About me

“It is absolutely imperative that every human being’s freedom and human rights are respected, all over the world.”– Jóhanna Sigurðardóttir

I am an Assistant Professor with the Artificial Intelligence Initiative in the Department of Biostatistics, University of Florida. I obtained my Ph.D. degree in statistics from the University of Wisconsin-Madison in 2018. After graduation, I briefly worked as a Data Scientist at Google. Then, I joined Fred Hutchinson Cancer Research Center as a post-doctoral research fellow working on biostatistics methodology and applications.

My research topics focus on treatment recommendations based on patient-level information, identifying signals from high-dimensional data, and other novel machine learning techniques with applications to biomarker identification, cancer surveillance, and digital health. My research goal is to realize data-driven healthcare decision-making through innovative methods combining statistical and machine learning techniques.

Address: 2004 Mowry Road, 5th Floor CTRB, Gainesville, FL 32611

Email: muxuan.liang@ufl.edu

My profile at Google Scholar, Research Gate

Interested students please feel free to contact me.

Education

  • Ph.D in Statistics, Department of Statistics, University of Wisconsin-Madison, 2014 - 2018

  • B.S. in Mathematics and applied mathematics, Tsinghua University, 2010 - 2014

Work Experience

  • Post-doctoral Fellow at Fred Hutchinson Cancer Research Center, 2019 - 2022

  • Quantitative Analyst at Google, 2018 - 2019

  • Summer Intern at Eli Lilly & Company, 2017

Service

  • Reviewer: Statistics in Medicine, Biometrics, Journal of Nonparametric Statistics, IEEE Robotics and Automation Letters, Journal of Machine Learning Research, Journal of the American Statistical Association, Journal of the Royal Statistical Society: Series B, Annals of Statistics.

  • 2022 ICSA Student Paper Award Committee

  • 2021 ICSA Student Paper Award Committee

Selected Talks

  • "Efficient Surrogate-Assisted Inference for Patient-Reported Outcome with Complex Missing Mechanisms."

Invited Talk, Joint Statistical Meetings 2022, Washington D.C., 08/2022.

  • "Statistical Inference of Decision Rules under a Non-differentiable Surrogate Loss in a General Classification Framework."

ENAR Spring Meeting 2022 (online), 03/2022.

Invited Talk, IISE, University of Florida, 11/2021.

  • “Indirect and Direct Learning of Optimal Individualized Treatment Rule." (slides)

Invited Talk, Department of Biostatistics, University of Florida, 09/2021.

Invited Talk, College of Health Solutions, Arizona State University, 05/2021.

Invited Talk, Department of Statistics, Purdue University, 03/2021.

Invited Talk, Biostatistics Department, MD Anderson Cancer Center, 02/2021.