Iterative Guessing Random Additive Noise Decoder for Universal Decoding of Product Codes. IEEE Transactions on Circuits and Systems I Regular Papers. 72(12). 8067–8080.

3.

Médard, Muriel, et al.. (2025). Soft-Output (SO) GRAND and Iterative Decoding to Outperform LDPC Codes. IEEE Transactions on Wireless Communications. 24(4). 3386–3399. 6 indexed citations

4.

Sluis, Joyce van, Marjolijn N. Lub‐de Hooge, Adrienne H. Brouwers, et al.. (2024). 667P Phase I study to assess biodistribution of CB307, a trispecific Humabody targeting CD137, prostate-specific membrane antigen, and human serum albumin with 89Zr-CB307 PET. Annals of Oncology. 35. S524–S524.

5.

Ercan, Furkan, et al.. (2024). A Sub-0.8-pJ/bit Universal Soft-Detection Decoder Using ORBGRAND. IEEE Journal of Solid-State Circuits. 60(7). 2645–2659. 5 indexed citations

6.

Duffy, Ken R., et al.. (2024). Soft-Output Guessing Codeword Decoding. IEEE Communications Letters. 29(2). 328–332. 3 indexed citations

7.

Médard, Muriel, et al.. (2024). Nonorthogonal Multiple Access With Guessing Random Additive Noise Decoding-Aided Macrosymbol (GRAND-AM). IEEE Internet of Things Journal. 11(17). 28036–28049. 1 indexed citations

8.

Duffy, Ken R., et al.. (2024). Soft-Output Successive Cancellation List Decoding. IEEE Transactions on Information Theory. 71(2). 1007–1017. 3 indexed citations

9.

Cohen, Alejandro, et al.. (2023). AES as Error Correction: Cryptosystems for Reliable Communication. IEEE Communications Letters. 27(8). 1964–1968. 7 indexed citations

10.

Bresser, Kaspar, Lianne Kok, Lisa A. King, et al.. (2022). Replicative history marks transcriptional and functional disparity in the CD8+ T cell memory pool. Nature Immunology. 23(5). 791–801. 29 indexed citations

11.

Médard, Muriel, et al.. (2022). Keep the Bursts and Ditch the Interleavers. IEEE Transactions on Communications. 70(6). 3655–3667. 27 indexed citations

12.

Duffy, Ken R., et al.. (2022). Ordered Reliability Bits Guessing Random Additive Noise Decoding. IEEE Transactions on Signal Processing. 70. 4528–4542. 66 indexed citations

13.

Duffy, Ken R., et al.. (2019). Capacity-Achieving guessing random additive noise decoding. MURAL - Maynooth University Research Archive Library (National University of Ireland, Maynooth). 89 indexed citations

14.

Marchingo, Julia M., et al.. (2018). Multiplexed Division Tracking Dyes for Proliferation-Based Clonal Lineage Tracing. The Journal of Immunology. 201(3). 1097–1103. 10 indexed citations

15.

Malone, David, et al.. (2013). Decentralised Learning MACs for Collision-free \nAccess in WLANs. Maynooth University ePrints and eTheses Archive (Maynooth University). 36 indexed citations

16.

Bianchi, Giuseppe, Ken R. Duffy, Douglas J. Leith, & Vsevolod Shneer. (2012). Modeling conservative updates in multi-hash approximate count sketches. 3. 10 indexed citations

17.

Duffy, Ken R. & Giovanni Luca Torrisi. (2011). Sample Path Large Deviations of Poisson Shot Noise with Heavy-Tailed Semiexponential Distributions. Journal of Applied Probability. 48(3). 688–698. 2 indexed citations

18.

Duffy, Ken R. & Giovanni Luca Torrisi. (2011). Sample Path Large Deviations of Poisson Shot Noise with Heavy-Tailed Semiexponential Distributions. Journal of Applied Probability. 48(3). 688–698. 2 indexed citations

19.

Duffy, Ken R., et al.. (2005). How to estimate the rate function of a cumulative process. Journal of Applied Probability. 42(4). 1044–1052. 2 indexed citations

20.

Duffy, Ken R., et al.. (2005). How to estimate the rate function of a cumulative process. Journal of Applied Probability. 42(4). 1044–1052. 1 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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