Guosheng Yin
Patrick S C Poon Endowed Professor in Statistics and Actuarial Science

Head of Department	(2017 – )
Patrick S C Poon Professor	(2018 – )	Endowed Professorship in Statistics and Actuarial Science
Professor	(2014 – )	Department of Statistics & Actuarial Science, University of Hong Kong
Associate Professor	(2009 – 2014)	Department of Statistics & Actuarial Science, University of Hong Kong
Associate Professor	(2008 – 2009)	Department of Biostatistics, University of Texas M.D. Anderson Cancer Center
Assistant Professor	(2003 – 2008)	Department of Biostatistics, University of Texas M.D. Anderson Cancer Center

Ph.D. in Biostatistics (2003) University of North Carolina at Chapel Hill
Fellow of Institute of Mathematical Statistics (IMS) (2021)
Ranked by Clarivate Analytics in the top 1% worldwide by citations (2015, 2019, 2021)
Fellow of American Statistical Association (ASA) (2013)
Elected Member of International Statistical Institute (ISI) (2012)
UNC-Chapel Hill Distinguished Alumni Award (2009)
Wikipedia page

Deputy Editor	(2020 – )	Contemporary Clinical Trials
Associate Editor	(2012 – 2020)	Contemporary Clinical Trials
Associate Editor	(2018 – 2021)	Japanese Journal of Statistics and Data Science
Associate Editor	(2018 – 2020)	Statistical Analysis and Data Mining
Associate Editor	(2013 – 2018)	Journal of American Statistical Association
Associate Editor	(2009 – 2015)	Bayesian Analysis

Selected Short Courses

Novel Adaptive Clinical Trial Designs for Immunotherapy	2019 International Society for Biopharmaceutical Statistics, Kyoto, Japan
Adaptive Methods for Modern Clinical Trials	2015 Joint Statistical Meeting, Seattle, USA
Adaptive Methods for Modern Clinical Trials	2014 Joint Statistical Meeting, Boston, USA
Clinical Trial Design: Overview and New Development	2012 Osaka University, Osaka, Japan

Lectures on youtube
Patrick SC Poon Endowment Professorship, 2018
National University of Singapore Institute for Mathematical Sciences, 2017, NOC Design


COVID-19 Research (AI-Driven Online COVID-19 Diagnosis)

Online COVID-19 Diagnostic System with Chest CT.

Liu, C., Cui, J., Gan, D., and Yin, G. (2021). Beyond COVID-19 diagnosis: Prognosis with hierarchical graph representation learning. International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021.

Liu, B., Gao, X., He, M., Liu, L. and Yin, G. (2020). A fast online COVID-19 diagnostic system with chest CT scans. KDD 2020 - AI For COVID-19.

Yin, G., Zhang, C. and Jin, H. (2020). Statistical issues and lessons learned from COVID-19 clinical trials with lopinavir-ritonavir and remdesivir. JMIR Public Health and Surveillance 2020;6(3):e19538 doi:10.2196/19538

Yin, G. and Jin, H. (2020). Comparison of transmissibility of coronavirus between symptomatic and asymptomatic patients: Reanalysis of the Ningbo COVID-19 data. JMIR Public Health and Surveillance 2020;6(2):e19464 doi:10.2196/19464.

Recent Papers (A Full List)

Gu, J. and Yin, G. (2021). Crystallization Learning with the Delaunay Triangulation. International Conference on Machine Learning ICML 2021 (Spotlight).

Yin, G. and Shi, H. (2021). Demystify Lindley’s Paradox by Connecting P-value and Posterior Probability. Statistics and Its Interface 14, 489–502.

Shi, H. and Yin, G. (2021). Reconnecting p-value and posterior probability under one- and two-sided tests. The American Statistician 75, 265-275.

He, B., Liu, Y., Wu, Y., Yin, G., and Zhao, X. (2020). Functional martingale residual process for high-dimensional Cox regression with model averaging. Journal of Machine Learning Research 21, 1-37.

Jiang, F., Cheng, Q., Yin, G. and Shen, H. (2020). Functional censored quantile regression. Journal of the American Statistical Association 115, 931-944.

McCaw, Z. R., Yin, G. and Wei, L. J. (2019). Using the restricted mean survival time difference as an alternative to the hazard ratio for analyzing clinical cardiovascular studies. Circulation 140, 1366–1368.

Shi, H. and Yin, G. (2019). Control of type I error rates in Bayesian sequential designs. Bayesian Analysis 14, 399–425.

Gu, J. and Yin, G. (2019). Fast algorithm for generalized multinomial models with ranking data. ICML 2019 (36th International Conference on Machine Learning). Proceedings of Machine Learning Research 97, 2445–2453.

Zhang, C. and Yin, G. (2019). Fast and stable maximum likelihood estimation for incomplete multinomial model. ICML 2019 (36th International Conference on Machine Learning). Proceedings of Machine Learning Research 97, 7463–7471.

Jiang, F., Yin, G. and Dominici, F. (2018). Bayesian model selection approach to boundary detection with non-local priors. 2018 Conference on Neural Information Processing Systems (NIPS).

Selected Publications (A Full List)

Shi, H. and Yin, G. (2018). Bayesian enhancement two-stage design for single-arm phase II clinical trials with binary and time-to-event endpoints. Biometrics 74, 1055–1064.

Dong, F. and Yin, G. (2018). Maximum likelihood estimation for incomplete multinomial data via the weaver algorithm. Statistics and Computing 28, 1095–1117.

Wang, G., Zou, C., and Yin, G. (2018). Change-point detection in multinomial data with a large number of categories. Annals of Statistics 46, 2020–2044.

Lin, R. and Yin, G. (2017). Nonparametric overdose control with late-onset toxicity in phase I clinical trials. Biostatistics 18, 180–194.

Shi, H. and Yin, G. (2017). Bayesian two-stage design for phase II clinical trials with switching hypothesis tests. Bayesian Analysis 12, 31–51.

Lin, R. and Yin, G. (2016). Bootstrap aggregating continual reassessment method for dose finding in drug-combination trials. Annals of Applied Statistics 10, 2349–2376.

Ro, K., Zou, C., Wang, Z., and Yin, G. (2015). Outlier detection for high dimensional data. Biometrika 102, 589–599.

Wu, Y., Ma, Y., and Yin, G. (2015). Smoothed and corrected score approach to censored quantile regression with measurement errors. Journal of the American Statistical Association 110, 1670–1683.

Zou, C., Yin, G., Feng, L., and Wang, Z. (2014). Nonparametric maximum likelihood approach to multiple change-point problems. Annals of Statistics 42, 970–1002.

Wu, Y. and Yin, G. (2014). Conditional quantile screening in ultrahigh-dimensional heterogeneous data. Biometrika 102, 65–76.

Liu, S., Yin, G., and Yuan, Y. (2013). Bayesian data augmentation dose finding with continual reassessment method and incomplete observations. Annals of Applied Statistics 7, 2138–2156.

Wu, Y. and Yin, G. (2013). Cure rate quantile regression for censored data with a survival fraction. Journal of the American Statistical Association 108, 1517–1531.

Yin, G., Chen, N., and Lee, J. J. (2012). Phase II trial design with Bayesian adaptive randomization and predictive probability. Journal of Royal Statistical Society C 61, 219–235.

Lee, J. J., Chen, N., and Yin, G. (2012). Worth adapting? Revisiting the usefulness of outcome-adaptive randomization. Clinical Cancer Research 18, 4498–4507.

Yuan, Y. and Yin, G. (2011). On the usefulness of outcome-adaptive randomization. Journal of Clinical Oncology 29, e390–e392.

Yuan, Y. and Yin, G. (2011). Bayesian Phase I/II adaptively randomized oncology trials with combined drugs. Annals of Applied Statistics 5, 924–942.

Yuan, Y. and Yin, G. (2011). Robust EM continual reassessment method in oncology dose finding. Journal of the American Statistical Association 106, 818–831.

Yuan, Y. and Yin, G. (2011). Dose-response curve estimation: A semiparametric mixture approach. Biometrics 67, 1543–1554.

Yuan, Y. and Yin, G. (2010). Bayesian quantile regression for longitudinal studies with nonignorable missing data. Biometrics 66, 105–114.

Yin, G. and Yuan, Y. (2009). A latent contingency table approach to dose finding for combinations of two agents. Biometrics 65, 866–875.

Li, H. and Yin, G. (2009). Generalized method of moments for linear regression with clustered failure time data. Biometrika 96, 293–306.

Yin, G. and Yuan, Y. (2009). Bayesian model averaging continual reassessment method in phase I clinical trials. Journal of the American Statistical Association 104, 954–968.

Yin, G. (2009). Bayesian generalized method of moments (with discussion). Bayesian Analysis 4, 191–208; and Rejoinder, 217–222.

Yin, G. and Yuan, Y. (2009). Bayesian dose finding in oncology for drug combinations by copula regression. Journal of Royal Statistical Society C 58, 211–224.

Yin, G., Li, H., and Zeng, D. (2008). Partially linear additive hazards regression with varying coefficients. Journal of the American Statistical Association 103, 1200–1213.

Yin, G., Zeng, D., and Li, H. (2008). Power-transformed linear quantile regression with censored data. Journal of the American Statistical Association 103, 1214–1224.

Ma, Y. and Yin, G. (2008). Cure rate model with mismeasured covariates under transformation. Journal of the American Statistical Association 103, 743–756.

Cong, X., Yin, G., and Shen, Y. (2007). Marginal analysis of correlated failure time data with informative cluster sizes. Biometrics 63, 663–672.

Ji, Y., Li, Y., and Yin, G. (2007). Bayesian dose finding in phase I clinical trials based on a new statistical framework. Statistica Sinica 17, 531–547.

Yin, G., Li, Y., and Ji, Y. (2006). Bayesian dose-finding in phase I/II trials using toxicity and efficacy odds ratio. Biometrics 62, 777–784.

Zeng, D., Yin, G., and Ibrahim, J. (2006). Semiparametric transformation models for survival data with a cure fraction. Journal of the American Statistical Association 101, 670–684.

Yin, G. (2005). Bayesian cure rate frailty models with application to a root canal therapy study. Biometrics 61, 552–558.

Yin, G. and Ibrahim, J. (2005). A general class of Bayesian survival models with zero and non-zero cure fractions. Biometrics 61, 403–412.

Zeng, D., Yin, G., and Ibrahim, J. (2005). Inference for a class of transformed hazard models. Journal of the American Statistical Association 100, 1000–1008.

Zeng, D., Lin, D. Y., and Yin, G. (2005). Maximum likelihood estimation in proportional odds model with random effects. Journal of the American Statistical Association 100, 470–483.

Yin, G. and Shen, Y. (2005). Adaptive design and estimation in randomized clinical trials with correlated observations. Biometrics 61, 362–369.

Yin, G. and Ibrahim, J. (2005). A class of Bayesian shared gamma frailty models with multivariate failure time data. Biometrics 61, 209–217.

Yin, G. and Cai, J. (2005). Quantile regression models with multivariate failure time data. Biometrics 61, 152–162.

Yin, G. and Ibrahim, J. (2005). Cure rate models: a unified approach. Canadian Journal of Statistics 33, 559–570.

Yin, G. and Cai, J. (2004). Additive hazards model for multivariate failure time data. Biometrika 91, 801–818.