Biostatistics

University of
Pittsburgh

About Me

Hi! Welcome to my webpage. I am Zhuodiao (Jordy) Kuang, a Ph.D. student in Biostatistics at the University of Pittsburgh School of Public Health, and a Visiting Student at the Carnegie Mellon University School of Computer Science. Previously, I earned my M.S. in Biostatistics from Columbia University Mailman School of Public Health, and my B.S. in Statistics from Renmin University of China. 

Research

The focus of my research is to develop statistical and deep-learning methods for the analysis of big and high-throughput biomedical data, such as electronic health records, wearable sensor signals, and genomic data. I am particularly interested in transfer learning and data integration, conformal prediction, and survival analysis, with applications to risk prediction, individualized treatment, and cognitive aging. 

My research interests are:

Survival Analysis

Survival Analysis
Transfer Learning

Transfer Learning
Deep Learning

Deep Learning
Conformal Prediction

Conformal Prediction

Currently I am working on the following applications:

•Conformal inference of individualized treatment rules under distributional shift
•Transfer learning with multi-source summary-level data for enhanced risk prediction
•Deep learning for survival analysis and wearable-sensor signals
•24-hour rest–activity rhythms and cognition in older adults

Publications & Conferences

Manuscripts

(+ denotes equal contribution)

  • Sui Z+, Kuang Z+, Tang L, Ding Y (2026+). Conformal Inference of Individualized Treatment Rules under Distributional Shift. Biostatistics. Under review.
  • Bo N+, Zeng L+, Kuang Z, Ding Y (2026+). Deep Learning in Survival Analysis: A Tutorial with Applications to Prediction and Causal Inference. Lifetime Data Analysis. Under review.
  • Wu B, Wang H, Kuang Z, Shapovalenko K (2026). Blood Pressure Estimation from PPG: A Comparative Study of Direct and ECG-Mediated Deep Learning Pipelines. arXiv:2607.23406.
  • Abbas M, Nasar Z, Kuang Z, Lu H, Rosenthal D, Butts H (2025). Addressing Food Insecurity Through Community Empowerment in a Staten Island (Richmond County) Neighborhood in New York City, Pt. 2. Journal of Community Health. Minor revisions.
  • Kuang Z, Huang Q, Daoyuan L, Gu T (2024). Fast Transfer Learning Method to Leverage Multi-Source Summary-Level Data for Enhanced Risk Prediction. Manuscript in preparation.
  • Kuang Z (2022). Transfer Learning in Brain Tumor Detection: From AlexNet to Hyb-DCNN-ResNet. SDPIT 2022 (CPCI & CNKI), ISSN 2791-0210.

Conference Presentations

  • Presenting & First Author — Leveraging Multi-Source Summary-Level Data for Enhanced Risk Prediction Through Synthetic Data. 15th International Conference on Health Policy Statistics (ICHPS), San Diego, CA, 2025.

Technical Skills

Programming & Scripting

  • R*
  • Python*
  • C / C++
  • MATLAB
  • PHP

Statistical Software

  • SAS*
  • Stata
  • SPSS
  • Excel

Data & Tools

  • MySQL
  • MongoDB
  • Git
  • LaTeX

Languages

  • English
  • Mandarin
  • Tujia
  • Spanish

* Denotes preferred language for given skills.

Selected Coursework & Awards

Biostatistics, Ph.D. & Visiting Study (Pitt / CMU)

  • Introduction to Deep Learning (Carnegie Mellon University)
  • Survival Analysis
  • Advanced Statistical Inference I & II
  • Linear Models
  • Mixed Models
  • Longitudinal & Clustered Data Analysis
  • Statistical Methods for Omics Data
  • Causal Moderation & Mediation
  • Essentials of Public Health

Biostatistics, Master of Science (Columbia)

  • Survival Analysis
  • Biostatistical Methods (I & II)
  • Principles of Epidemiology
  • Statistical Inference
  • Advanced Statistical Computing
  • Statistical Computing with SAS
  • Probability
  • Data Science

Statistics, Bachelor of Science (Renmin)

  • Advanced Algebra
  • Mathematical Analysis
  • Probability Theory
  • Real Analysis
  • Sampling Techniques
  • Stochastic Processes
  • Regression Analysis
  • Statistical Computing
  • Applied Time Series Analysis
  • Experimental Design
  • Nonparametric Statistics
  • Optimization

Big Data & Data Science, Minor (Renmin)

  • Introductory Programming
  • Database Systems
  • Machine Learning
  • Foundation of Data Science
  • Computer Algorithm and Programming Design
  • Application of Statistical Software
  • Data Science Practice

Honors & Awards

  • University of Pittsburgh Travel Fund Award, 2026
  • Chair’s Award, Columbia University Mailman School of Public Health (top 1%), 2025
  • Columbia University SCALE Travel Fund Award (top 5%), 2024
  • Outstanding Undergraduate Scholarship (top 10%), 2023
  • Baosteel Grand Prize Candidate (top 5%), 2022
  • National Honorable Mention, Mathematical Contest in Modeling (top 2%), 2021
  • Outstanding Student Scholarship (top 20%), 2021
  • Undergraduate Entrepreneurship Scholarship (Smart Data Co-founder), 2020