Danila Sinopalnikov

1.2k citations
3 papers · 29 · h-index 2

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Semantic Web and Ontologies
    • Geochemistry and Geologic Mapping
    • Domain Adaptation and Few-Shot Learning
    • Reinforcement Learning in Robotics
    • Advanced Graph Neural Networks
    • Multimodal Machine Learning Applications

Papers in

Journals
Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (2 papers)

In The Last Decade

Danila Sinopalnikov

2 papers receiving 27 citations

Peers

Danila Sinopalnikov
Comparison fields: 5 of 10
  • Artificial Intelligence 28
  • Computer Vision and Pattern Recognition 14
  • Cultural Studies 1
  • Information Systems 2
  • Control and Systems Engineering 2
Replace Karan Goel with:
Karan Goel United States
Nikola Momchev United States
Ozan Çağlayan United Kingdom
Shreyas Saxena Israel
Avital Oliver United States
David Ha Japan
Carolin Haas Germany
Sanmi Koyejo United States
Chris Waites United States
Nayan Singhal United States
Danila Sinopalnikov relative to Karan Goel United States Karan Goel's profile →
Citations per field
00.5×1.5×
Karan Goel · 1×
Citations per year

Countries citing papers authored by Danila Sinopalnikov

Since Specialization
Citations

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

Fields of papers citing papers by Danila Sinopalnikov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 9 scholars most cited alongside Danila Sinopalnikov, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Danila Sinopalnikov Line = papers co-authored together Danila Sinopalnikov links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown

About Danila Sinopalnikov

Danila Sinopalnikov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 29 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers), Multimodal Machine Learning Applications (2 papers) and Text Readability and Simplification (1 paper). The work is most often cited by research in Artificial Intelligence (28 citations), Computer Vision and Pattern Recognition (14 citations), Cultural Studies (1 citation), Information Systems (2 citations) and Control and Systems Engineering (2 citations). Danila Sinopalnikov has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Nikola Momchev, Dmitry Tsarkov, Nathan Scales, Nathanael Schärli, Xiao Wang, Marc van Zee, Olivier Bousquet, Hylke Buisman and Daniel Keysers. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).

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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