Rong Zablocki
Research Statistician (IV)
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolRong Zablocki is a research statistician with nine years of applied experience developing advanced models for longitudinal, multivariate, and functional data, especially using wearable accelerometer inputs in digital health. Based at UC San Diego, she has been a co-investigator on NIH-funded projects and builds novel Bayesian and MCMC-based methods to tackle high-dimensional, longitudinal problems in oncology, psychiatry, and physical activity research. Her toolkit centers on R and Linux cluster computing, with additional competence in Python, MatLab, SQL and large-scale simulation/genetic tools like PLINK and HapGen2. She has hands-on clinical trial experience from industry—designing SAPs, randomization and FDA submission support—which complements her academic work on adaptive testing and pathway-informed graphical models. Rong is detail-oriented and collaborative, frequently translating complex methodological innovations into reproducible analysis pipelines for interdisciplinary teams. A less obvious strength is her pattern of bridging clinical trial rigor with cutting-edge AI/ML for digital phenotyping, enabling both regulatory-quality inference and exploratory high-dimensional discovery.
9 years of coding experience
Doctor of Philosophy - PhD, Computational Statistics, Doctor of Philosophy - PhD, Computational Statistics at Claremont Graduate UniversityMaster of Public Health - MPH, Epidemiology, Master of Public Health - MPH, Epidemiology at San Diego State University
English, Chinese
Github Skills (6)
transparency6
data-storage6
reconciliation5
eda4
python4
redcap2
Programming languages (1)
Github contributions (5)
ABCD-STUDY/abcd_acs_raked_propensity
Jan 2019 - Feb 2020
Contributions:12 commits, 5 pushes, 1 branch in 1 year
rongw16/GWASH
Dec 2017 - May 2018
Contributions:6 pushes, 1 branch in 4 months
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