Mauro Poggio

1.6k total citations · 1 hit paper
6 papers, 1.1k citations indexed

About

Mauro Poggio is a scholar working on Immunology, Electrical and Electronic Engineering and Molecular Biology. According to data from OpenAlex, Mauro Poggio has authored 6 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Immunology, 3 papers in Electrical and Electronic Engineering and 2 papers in Molecular Biology. Recurrent topics in Mauro Poggio's work include Immune Cell Function and Interaction (3 papers), Advanced Memory and Neural Computing (3 papers) and Neural dynamics and brain function (2 papers). Mauro Poggio is often cited by papers focused on Immune Cell Function and Interaction (3 papers), Advanced Memory and Neural Computing (3 papers) and Neural dynamics and brain function (2 papers). Mauro Poggio collaborates with scholars based in United States and Italy. Mauro Poggio's co-authors include Lawrence Fong, Ursula E. Lang, Chien-Chun Steven Pai, Robert Blelloch, Cassandra D. Belair, Anthony Chang, Elizabeth Montabana, Tianyi Hu, Qi Fu and Fernando Corinto and has published in prestigious journals such as Cell, Cancer Research and Biophysical Journal.

In The Last Decade

Mauro Poggio

6 papers receiving 1.1k citations

Hit Papers

align trajectories sort by overperformance

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.

Suppression of Exosomal PD-L1 Induces Systemic Anti-tumor Immunity and Memory

2019 1.1k citations Mauro Poggio, Tianyi Hu et al. Cell profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mauro Poggio United States	3	780	439	366	345	193	6	1.1k
Anthony Chang United States	2	780 1.0×	432 1.0×	361 1.0×	345 1.0×	193 1.0×	4	1.1k
Tianyi Hu United States	6	945 1.2×	460 1.0×	437 1.2×	428 1.2×	197 1.0×	10	1.3k
Chien-Chun Steven Pai United States	8	835 1.1×	572 1.3×	460 1.3×	367 1.1×	198 1.0×	9	1.3k
Malgorzata Anna Zal United States	10	336 0.4×	563 1.3×	246 0.7×	264 0.8×	87 0.5×	26	959
Simon Völkl Germany	23	340 0.4×	461 1.1×	392 1.1×	80 0.2×	111 0.6×	58	1.0k
Hoa Truong Netherlands	12	453 0.6×	236 0.5×	274 0.7×	163 0.5×	120 0.6×	18	961
Gerald E. Archer United States	14	357 0.5×	545 1.2×	629 1.7×	111 0.3×	105 0.5×	28	1.3k
Niko P. Bretz Germany	13	897 1.1×	273 0.6×	286 0.8×	457 1.3×	44 0.2×	14	1.2k
Taylor A. Black United States	10	579 0.7×	71 0.2×	441 1.2×	466 1.4×	188 1.0×	19	1.0k
Stefanie Derzsi United Kingdom	5	397 0.5×	169 0.4×	552 1.5×	181 0.5×	256 1.3×	5	1.0k

Countries citing papers authored by Mauro Poggio

Since Specialization

Citations

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

Fields of papers citing papers by Mauro Poggio

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mauro Poggio

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

6 of 6 papers shown

1.

Benjamin, Joel S., Abigail Jarret, Shashank Bharill, et al.. (2024). Abstract 2375: 23ME-01473, a novel anti-ULBP6/2/5 monoclonal antibody, reinvigorates anti-tumor NK cell function through NKG2D and FcγRIIIa activation. Cancer Research. 84(6_Supplement). 2375–2375. 1 indexed citations

2.

Fang, Xin, Cristina Melero, Caroline Bonnans, et al.. (2023). 23ME-00610, a genetically informed, first-in-class antibody targeting CD200R1 to enhance antitumor T cell function. OncoImmunology. 12(1). 2217737–2217737. 9 indexed citations

3.

Poggio, Mauro, Tianyi Hu, Chien-Chun Steven Pai, et al.. (2019). Suppression of Exosomal PD-L1 Induces Systemic Anti-tumor Immunity and Memory. Cell. 177(2). 414–427.e13. 1050 indexed citations breakdown →

4.

Poggio, Mauro, et al.. (2018). Supervised neural networks with memristor binary synapses. International Journal of Circuit Theory and Applications. 46(1). 221–233. 26 indexed citations

5.

Poggio, Mauro & Fernando Corinto. (2015). Nanopore ionic memristive effects. 1–2. 2 indexed citations

6.

Poggio, Mauro & Luke P. Lee. (2014). Memristor Neural Model for Alzheimer Disease. Biophysical Journal. 106(2). 795a–795a. 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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