Bio

I am a postdoctoral scholar in Department of Neurology & Neurological Sciences, Stanford University and under supervision of Dr. Zihuai He. Before that, I obtained my PhD degree in statistics under supervision of Prof. Philip L.H. Yu and Prof. Guosheng Yin in University of Hong Kong and my bachelor degrees in statistics from Renmin University of China.

My researches concentrate on preference learning, network data modeling, quantitative analysis of survival and public health data and geometry-aware statistical inference.

Honors & Awards

  • Excellent Research Award, Department of Statistics and Actuarial Science, University of Hong Kong (2022)
  • Excellent Research Award, Department of Statistics and Actuarial Science, University of Hong Kong (2021)
  • Excellent Teaching Assistant Award, Department of Statistics and Actuarial Science, University of Hong Kong (2021)
  • Honorable Mention, Interdisciplinary Contest in Modeling (2017)
  • Runner-up, Beijing-Hong Kong Data Modeling Competition (2017)
  • First Prize, Contemporary Undergraduate Mathematical Contest in Modeling (Beijing) (2016)

Professional Education

  • Ph.D., University of Hong Kong, Statistics (2022)
  • B.S., Renmin University of China, Statistics (2018)

Stanford Advisors

Contact

Additional Info

Links

All Publications

  • ANALYSIS OF PREFERENCES IN SOCIAL NETWORKS ANNALS OF APPLIED STATISTICS Gu, B., Yu, P. H. 2023; 17 (1): 89-107

    View details for DOI 10.1214/22-AOAS1617

    View details for Web of Science ID 000929003900005

  • Bayesian Log-Rank Test AMERICAN STATISTICIAN Gu, J., Zhang, Y., Yin, G. 2023

    View details for DOI 10.1080/00031305.2022.2161637

    View details for Web of Science ID 000908150700001

  • Bayesian SIR model with change points with application to the Omicron wave in Singapore SCIENTIFIC REPORTS Gu, J., Yin, G. 2022; 12 (1): 20864

    Abstract

    The Omicron variant has led to a new wave of the COVID-19 pandemic worldwide, with unprecedented numbers of daily confirmed new cases in many countries and areas. To analyze the impact of society or policy changes on the development of the Omicron wave, the stochastic susceptible-infected-removed (SIR) model with change points is proposed to accommodate the situations where the transmission rate and the removal rate may vary significantly at change points. Bayesian inference based on a Markov chain Monte Carlo algorithm is developed to estimate both the locations of change points as well as the transmission rate and removal rate within each stage. Experiments on simulated data reveal the effectiveness of the proposed method, and several stages are detected in analyzing the Omicron wave data in Singapore.

    View details for DOI 10.1038/s41598-022-25473-y

    View details for Web of Science ID 000932261400072

    View details for PubMedID 36460721

    View details for PubMedCentralID PMC9718478

  • Triangular Concordance Learning of Networks JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS Gu, J., Yin, G. 2022

    View details for DOI 10.1080/10618600.2022.2099405

    View details for Web of Science ID 000853487100001

  • Sparse concordance-based ordinal classification SCANDINAVIAN JOURNAL OF STATISTICS Fan, Y., Gu, J., Yin, G. 2022

    View details for DOI 10.1111/sjos.12606

    View details for Web of Science ID 000846860600001

  • Joint latent space models for ranking data and social network STATISTICS AND COMPUTING Gu, J., Yu, P. H. 2022; 32 (3)

    View details for DOI 10.1007/s11222-022-10106-1

    View details for Web of Science ID 000810665300001

  • Reconstructing the Kaplan-Meier Estimator as an M-estimator AMERICAN STATISTICIAN Gu, J., Fan, Y., Yin, G. 2022; 76 (1): 37-43

    View details for DOI 10.1080/00031305.2021.1947376

    View details for Web of Science ID 000678954700001

  • Crystallization Learning with the Delaunay Triangulation The 38th International Conference on Machine Learning Gu, J., Yin, G. 2021: 3854-3863
  • Analysis of ranking data WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS Yu, P. H., Gu, J., Xu, H. 2019; 11 (6)

    View details for DOI 10.1002/wics.1483

    View details for Web of Science ID 000489576600004

  • Fast Algorithm for Generalized Multinomial Models with Ranking Data The 36th International Conference on Machine Learning Gu, J., Yin, G. 2019: 2445- 2453
https://orcid.org/0000-0002-8773-9558