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Hanchao Zhang

Research Scientist
Google LLC

100 Bayview Dr, Mountain View, CA 94043

 Hanchao.Zhang@nyu.edu


About Me

I work as a Research Data Scientist at Google, where our team uses statistical methods to analyze complex strategic questions, optimize decision-making, and solve key challenges across the company. Before joining Google, I completed my Ph.D. at New York University.

Worked under the mentorship of Prof. Thaddeus Tarpey. My research involves developing statistical models that cater to high-dimensional complex data, such as functional and imaging data. This endeavor confronts two main challenges: first, defining a metric with self-consistent properties to cluster populations into subgroups based on rich and complex tensor data; second, accurately identifying subgroups that closely align with target labels of interest by developing methods like semi-supervised clustering algorithms.

Prior to joining the Ph.D. program, I completed my master’s degree in Biostatistics and Data Science from Cornell University, advised by Prof. Iván Díaz.

Outside of academia, I began my journey as a professional swimmer at the age of 5 and went on to achieve several regional and national championships. I’m also passionate about Chinese calligraphy, and my artwork has been exhibited in top galleries and museums including the National Art Museum of China (Beijing). In addition, I am also interested in diving, oil painting, and aeromodelling.

Education

2024

New York University

Ph.D. in Biostatistics
2023

Columbia University

Visiting Ph.D. Student in Statistics
2020

Johns Hopkins University

Visiting Student in Biostatistics
2018

Cornell University

M.S. in Biostatistics and Data Science
2017

Capital University of Economics and Business

B.S. in International Finance

Research Interests

  • Causal Inference and Precision Medicine: causal subpopulation identification, mediation analysis, Personalized recommendation based on treatment effect and generated modifiers of treatment effect
  • Machine Learning: fairness ML, semi-supervised learning
  • Manifold Learning and Metric Learning: positive semi-definite manifold learning and metric learning
  • Functional Data Analysis: functional regression, and clustering methods

Professional Experiences

2025

Research Data Scientist, Google

  • Apply statistical methods to analyze complex strategic questions
  • Optimize decision-making processes across company initiatives
2024

Ph.D. Research, NYU

  • Supervised by Prof. Thaddeus Tarpey

Awards

Publications and Projects

  1. arXiv
    K-Tensors: Clustering Positive Semi-Definite Matrices
    Hanchao Zhang, Thaddeus Tarpey
    arXiv (arXiv), Jun. 2023.
    Website PDF GitHub Package BibTeX arXiv.org
  2. arXiv
    On the Inference of Confounders for Mediation Analysis
    Hanchao Zhang, Thaddeus Tarpey
    arXiv (arXiv), Jun. 2024.
  3. AHI
    Open Case Studies
    Stephanie Hicks, Leah Jager, Margaret Taub, Carrie Wright, John Muschelli, Lyla Atta, Michael Breshock, Qier Meng, Alexandra Stephens, Hanchao Zhang, and etc.
    the Johns Hopkins University, and Bloomberg American Health Initiative.
    Website Project Page GitHub Contact

Talks

  1. JSM
    Common Principal Components Subspace for Positive Semi-Definie Matrices
    Hanchao Zhang, Thaddeus Tarpey
    the Joint Statistical Meeting (JSM), 2024, Portland, OR.
    Poster Code Package Program Invited Session

  2. TTHS
    K-Tensors: Clustering Postive Semi-Definite Matrices
    Hanchao Zhang, Thaddeus Tarpey
    Thomas R. Ten Have Symposium (TTHS), Harvard University, 2023, Cambridge, MA.
    Poster Code Package Program Poster Presentation

  3. ENAR
    Clustering Positive Semi-Definite Matrices: A Metric Learning Approach
    Hanchao Zhang, Thaddeus Tarpey
    the International Biometric Society Eastern North American Region (ENAR), 2023, Nashville, TN.
    Slides Code Website Invited Talk

  4. FDAWG
    Functional Data Clustering and Generated Effect Modifier on Functional and Penalized Functional Regression
    Hanchao Zhang, Thaddeus Tarpey
    the Functional Data Analysis Working Group (FDAWG), Columbia University, 2022, New York, NY.
    Slides Code Website Invited Talk

  5. JSM
    Optimal Transformations of High-Dimensional Functional Data for Clustering Methods
    Hanchao Zhang, Thaddeus Tarpey
    the Joint Statistical Meeting (JSM), 2022, Washington, D.C.
    Slides Code Website Invited Talk

  6. JSM
    Optimal Linear Transformations of Functional Data for Clustering Methods
    Hanchao Zhang, Thaddeus Tarpey
    the Joint Statistical Meeting (JSM), 2021, Online.
    Slides Code Website Invited Talk

Collaboration

Services

Journal Reviewer

Membership & Activities

Resources