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 visual–language foundation model for pathology image analysis using medical Twitter
2023263 citationsZhi Huang, Federico Bianchi et al.Nature Medicineprofile →
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
2023118 citationsFederico Bianchi, Pratyusha Kalluri et al.BOA (University of Milano-Bicocca)profile →
Optimizing generative AI by backpropagating language model feedback
202518 citationsMert Yüksekgönül, Federico Bianchi et al.Natureprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Federico Bianchi
Since
Specialization
Citations
This map shows the geographic impact of Federico Bianchi'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 Federico Bianchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Bianchi more than expected).
Fields of papers citing papers by Federico Bianchi
This network shows the impact of papers produced by Federico Bianchi. 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 Federico Bianchi. The network helps show where Federico Bianchi may publish in the future.
Co-authorship network of co-authors of Federico Bianchi
This figure shows the co-authorship network connecting the top 25 collaborators of Federico Bianchi.
A scholar is included among the top collaborators of Federico Bianchi 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 Federico Bianchi. Federico Bianchi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yüksekgönül, Mert, Federico Bianchi, Sheng Liu, et al.. (2025). Optimizing generative AI by backpropagating language model feedback. Nature. 639(8055). 609–616.18 indexed citations breakdown →
Huang, Zhi, Federico Bianchi, Mert Yüksekgönül, Thomas J. Montine, & James Zou. (2023). A visual–language foundation model for pathology image analysis using medical Twitter. Nature Medicine. 29(9). 2307–2316.263 indexed citations breakdown →
Bianchi, Federico & Pascal Hitzler. (2019). On the Capabilities of Logic Tensor Networks for Deductive Reasoning..3 indexed citations
19.
Bianchi, Federico, et al.. (2018). Type Vector Representations from Text: An Empirical Analysis.. 72–83.1 indexed citations
20.
Bianchi, Federico & Matteo Palmonari. (2017). Joint Learning of Entity and Type Embeddings for Analogical Reasoning with Entities.. BOA (University of Milano-Bicocca). 1983. 57–68.2 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.