Skills

Statistical methodology

Proficient in using “proportional hazards model”, “generalized linear mixed model”, “joint model”, “logistic regression”, “interval censored data model”, “competing risks model”, etc.
Experience in dealing with “missing data”, using “random survival forest”, “deep neural network” [Coursera Certificate], “expectation maximization algorithm”, designing and performing “simulation study”, etc.

Skilful in R (fluent in writing codes and using packages, e.g. “lme4”, “JM”, “survival”, “ggplot2”, “lattice”, “dplyr”, “keras”, etc.) [Udemy Certificate].
Capable of building R Shiny apps and R Shiny Dashboard.
Skillful in FORTRAN (fluent in writing codes) using IMSL library.
Fluent in writing codes using C/C++ and Python.
Fluent in writing codes and data analysis using SAS macro, e.g. “JMFit”.
Fluent in writing codes and using Matlab toolbox, e.g. “Image Processing”.
Fluent in analysing data using Stata, Minitab, SPSS

Short courses/Online courses:

Completed the short course “Survival Analysis Methods for Non-Proportional Hazards” at the 3rd Stat4Onc Annual Symposium.
“Introduction to Data Science using R” (Udemy, Certificate link)
“Neural Networks and Deep Learning” (Coursera, Certificate link).

Summer Program:

Successful completion of the “UConn Statistics Biopharmaceutical Summer Academy”, organized by Boerhinger Ingelheim Pharmaceuticals, Inc. and Department of Statistics, UConn, August 06–August 24, 2018.

Software/Language:

“SAS Visual Data Mining and Machine Learning on SAS Viya: Interactive Machine Learning”, organized by Department of Statistics, UConn and SAS, May 14, 2019.