Max Ward

1.0k total citations · 1 hit paper
28 papers, 506 citations indexed

About

Max Ward is a scholar working on Molecular Biology, Computer Networks and Communications and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Max Ward has authored 28 papers receiving a total of 506 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 5 papers in Computer Networks and Communications and 4 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Max Ward's work include RNA and protein synthesis mechanisms (9 papers), RNA modifications and cancer (7 papers) and RNA Research and Splicing (6 papers). Max Ward is often cited by papers focused on RNA and protein synthesis mechanisms (9 papers), RNA modifications and cancer (7 papers) and RNA Research and Splicing (6 papers). Max Ward collaborates with scholars based in Australia, United States and Germany. Max Ward's co-authors include Girish Dwivedi, Jason K. Eshraghian, Xinxin Wang, Wei Lü, Emre Neftci, Mohammed Bennamoun, Gregor Lenz, Doo Seok Jeong, Amitava Datta and David H. Mathews and has published in prestigious journals such as Nature, Science and Nucleic Acids Research.

In The Last Decade

Max Ward

24 papers receiving 482 citations

Hit Papers

Training Spiking Neural Networks Using Lessons From Deep ... 2023 2026 2024 2025 2023 50 100 150 200 250
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. Training Spiking Neural Networks Using Lessons From Deep Learning
    2023 297 citations Jason K. Eshraghian, Max Ward et al. Proceedings of the IEEE profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Max Ward Australia 10 234 121 118 108 54 28 506
Hanlin Tang United States 11 91 0.4× 121 1.0× 47 0.4× 213 2.0× 35 0.6× 37 620
Chao Sun China 18 142 0.6× 31 0.3× 227 1.9× 152 1.4× 194 3.6× 54 758
Toru Aonishi Japan 12 61 0.3× 129 1.1× 33 0.3× 201 1.9× 118 2.2× 52 436
Eyal Hulata Israel 8 71 0.3× 65 0.5× 55 0.5× 350 3.2× 245 4.5× 12 550
Gianluca Tempesti Switzerland 16 187 0.8× 398 3.3× 282 2.4× 16 0.1× 62 1.1× 66 760
Hugo de Garis United States 12 140 0.6× 421 3.5× 44 0.4× 139 1.3× 42 0.8× 78 668
J.B. Butcher United Kingdom 13 284 1.2× 157 1.3× 18 0.2× 39 0.4× 20 0.4× 38 573
F. Javier Toledo Spain 12 70 0.3× 40 0.3× 18 0.2× 52 0.5× 29 0.5× 36 297
Yuwei Cui United States 8 71 0.3× 89 0.7× 51 0.4× 261 2.4× 112 2.1× 9 372
Fatima Hashim Abbas Iraq 11 102 0.4× 24 0.2× 153 1.3× 82 0.8× 118 2.2× 30 361

Countries citing papers authored by Max Ward

Since Specialization
Citations

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

Fields of papers citing papers by Max Ward

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Max Ward

This figure shows the co-authorship network connecting the top 25 collaborators of Max Ward. A scholar is included among the top collaborators of Max Ward 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 Max Ward. Max Ward 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.
Ward, Max, et al.. (2025). JAX-RNAfold: scalable differentiable folding. Bioinformatics. 41(5). 4 indexed citations
2.
3.
Li, Nan, et al.. (2025). Machine learning reveals novel targets for both glioblastoma and osteosarcoma. Heliyon. 11(5). e42997–e42997. 1 indexed citations
4.
Ward, Max, et al.. (2025). Neck-focused Remote Photoplethysmography (rPPG): A comparative study using clinical data and the PyVHR framework. Computers in Biology and Medicine. 197(Pt A). 110956–110956.
5.
Ward, Max, et al.. (2025). Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models. Cureus. 17(3). e81097–e81097. 1 indexed citations
6.
Ward, Max, et al.. (2025). mRNA folding algorithms for structure and codon optimization. Briefings in Bioinformatics. 26(4).
7.
Schneider, Daniel, Max Ward, Timothy G. White, et al.. (2025). Mind the Gap: Social media analysis of 38,609 posts highlights discrepancies between clinical focus and patient priorities in neurosurgical care. Journal of Clinical Neuroscience. 137. 111334–111334. 1 indexed citations
8.
Ward, Max, et al.. (2024). Hardening Active Directory Graphs via Evolutionary Diversity Optimization-based Policies. UWA Profiles and Research Repository (University of Western Australia). 5(3). 1–36. 1 indexed citations
9.
Datta, Amitava, et al.. (2024). memerna: Sparse RNA folding including coaxial stacking. Journal of Molecular Biology. 437(3). 168819–168819.
10.
Dai, Ning, et al.. (2023). RNA design via structure-aware multifrontier ensemble optimization. Bioinformatics. 39(Supplement_1). i563–i571. 9 indexed citations
11.
Ward, Max, et al.. (2023). Fitness functions for RNA structure design. Nucleic Acids Research. 51(7). e40–e40. 13 indexed citations
12.
Eshraghian, Jason K., Max Ward, Emre Neftci, et al.. (2023). Training Spiking Neural Networks Using Lessons From Deep Learning. Proceedings of the IEEE. 111(9). 1016–1054. 297 indexed citations breakdown →
13.
Ward, Max, et al.. (2023). A Scalable Double Oracle Algorithm for Hardening Large Active Directory Systems. UWA Profiles and Research Repository (University of Western Australia). 993–1003. 5 indexed citations
14.
Torda, Andrew E., et al.. (2023). Differentiable partition function calculation for RNA. Nucleic Acids Research. 52(3). e14–e14. 7 indexed citations
15.
Ward, Max, et al.. (2023). Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment. Biomedical Optics Express. 14(12). 6470–6470. 6 indexed citations
16.
Guo, Mingyu, Max Ward, Aneta Neumann, Frank Neumann, & Hung T. Nguyen. (2023). Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 37(5). 5649–5656. 9 indexed citations
17.
Wise, Michael J., et al.. (2022). Deep learning models for RNA secondary structure prediction (probably) do not generalize across families. Bioinformatics. 38(16). 3892–3899. 40 indexed citations
18.
Ward, Max, et al.. (2022). Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation. Proceedings of the Genetic and Evolutionary Computation Conference. 1191–1199. 11 indexed citations
19.
Ward, Max, Amitava Datta, Michael J. Wise, & David H. Mathews. (2017). Advanced multi-loop algorithms for RNA secondary structure prediction reveal that the simplest model is best. Nucleic Acids Research. 45(14). 8541–8550. 20 indexed citations
20.
Ward, Max. (1963). Developmental patterns of adventitious sporophytes in Phlebodium aureum J. Sm.. 58(373). 377–380. 5 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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