Shuang Song 🐬
Shuang Song

Postdoctoral Researcher

Harvard University

About Me

Hi 😊! I am Shuang Song, a postdoctoral researcher mentored by Prof. Xihong Lin in the Department of Biostatistics at Harvard University. I received my Ph.D. under the joint supervision of Prof. Jun S. Liu and Prof. Lin Hou in Tsinghua University. During my Ph.D., I had the privilege of being a visiting researcher at the Big Data Institute at the University of Oxford, where I was mentored by Prof. Augustine Kong.

Before pursuing my Ph.D., I obtained my B.S. in Mathematics from Tsinghua University. I was also a visiting researcher in the Department of Biostatistics at Yale University, mentored by Prof. Hongyu Zhao.

Currently, I'm on the job market. Please don't hesitate to contact me if you are interested in my research or would like to share any ideas 🥳!

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Interests
  • Statistical Genetics / Genomics
  • Bayesian Methodology
  • High-dimensional Data Theory
  • Whole-genome Sequencing
  • AI-empowered Statistical Methods
Experience

  • Postdoctoral Researcher (2024 - Present)

    Harvard University

  • Ph.D. in Statistics (2019-2024) B.S. in Mathematics (2015-2019)

    Tsinghua University

  • Researcher (2022-2023)

    University of Oxford

  • Researcher (2018)

    Yale University

  • Algorithm Intern (2021)

    Kuang-chi Institute of Advanced Technology

📚 My Research

I am broadly interested in developing statistical and machine learning methods to advance precision medicine and improve human health. 🧬My current research focuses on whole-genome sequencing and AI-empowered statistical genetics. I am also interested in the statistical theory of high-dimensional methods and Bayesian methodologies.

Additional areas of interest include polygenic risk prediction, heritability estimation, participation bias, and multi-omics integration. I am passionate about creating scalable, interpretable tools that translate methodological advances into meaningful impact in genetics and genomics.

📖 Featured Publications
Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks
WGS

Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks

Significance: Rare variants (RV) identified through whole genome sequencing hold great promise for elucidating the genetic basis of disease onset, but existing methods for RV association testing are not well suited for time-to-event phenotypes. Here, we develop GATE-STAAR, a scalable and accurate framework integrating frailty modeling with functional annotations. We propose a rare-variant saddlepoint approximation to handle heavy censoring. Through comprehensive simulations and large-scale analysis of approximately 400 K UK Biobank participants, with replication in approximately 230 K All of Us participants, GATE-STAAR uncovers biologically meaningful RV associations while ensuring rigorous control of type I error. This framework is powerful to dissect the genetic architecture of disease onset and progression and advance precision medicine.

Participation bias in the estimation of heritability and genetic correlation
Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations
📜 Recent Publications
S Song, X Li, H Zhou, Z Li, X Lin (2026). Scalable and accurate rare variant association tests for whole genome sequencing time-to-event analysis in large biobanks. Proceedings of the National Academy of Sciences (PNAS).
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S Song, S Benonisdottir, JS Liu, A Kong (2025). Participation bias in the estimation of heritability and genetic correlation. Proceedings of the National Academy of Sciences (PNAS).
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S Song, L Wang, L Hou, JS Liu (2024). Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations. Nature Communications.
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S Song, W Jiang, Y Zhang, L Hou, H Zhao (2022). Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. The American Journal of Human Genetics.
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S Song, L Hou, JS Liu (2022). A data-adaptive Bayesian regression approach for polygenic risk prediction. Bioinformatics.
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S Song, N Shan, G Wang, X Yan, JS Liu, L Hou (2021). Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants. Bioinformatics.
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S Song, W Jiang, L Hou, H Zhao (2020). Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLOS Computational Biology.
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S Song, H Sun, JS Liu, L Hou (2022). Multi-Cell-Type Openness-Weighted Association Studies for Trait-Associated Genomic Segments Prioritization. Genes.
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W Jiang, S Song (co-first), L Hou, H Zhao (2021). A set of efficient methods to generate high-dimensional binary data with specified correlation structures. The American Statistician.
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X Yang, S Song (co-first), H Zhang (2021). Law of the Iterated Logarithm and Model Selection Consistency for Independent and Dependent GLMs. Frontiers of Mathematics in China.
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X Zhang, S Song, W Sui, L Sun, Z Wang, L Chen, A Bian, Y Zhong (2025). The predictive value of the orientation and offset of angle α and angle κ for visual outcomes after trifocal intraocular lens implantation in an Asian cohort. Graefe’s Archive for Clinical and Experimental Ophthalmology.
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X Zhang, S Song, B Chen, L Leocani, X Zhao, Y Zhong, M Pisa (2025). The Effects of Disease‐Modifying Therapies on Optic Nerve Degeneration in Multiple Sclerosis. European Journal of Neurology.
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W Jiang, S Song, L Lyu, C Chang, Y Zhao, B Wu, L Wang, M Girgenti (2023). A robust penalized-regression-based method for multivariable Mendelian randomization using GWAS summary statistics. Researchsquare.
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K Li, X Chen, S Song, L Hou, S Chen, R Jiang (2024). Cofea: correlation-based feature selection for single-cell chromatin accessibility data. Briefings in Bioinformatics.
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X Chen, S Chen, S Song, Z Gao, L Hou, X Zhang, H Lv, R Jiang (2022). Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nature Machine Intelligence.
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N Shan, Y Xie, S Song, W Jiang, Z Wang, L Hou (2021). A novel transcriptional risk score for risk prediction of complex human diseases. Genetic Epidemiology.
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W Jiang, X Zhang, S Li, S Song, H Zhao (2022). An unbiased kinship estimation method for genetic data analysis. BMC Bioinformatics.
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W Jiang, X Zhang, S Li, S Song, H Zhao (2021). Correcting statistical bias in correlation-based kinship estimators. bioRxiv.
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X Zhang, H Wang, K Zhang, J Ma, H He, S Song, E Shao, B Chen, J Yang (2024). Blood Glucose Levels Moderate the Associations Between IGF-1 Levels and Choroidal Metrics in Patients With Diabetes With Acromegaly Without Diabetic Retinopathy. Translational Vision Science & Technology.
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Z Ren, R Li, T Zhang, B Chen, C Wang, M Li, S Song, Y Xiao, B Xu, Z Liu (2021). Reduction of Human Mobility Matters during Early COVID-19 Outbreaks: Evidence from India, Japan and China. International Journal of Environmental Research and Public Health.
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🏆 Awards
  • PQG Travel Award (2026)
  • NSF Travel Award for the ICSA Applied Statistics Symposium (2025)
  • “Zhong Jiaqing” Award in Probability and Statistics (2023)
  • Outstanding Doctoral Dissertation (2024)
  • Outstanding Graduate of Beijing (2024)
  • Outstanding Graduate of Tsinghua University (2024)
  • President Scholarship (Only awarded to 20 students) (2021)
  • First Prize “RONG” Scholarship (2021)
  • “BeiGene” Paper Award for Youth Scholars, Outstanding paper (2020 & 2021)
📅 Recent & Upcoming Talks
Mar 12 2026

ILCCO

ECR session

Aug 07 2025

JSM

Contributed session📍 Nashville, Tennessee

Apr 07 2025

ICSA

Invited session📍 University of Connecticut

🔑 Software

🧬 WGS / WES

Rare-variant association tools for sequencing studies.

GATE-STAAR GATE-STAAR-RAP

🧪 Heritability / Genetic Correlation

Methods for SNP heritability and participation-bias correction.

LDER ParticipationBias

📈 Polygenic Risk Prediction

Polygenic risk score modeling and Bayesian prediction tools.

NeuPred EBPRS

🧫 Multiomics

Integrative analysis across transcriptome and omics modalities.

MTWAS OWAS OWASjoint

🧭 Mendelian Randomization

Robust multivariable Mendelian randomization inference.

MVMR-PRESS

🧰 Statistical Tools

Generate high-dimensional binary data with specified correlation structures.

CorBin
👩‍💻 Service

Reviewer:

  • Nature Communications
  • The American Journal of Human Genetics
  • npj Digital Medicine
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • BMC Bioinformatics
  • BMC Medicine
  • Scientific Reports

Teaching:

  • Teaching Assistant for Probability Theory (2021)
  • Teaching Assistant for the Science and Art of Data Analysis (2021)
  • Teaching Assistant for Mathematics History (2020 & 2021)
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