Russell Kunes

1.8k total citations
4 papers, 36 citations indexed

About

Russell Kunes is a scholar working on Molecular Biology, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Russell Kunes has authored 4 papers receiving a total of 36 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Molecular Biology, 2 papers in Artificial Intelligence and 1 paper in Pulmonary and Respiratory Medicine. Recurrent topics in Russell Kunes's work include Reinforcement Learning in Robotics (1 paper), Advanced Text Analysis Techniques (1 paper) and Computational and Text Analysis Methods (1 paper). Russell Kunes is often cited by papers focused on Reinforcement Learning in Robotics (1 paper), Advanced Text Analysis Techniques (1 paper) and Computational and Text Analysis Methods (1 paper). Russell Kunes collaborates with scholars based in United States, Sweden and Germany. Russell Kunes's co-authors include Dana Pe’er, Tal Nawy, Thomas Walle, Max Land, Minjun Li, Ján Remšík, Tejus Bale, Chrysothemis C. Brown, Xinran Tong and Doron Haviv and has published in prestigious journals such as Nature, Nature Biotechnology and Digital Access to Scholarship at Harvard (DASH) (Harvard University).

In The Last Decade

Russell Kunes

4 papers receiving 36 citations

Peers

Countries citing papers authored by Russell Kunes

Since Specialization

Citations

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

Fields of papers citing papers by Russell Kunes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Russell Kunes

This figure shows the co-authorship network connecting the top 25 collaborators of Russell Kunes. A scholar is included among the top collaborators of Russell Kunes 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 Russell Kunes. Russell Kunes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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4 of 4 papers shown

#	Work	Indexed citations
1	Interferon-γ orchestrates leptomeningeal anti-tumour response 2025 ·Nature ·Ján Remšík, Xinran Tong, Russell Kunes, Minjun Li, (unknown), (unknown), (unknown), (unknown), (unknown), Uğur Sener, Jessica Wilcox, (unknown), (unknown), Tejus Bale, Ronan Chaligné, Joseph C. Sun, Chrysothemis C. Brown, Dana Pe’er, Adrienne Boire	8
2	Supervised discovery of interpretable gene programs from single-cell data 2023 ·Nature Biotechnology ·Russell Kunes, Thomas Walle, Max Land, Tal Nawy, Dana Pe’er	26
3	Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping 2023 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Russell Kunes, (unknown), Max Land, Doron Haviv, Dana Pe’er, Simon Tavaré	1
4	Prediction Focused Topic Models via Feature Selection 2020 ·Digital Access to Scholarship at Harvard (DASH) (Harvard University) ·(unknown), Russell Kunes, Finale Doshi‐Velez	1

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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