Yun S. Song

Biography

Yun S. Song is a professor of EECS and Statistics working at the interface of AI/ML, statistics, and biology.
He currently serves as Director of the Center for Computational Biology and Co-Director of the Bakar Computational Biomedicine Institute. He is an IGI Investigator and a core member of the UCSF/UC Berkeley Joint Program in Computational Precision Health and the Center for Theoretical & Evolutionary Genomics.

His honors and awards include NIH Pathway to Independence Award K99/R00 (2006), Alfred P. Sloan Research Fellowship (2008), Packard Fellowship for Science and Engineering (2008), NSF CAREER Award (2009), Jim and Donna Gray Faculty Award for Excellence in Undergraduate Teaching (2013), Miller Research Professorship (2014), Math+X Simons Chair (2015), and Chan Zuckerberg Biohub Investigator Award (2017).

Education

2001, PhD, Physics, Stanford University

1997, B.S., Mathematics, MIT

1996, B.S., Physics, MIT

Research Areas

Biosystems & Computational Biology (BIO)

Artificial Intelligence (AI)

Theory (THY)

Applied Probability and Statistics

Research Centers

Berkeley Artificial Intelligence Research Lab (BAIR)

Center for Computational Biology (CCB)

G. Benegas, C. Albors, A. Aw, C. Ye, and Y. S. Song, "A DNA language model based on multispecies alignment predicts the effects of genome-wide variants," Nature Biotechnology, vol. 43, pp. 1960-1965, Dec. 2025.
Y. Deng, R. Nielsen, and Y. S. Song, "Robust and Accurate Bayesian Inference of Genome-Wide Genealogies for Hundreds of Genomes," Nature Genetics, vol. 57, pp. 2124–2135, Sep. 2025.
G. Benegas, C. Ye, C. Albors, C. Li, and Y. S. Song, "Genomic language models: opportunities and challenges," Trends in Genetics, vol. 41, no. 4, pp. 286-302, April 2025.
S. Prillo, W. Wu, and Y. S. Song, "Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction," in Advances in Neural Information Processing Systems (NeurIPS 2024), 2024.
G. Benegas, S. Batra, and Y. S. Song, "DNA language models are powerful predictors of genome-wide variant effects," PNAS, vol. 120, no. 44, pp. e2311219120, Oct. 2023.
C. Ye, C. Albors, R. Rastogi, K. Antoine, N. Ioannidis, M. Jagota, and Y. S. Song, "Cross-protein transfer learning substantially improves disease variant prediction," Genome Biology, vol. 24, no. 182, Aug. 2023.
S. Prillo, Y. Song, P. Boyeau, X. Li, P. Chen, and Y. S. Song, "CherryML: Scalable maximum likelihood estimation of phylogenetic models," Nature Methods, vol. 20, pp. 1232-1236, June 2023.
D. Erdmann-Pham, K. Dao Duc, and Y. S. Song, "The key parameters that govern translation efficiency," Cell Systems, vol. 10, no. 2, pp. 183-192.e6, Jan. 2020.
R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, P. Chen, J. F. Canny, P. Abbeel, and Y. S. Song, "Evaluating Protein Transfer Learning with TAPE," in Advances in Neural Information Processing Systems (NeurIPS), Vol. 32, 2019.
J. Spence and Y. S. Song, "Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations," Science Advances, vol. 5, no. 10, pp. eaaw9206, Oct. 2019.
M. Wang, J. Fischer, and Y. S. Song, "Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition," Annals of Applied Statistics,, vol. 13, no. 2, pp. 1103-1127, June 2019.
J. Chan, V. Perrone, J. Spence, P. Jenkins, S. Mathieson, and Y. S. Song, "A likelihood-free inference framework for population genetic data using exchangeable neural networks," in Advances in Neural Information Processing Systems (NeurIPS), 2018.
P. Palamara, J. Terhorst, Y. S. Song, and A. Price, "High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability," Nature Genetics, vol. 50, pp. 1311-1317, Aug. 2018.
K. Dao Duc and Y. S. Song, "The impact of ribosomal interference, codon usage, and exit tunnel interactions on translation elongation rate variation," PLoS Genetics, vol. 14, no. 1, pp. e1007166, Jan. 2018.
M. Wang, K. Dao Duc, J. Fischer, and Y. S. Song, "Operator norm inequalities between tensor unfoldings on the partition lattice," Linear Algebra and its Applications, vol. 520, pp. 44-66, May 2017.
J. Terhorst, J. A. Kamm, and Y. S. Song, "Robust and scalable inference of population history from hundreds of unphased whole genomes," Nature Genetics, vol. 49, pp. 303-309, Jan. 2017.
S. Sheehan and Y. S. Song, "Deep learning for population genetic inference," PLoS Comput Biol, vol. 12, no. 5, pp. e1004845, March 2016.
A. Bhaskar, Y. X. R. Wang, and Y. S. Song, "Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data," Genome Research, vol. 25, no. 2, pp. 268-279, 2015.
J. Terhorst and Y. S. Song, "Fundamental limits on the accuracy of demographic inference based on the sample frequency spectrum," PNAS, vol. 112, no. 25, pp. 7677-7682, June 2015.
M. Steinrücken, A. Bhaskar, and Y. S. Song, "A novel spectral method for inferring general diploid selection from time series genetic data," Annals of Applied Statistics, vol. 8, no. 4, pp. 2203-2222, 2014.

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Yun S. Song: Faculty Home Page

Yun S. Song

Professor

Contact Information

Links

Research Support

Biography

Education

Research Areas

Research Centers

Selected Publications

Selected Honors and Awards