Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of Pennsylvania 2016
• Recognition
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  Awards & Honors

  • Whitehead Scholar. Whitehead Charitable Foundation. January 2021
• Research
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  Selected Grants

  • Training Program in Bioinformatics at the Intersection of Cancer Immunology and Microbiome awarded by National Institutes of Health 2020 - 2025
  • Anti-PD1 in combination with an inhibitor of PCSK9 to increase MHC class I molecule expression on tumor cells as immunotherapy for NSCLC awarded by Department of Defense 2021 - 2024
  • Quantitative Methods for HIV/AIDS Research awarded by National Institutes of Health 2018 - 2023
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Lu, Jiarui, Pixu Shi, and Hongzhe Li. “Generalized linear models with linear constraints for microbiome compositional data.” Biometrics 75, no. 1 (March 2019): 235–44. https://doi.org/10.1111/biom.12956.
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    • Shi, Pixu, and Hongzhe Li. “A model for paired‐multinomial data and its application to analysis of data on a taxonomic tree.” Biometrics 73, no. 4 (December 2017): 1266–78. https://doi.org/10.1111/biom.12681.
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    • Fogarty, Colin B., Pixu Shi, Mark E. Mikkelsen, and Dylan S. Small. “Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies.” Journal of the American Statistical Association 112, no. 517 (January 2, 2017): 321–31. https://doi.org/10.1080/01621459.2016.1138865.
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    • Shi, Pixu, Anru Zhang, and Hongzhe Li. “Regression analysis for microbiome compositional data.” The Annals of Applied Statistics 10, no. 2 (June 1, 2016). https://doi.org/10.1214/16-aoas928.
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    • Lin, W., P. Shi, R. Feng, and H. Li. “Variable selection in regression with compositional covariates.” Biometrika 101, no. 4 (December 1, 2014): 785–97. https://doi.org/10.1093/biomet/asu031.
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    • Shi, Pixu, Yuchen Zhou, and Anru R. Zhang. “High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis.” Biometrika, n.d. https://doi.org/10.1093/biomet/asab020.
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