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 guide to deep learning in healthcare
20182.4k citationsAndre Esteva, Bharath Ramsundar et al.Nature Medicineprofile →
MoleculeNet: a benchmark for molecular machine learning
20171.7k citationsZhenqin Wu, Bharath Ramsundar et al.Chemical Scienceprofile →
Low Data Drug Discovery with One-Shot Learning
2017514 citationsHan Altae-Tran, Bharath Ramsundar et al.ACS Central Scienceprofile →
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
2017358 citationsBowen Liu, Bharath Ramsundar et al.ACS Central Scienceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Bharath Ramsundar
Since
Specialization
Citations
This map shows the geographic impact of Bharath Ramsundar'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 Bharath Ramsundar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bharath Ramsundar more than expected).
Fields of papers citing papers by Bharath Ramsundar
This network shows the impact of papers produced by Bharath Ramsundar. 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 Bharath Ramsundar. The network helps show where Bharath Ramsundar may publish in the future.
Co-authorship network of co-authors of Bharath Ramsundar
This figure shows the co-authorship network connecting the top 25 collaborators of Bharath Ramsundar.
A scholar is included among the top collaborators of Bharath Ramsundar 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 Bharath Ramsundar. Bharath Ramsundar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Esteva, Andre, Bharath Ramsundar, Volodymyr Kuleshov, et al.. (2018). A guide to deep learning in healthcare. Nature Medicine. 25(1). 24–29.2350 indexed citations breakdown →
8.
Ramsundar, Bharath & Reza Bosagh Zadeh. (2018). TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning. CERN Document Server (European Organization for Nuclear Research).53 indexed citations
Feinberg, Evan N., Brooke E. Husic, Yang Li, et al.. (2018). Spatial Graph Convolutions for Drug Discovery.2 indexed citations
11.
Altae-Tran, Han, et al.. (2017). Low Data Drug Discovery with One-Shot Learning. ACS Central Science. 3(4). 283–293.514 indexed citations breakdown →
12.
Wu, Zhenqin, Bharath Ramsundar, Evan N. Feinberg, et al.. (2017). MoleculeNet: a benchmark for molecular machine learning. Chemical Science. 9(2). 513–530.1696 indexed citations breakdown →
13.
Ramsundar, Bharath, Bowen Liu, Zhenqin Wu, et al.. (2017). Is Multitask Deep Learning Practical for Pharma?. Journal of Chemical Information and Modeling. 57(8). 2068–2076.183 indexed citations
14.
Liu, Bowen, Bharath Ramsundar, Joseph Gomes, et al.. (2017). Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models. ACS Central Science. 3(10). 1103–1113.358 indexed citations breakdown →
McGibbon, Robert T., Matthew P. Harrigan, Bharath Ramsundar, et al.. (2016). msmbuilder: MSMBuilder 3.5. Zenodo (CERN European Organization for Nuclear Research).1 indexed citations
17.
McGibbon, Robert T., Matthew P. Harrigan, Bharath Ramsundar, et al.. (2016). msmbuilder: MSMBuilder 3.4. Zenodo (CERN European Organization for Nuclear Research).2 indexed citations
18.
Swaminathan, Sundararaman, et al.. (2014). NVMKV: a scalable and lightweight flash aware key-value store. 8–8.33 indexed citations
Li, Lei, Bharath Ramsundar, & Stuart Russell. (2013). Dynamic Scaled Sampling for Deterministic Constraints. International Conference on Artificial Intelligence and Statistics. 397–405.4 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.