Sidi Wang | Biostatistics

Sidi Wang, PhD

I work as a Mathematical Statistician at the Food and Drug Administration.

I was advised by Professor Kelley Kidwell. My research interests lie broadly in Bayesian statistics and small sample clinical trials.
I received my BSc in Economics and Finance (with specialization in maths and statistics) from the University College Dublin in 2016, my MSc in Business Analytics from the National University of Singapore in 2017, and my MSc and PhD in Biostatistics from the University of Michigan in 2021 and 2024.

snSMART: An R Package for Small Sample, Sequential, Multiple Assignment, Randomized Trial Data Analysis.
Sidi Wang, Fang Fang, Roy Tamura, Thomas Braun and Kelley Kidwell (2024)
Journal of Open Source Software, 9(101), 6971

Association between nociplastic pain and premature endocrine therapy discontinuation in breast cancer patients.
E Joyce, G Carr, S Wang, CM Brummett, KM Kidwell, NL Henry
Breast Cancer Research and Treatment 197 (2), 397-404

Small Sample, Sequential, Multiple Assignment, Randomized Trial Design and Analysis.
S Wang, T Braun, R Tamura, KM Kidwell
Handbook of Statistical Methods for Precision Medicine (pp. 36-53). Chapman and Hall/CRC

Developed and implemented a linear model based on certain heritability assumptions for the prediction of complex traits
Wrote the core algorithm in C++
Evaluated model prediction precision against a few other existing methods by using UK Biobank data

Developed and implemented a hierarchical Bayesian model for analyzing reports from large databases for passive surveillance of drug-related adverse events
Implemented EM algorithm for estimating hyperparameters
Evaluated fitted models with simulation studies
Developed an R-package for all the methods studied in the project

with Dr. Wang Tong

Led a group of six and performed data visualization and primary analysis on 10-year sales data of a global skincare company
Completed predictive data analytics with several machine learning models

with Dr. Liu Kang

Led a team of 4 research assistants, collected and prepared data, translated thesis from Chinese to English

Research Project: Predicting the landmark event and benefit of Overall Survival (OS) from available Progression Free Survival (PFS) Information in Metastatic Cancer Trials

Built an end-to-end supplies forecasting model which connected and optimized the overall budgeting, supply chain planning, and revenue tracking processes of the newly established PageWide Industrial (PWI) business
Developed a fully automated pricing tool for the supplies, which shortened the turnaround time from hours to a few minutes

Took full ownership of the attrition model overhaul in R. Utilized machine learning algorithms (XGBoost) for employee attrition analysis and prediction
Achievements: successfully identified the failures of the previous model. Increased model sensitivity from 60% to 85%. Project Report: Human Resources Analytics: Machine Learning in Predicting Future Resignations, supervised by Prof. Quek Ser Aik (this project was covered by Channel NewsAsia)

Prepared and conducted audit engagements for fund companies and one of the top four British & Irish banks; Evaluated bank loan performance and borrower company operation; Reviewed and tested company risk taking
Reported directly to Audit Director as an intern; I was awarded twice as “Deloitte Dots” receiver, graded as “High Performance” by the firm, and was offered a full-time contract by the time I finished my internship contract