Thomas H. Pulliam

8.3k total citations · 3 hit papers
142 papers, 5.9k citations indexed

About

Thomas H. Pulliam is a scholar working on Computational Mechanics, Aerospace Engineering and Applied Mathematics. According to data from OpenAlex, Thomas H. Pulliam has authored 142 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 104 papers in Computational Mechanics, 49 papers in Aerospace Engineering and 29 papers in Applied Mathematics. Recurrent topics in Thomas H. Pulliam's work include Computational Fluid Dynamics and Aerodynamics (99 papers), Fluid Dynamics and Turbulent Flows (69 papers) and Gas Dynamics and Kinetic Theory (29 papers). Thomas H. Pulliam is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (99 papers), Fluid Dynamics and Turbulent Flows (69 papers) and Gas Dynamics and Kinetic Theory (29 papers). Thomas H. Pulliam collaborates with scholars based in United States, Canada and Australia. Thomas H. Pulliam's co-authors include D. CHAUSSEE, J. L. Steger, David W. Zingg, Marian Nemec, Dennis C. Jespersen, Pieter G. Buning, Paul Nghiem, Henry Lee, Venkateswaran Sankaran and Terry L. Holst and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Thomas H. Pulliam

138 papers receiving 5.5k citations

Hit Papers

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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.

A diagonal form of an implicit approximate-factorization algorithm

1981 990 citations Thomas H. Pulliam et al. profile →
Implicit Finite-Difference Simulations of Three-Dimensional Compressible Flow

1980 557 citations Thomas H. Pulliam et al. profile →
Spatial transcriptomics at subspot resolution with BayesSpace

2021 448 citations Edward Zhao, Matthew R. Stone et al. Nature Biotechnology profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Thomas H. Pulliam	4.4k	2.5k	867	473	310	142	5.9k
Xiangyu Hu	6.5k 1.5×	1.0k 0.4×	681 0.8×	179 0.4×	126 0.4×	220	7.9k
Lili Ju	1.7k 0.4×	370 0.1×	123 0.1×	148 0.3×	141 0.5×	172	4.9k
Habib N. Najm	3.4k 0.8×	1.1k 0.4×	153 0.2×	217 0.5×	1.3k 4.1×	180	7.4k
Stefan Turek	3.4k 0.8×	191 0.1×	184 0.2×	74 0.2×	89 0.3×	166	5.0k
Robert D. Russell	2.3k 0.5×	215 0.1×	554 0.6×	116 0.2×	61 0.2×	68	4.6k
Graham F. Carey	4.2k 0.9×	334 0.1×	218 0.3×	72 0.2×	226 0.7×	298	6.8k
Doyle Knight	4.4k 1.0×	2.9k 1.2×	1.4k 1.6×	87 0.2×	299 1.0×	287	5.4k
Carol S. Woodward	956 0.2×	194 0.1×	81 0.1×	337 0.7×	464 1.5×	56	3.4k
Anders Logg	1.7k 0.4×	198 0.1×	69 0.1×	238 0.5×	161 0.5×	69	4.7k
Garth N. Wells	2.2k 0.5×	235 0.1×	63 0.1×	120 0.3×	151 0.5×	79	6.0k

Countries citing papers authored by Thomas H. Pulliam

Since Specialization

Citations

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

Fields of papers citing papers by Thomas H. Pulliam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas H. Pulliam

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas H. Pulliam. A scholar is included among the top collaborators of Thomas H. Pulliam 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 H. Pulliam. Thomas H. Pulliam 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

Tachiki, Lisa, Daniel S. Hippe, Thomas H. Pulliam, et al.. (2025). Risk of disease progression after discontinuing immunotherapy in 105 patients with Merkel cell carcinoma who responded to PD-1 pathway blockade. Journal for ImmunoTherapy of Cancer. 13(8). e012123–e012123.

Zeng, Zhen, Jiajia Zhang, Shuai Li, et al.. (2025). A minimal gene set characterizes TIL specific for diverse tumor antigens across different cancer types. Nature Communications. 16(1). 1070–1070. 4 indexed citations

Miller, Natalie J., Wenwen Chen, Evan Hall, et al.. (2024). TRICK-MCC: A proof-of-concept, investigator-initiated study of combination therapy with anti–PD-1, anti–LAG-3, and anti–TIM-3 in participants with advanced or metastatic PD-(L)1 refractory Merkel cell carcinoma.. Journal of Clinical Oncology. 42(16_suppl). TPS9618–TPS9618.

Ryu, Heeju, Timothy Bi, Thomas H. Pulliam, et al.. (2024). Merkel cell polyomavirus-specific and CD39+CLA+ CD8 T cells as blood-based predictive biomarkers for PD-1 blockade in Merkel cell carcinoma. Cell Reports Medicine. 5(2). 101390–101390. 11 indexed citations

Pulliam, Thomas H., Peter H. Goff, Kimberly S. Smythe, et al.. (2024). Intratumoral STING agonist reverses immune evasion in PD-(L)1-refractory Merkel cell carcinoma: mechanistic insights from detailed biomarker analyses. Journal for ImmunoTherapy of Cancer. 12(10). e009803–e009803. 5 indexed citations

Pulliam, Thomas H., Lichen Jing, Heeju Ryu, et al.. (2024). Circulating cancer-specific CD8 T cell frequency is associated with response to PD-1 blockade in Merkel cell carcinoma. Cell Reports Medicine. 5(2). 101412–101412. 15 indexed citations

Pulliam, Thomas H., Xinyi Fan, Daniel S. Hippe, et al.. (2023). Characterization of Immunosuppressive Myeloid Cells in Merkel Cell Carcinoma: Correlation with Resistance to PD-1 Pathway Blockade. Clinical Cancer Research. 30(6). 1189–1199. 12 indexed citations

Kegerise, Michael A., et al.. (2023). Measurements and Computations of the Turbulent Corner Flow on the NASA Juncture-Flow Model with a Symmetric Wing. AIAA SCITECH 2023 Forum. 1 indexed citations

Tachiki, Lisa, Daniel S. Hippe, Evan Hall, et al.. (2023). Extended duration of treatment using reduced-frequency dosing of anti-PD-1 therapy in patients with advanced melanoma and Merkel cell carcinoma. Cancer Immunology Immunotherapy. 72(11). 3839–3850. 9 indexed citations

10.

Church, Candice D., Thomas H. Pulliam, Song Y. Park, et al.. (2022). Transcriptional and functional analyses of neoantigen-specific CD4 T cells during a profound response to anti-PD-L1 in metastatic Merkel cell carcinoma. Journal for ImmunoTherapy of Cancer. 10(9). e005328–e005328. 14 indexed citations

11.

Pulliam, Thomas H., et al.. (2022). LB1029 Correlation of merkel virus-specific CD8 T cells with response to immunotherapy in merkel cell carcinoma. Journal of Investigative Dermatology. 142(8). B36–B36. 1 indexed citations

12.

Pulliam, Thomas H., Kimberly S. Smythe, Lisa Tachiki, et al.. (2022). An intratumoral STING agonist-mediated clinical response in PD-L1-refractory MCC via an unexpected mechanism of action. Journal of Investigative Dermatology. 142(10). 2839–2839. 2 indexed citations

13.

Tachiki, Lisa, Daniel S. Hippe, Nora Alexander, et al.. (2022). Impact of duration of immunotherapy on clinical outcomes in advanced Merkel cell carcinoma patients responding to first-line immunotherapy. Journal of Investigative Dermatology. 142(10). 2840–2840. 1 indexed citations

14.

Zhao, Edward, Matthew R. Stone, Xing Ren, et al.. (2021). Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology. 39(11). 1375–1384. 448 indexed citations breakdown →

15.

Goff, Peter H., et al.. (2021). Intersection of Two Checkpoints: Could Inhibiting the DNA Damage Response Checkpoint Rescue Immune Checkpoint-Refractory Cancer?. Cancers. 13(14). 3415–3415. 16 indexed citations

16.

Pulliam, Thomas H., et al.. (2020). Polyomavirus‐driven Merkel cell carcinoma: Prospects for therapeutic vaccine development. Molecular Carcinogenesis. 59(7). 807–821. 32 indexed citations

17.

Akaike, Tomoko, et al.. (2020). 865 Immunotherapy in advanced Merkel cell carcinoma: Frequent responses but a potentially concerning recurrence rate after therapy discontinuation. Journal of Investigative Dermatology. 140(7). S113–S113. 1 indexed citations

18.

Tarabadkar, Erica S., Teresa Fu, Kristina Lachance, et al.. (2020). Narrow excision margins are appropriate for Merkel cell carcinoma when combined with adjuvant radiation: Analysis of 188 cases of localized disease and proposed management algorithm. Journal of the American Academy of Dermatology. 84(2). 340–347. 24 indexed citations

19.

Colunga, Aric, Thomas H. Pulliam, & Paul Nghiem. (2017). Merkel Cell Carcinoma in the Age of Immunotherapy: Facts and Hopes. Clinical Cancer Research. 24(9). 2035–2043. 69 indexed citations

20.

Pulliam, Thomas H., Stuart E. Rogers, & Timothy J. Barth. (1996). Practical Aspects of Krylov Subspace Iterative Methods in CFD. NASA Technical Reports Server (NASA). 14(7). 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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