Dmitri S. Pavlichin
- Artificial Intelligence top 10%
- Molecular Biology
- Atomic and Molecular Physics, and Optics
- Aging top 5%
- Biophysics top 10%
- Co-authors
- Hideo MabuchiTsachy WeissmanHendra I. NurdinJoseph KerckhoffDaniel HerschlagMax GreenfeldAravinthan D. T. SamuelAlbert Kao
- Topics
- Genomics and Phylogenetic Studies (4 papers)Algorithms and Data Compression (4 papers)Neural Networks and Reservoir Computing (3 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of the American Chemical SocietyPhysical Review Letters
- Partner nations
- United StatesAustraliaChina
In The Last Decade
Dmitri S. Pavlichin
20 papers receiving 400 citations
Peers
Comparison fields: 5 of 70
- Artificial Intelligence 166
- Molecular Biology 157
- Atomic and Molecular Physics, and Optics 101
- Aging 62
- Biophysics 36
Countries citing papers authored by Dmitri S. Pavlichin
This map shows the geographic impact of Dmitri S. Pavlichin'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 Dmitri S. Pavlichin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmitri S. Pavlichin more than expected).
Fields of papers citing papers by Dmitri S. Pavlichin
This network shows the impact of papers produced by Dmitri S. Pavlichin. 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 Dmitri S. Pavlichin. The network helps show where Dmitri S. Pavlichin may publish in the future.
Co-authorship network of co-authors of Dmitri S. Pavlichin
This figure shows the co-authorship network connecting the top 25 collaborators of Dmitri S. Pavlichin. A scholar is included among the top collaborators of Dmitri S. Pavlichin 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 Dmitri S. Pavlichin. Dmitri S. Pavlichin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 2 | |
| 3 | 19 | |
| 4 | 6 | |
| 5 | Approximate Profile Maximum Likelihood | 8 |
| 6 | 38 | |
| 7 | 8 | |
| 8 | 0 | |
| 9 | 17 | |
| 10 | 7 | |
| 11 | 1 | |
| 12 | 10 | |
| 13 | 2 | |
| 14 | 27 | |
| 15 | 5 | |
| 16 | 63 | |
| 17 | 0 | |
| 18 | 92 | |
| 19 | Coherent-feedback formulation of a continuous quantum error correction protocol | 2 |
| 20 | 85 |
About Dmitri S. Pavlichin
Dmitri S. Pavlichin is a scholar working on Aging, Artificial Intelligence and Biophysics, having authored 22 papers that have together received 408 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (4 papers), Algorithms and Data Compression (4 papers) and Neural Networks and Reservoir Computing (3 papers). The work is most often cited by research in Aging (62 citations), Biophysics (36 citations) and Artificial Intelligence (166 citations). Dmitri S. Pavlichin has collaborated with scholars based in United States, Australia and China. Frequent co-authors include Hideo Mabuchi, Tsachy Weissman, Hendra I. Nurdin, Joseph Kerckhoff, Daniel Herschlag, Max Greenfeld, Aravinthan D. T. Samuel, Albert Kao, Harrison W. Gabel and Damon A. Clark. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review Letters.
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.