World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
42
Citations
6506
World Ranking
1810
National Ranking
774

Yun S. Song publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Yun S. Song sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 107 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Yun S. Song D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Yun S. Song sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 42 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Research.com Recognitions

  • 2008 - Fellow of Alfred P. Sloan Foundation

Overview

Yun S. Song is affiliated with the University of California, Berkeley in the United States. Their research primarily falls within the field of Biochemistry, Genetics and Molecular Biology, with a strong focus on several subfields including Molecular Biology, Genetics, Immunology, Artificial Intelligence, and Epidemiology.

The scientist's work covers multiple topics related to genomics and molecular biology. These topics include:

  • Genomics and Phylogenetic Studies
  • Single-cell and spatial transcriptomics
  • RNA and protein synthesis mechanisms
  • Machine Learning in Bioinformatics
  • Cancer Genomics and Diagnostics
  • Genetic Associations and Epidemiology
  • Genomics and Rare Diseases

Yun S. Song has published extensively, with frequent publications appearing in venues such as:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Proceedings of the National Academy of Sciences
  • Genome Biology

Some recent papers associated with Yun S. Song include:

  • Type I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19, 2021, Science Translational Medicine
  • Multi-geohazards susceptibility mapping based on machine learning-a case study in Jiuzhaigou, China, 2020, Natural Hazards
  • XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment, 2021, Science Advances
  • DNA language models are powerful predictors of genome-wide variant effects, 2023, Proceedings of the National Academy of Sciences
  • Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation, 2023, Cell

Yun S. Song has collaborated frequently with a number of co-authors, including:

  • Milind Jagota
  • Carlos Albors
  • Chengzhong Ye
  • Yun Deng
  • Chun Ye

The scientist has been recognized as a Fellow of the Alfred P. Sloan Foundation in 2008.

Best Publications

  • Robust and scalable inference of population history from hundreds of unphased whole genomes

    Jonathan Terhorst;John A Kamm;Yun S Song

  • Deep Learning for Population Genetic Inference.

    Sara Sheehan;Yun S. Song

  • Estimating Variable Effective Population Sizes from Multiple Genomes: A Sequentially Markov Conditional Sampling Distribution Approach

    Sara Sheehan;Kelley Harris;Yun S. Song

  • Efficiently inferring the demographic history of many populations with allele count data.

    Jack Kamm;Jonathan Terhorst;Richard Durbin;Yun S. Song

  • Constructing minimal ancestral recombination graphs.

    Yun S. Song;Jotun Hein

  • ECHO: A reference-free short-read error correction algorithm

    Wei-Chun Kao;Andrew H. Chan;Yun S. Song

  • BayesCall: A model-based base-calling algorithm for high-throughput short-read sequencing

    Wei-Chun Kao;Kristian Stevens;Yun S. Song

  • A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks.

    Jeffrey Chan;Valerio Perrone;Jeffrey P Spence;Paul A Jenkins

  • Efficient Computation of the Joint Sample Frequency Spectra for Multiple Populations

    John A. Kamm;Jonathan Terhorst;Yun S. Song

  • Fundamental limits on the accuracy of demographic inference based on the sample frequency spectrum.

    Jonathan Terhorst;Yun S. Song

  • Minimum recombination histories by branch and bound

    Rune B. Lyngsø;Yun S. Song;Jotun Hein

  • Inference of complex population histories using whole-genome sequences from multiple populations.

    Matthias Steinrücken;Jack Kamm;Jeffrey P Spence;Yun S Song

  • Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

    Anand Bhaskar;Y.X. Rachel Wang;Yun S. Song

  • A Simple Method for Finding Explicit Analytic Transition Densities of Diffusion Processes with General Diploid Selection

    Yun S. Song;Matthias Steinrücken

  • Open String Instantons and Relative Stable Morphisms

    Jun Li;Yun S Song

  • High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability

    Pier Francesco Palamara;Jonathan Terhorst;Yun S. Song;Alkes L. Price

  • The Key Parameters that Govern Translation Efficiency

    Dan D. Erdmann-Pham;Khanh Dao Duc;Yun S. Song

  • Descartes’ rule of signs and the identifiability of population demographic models from genomic variation data

    Anand Bhaskar;Yun S Song

  • Multi-locus Analysis of Genomic Time Series Data from Experimental Evolution

    Jonathan Terhorst;Christian Schlötterer;Yun S. Song

  • A NOVEL SPECTRAL METHOD FOR INFERRING GENERAL DIPLOID SELECTION FROM TIME SERIES GENETIC DATA.

    Matthias Steinrücken;Anand Bhaskar;Yun S. Song

  • Distortion of genealogical properties when the sample is very large.

    Anand Bhaskar;Andrew G. Clark;Yun S. Song

  • An Accurate Sequentially Markov Conditional Sampling Distribution for the Coalescent With Recombination

    Joshua S. Paul;Matthias Steinrücken;Yun S. Song

  • An efficient algorithm for statistical multiple alignment on arbitrary phylogenetic trees.

    Gerton Lunter;István Miklós;Yun S. Song;Jotun Hein

Frequent Co-Authors

Rasmus Nielsen
Rasmus Nielsen University of California, Berkeley
Jotun Hein
Jotun Hein University of Oxford
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Montgomery Slatkin
Montgomery Slatkin University of California, Berkeley
Charles H. Langley
Charles H. Langley University of California, Davis
David J. Meltzer
David J. Meltzer Southern Methodist University
Richard Villems
Richard Villems University of Tartu
Eske Willerslev
Eske Willerslev University of Copenhagen
Ene Metspalu
Ene Metspalu University of Tartu

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