diff --git a/.github/workflows/jekyll.yml b/.github/workflows/jekyll.yml
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+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# Sample workflow for building and deploying a Jekyll site to GitHub Pages
+name: Deploy Jekyll site to Pages
+
+on:
+ # Runs on pushes targeting the default branch
+ push:
+ branches: ["master"]
+
+ # Allows you to run this workflow manually from the Actions tab
+ workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
+permissions:
+ contents: read
+ pages: write
+ id-token: write
+
+# Allow one concurrent deployment
+concurrency:
+ group: "pages"
+ cancel-in-progress: true
+
+jobs:
+ # Build job
+ build:
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ - name: Setup Ruby
+ uses: ruby/setup-ruby@ee2113536afb7f793eed4ce60e8d3b26db912da4 # v1.127.0
+ with:
+ ruby-version: '3.0' # Not needed with a .ruby-version file
+ bundler-cache: true # runs 'bundle install' and caches installed gems automatically
+ cache-version: 0 # Increment this number if you need to re-download cached gems
+ - name: Setup Pages
+ id: pages
+ uses: actions/configure-pages@v3
+ - name: Build with Jekyll
+ # Outputs to the './_site' directory by default
+ run: bundle exec jekyll build --baseurl "${{ steps.pages.outputs.base_path }}"
+ env:
+ JEKYLL_ENV: production
+ - name: Upload artifact
+ # Automatically uploads an artifact from the './_site' directory by default
+ uses: actions/upload-pages-artifact@v1
+
+ # Deployment job
+ deploy:
+ environment:
+ name: github-pages
+ url: ${{ steps.deployment.outputs.page_url }}
+ runs-on: ubuntu-latest
+ needs: build
+ steps:
+ - name: Deploy to GitHub Pages
+ id: deployment
+ uses: actions/deploy-pages@v3
diff --git a/AboutMe.html b/AboutMe.html
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+---
+layout: page
+---
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ I am Yongzhe 永哲 :) and a biostatistician from the City of Hope Comprehensive Cancer Center
+ with a passion for clinical and biomedical studies.
+ I am very passionate about data visualization and believe that it is a powerful tool for communicating complex information in a way
+ that is accessible to a wide range of audience.
+
+
+
+
+
Where I learned my skills...
+
+ Before I joined in the City of Hope, I earned my Master of Science in biostatistics from the University of Washington (2022),
+ where I had the privilege of being advised by Jim Hughes and
+ Helen Y. Chu.
+ During my time there, I actively participated in numerous research focused on combating the COVID-19 pandemic, spanning from
+ epidemiological studies to molecular investigations.
+ Prior to my graduate studies, I completed my Bachelor of Science in statistics at the University of California, Los Angeles (2020). At UCLA,
+ I developed a keen interest in applying advanced machine learning models to gain insights into the behavior of concrete and cement, aiming to mitigate carbon emissions.
+ I was fortunate to receive guidance from Mathieu Bauchy and
+ Hongquan Xu, who supported me in exploring this fascinating area of research.
+
+
+
What I am doing now...
+
+ I worked as a biostatistician at the City of Hope, focusing primarily on research related to women's cancers such as gynecologic cancer,
+ breast cancer, and colorectal cancer. My goal was to investigate the disparities in cancer behavior based on various social determinants,
+ such as race/ethnicity and insurance, as well as biological and molecular information, including genomics, proteomics, and cytokines.
+ Additionally, I evaluated programs/trials aimed at translating scientific knowledge into community practice to reduce and eliminate inequalities
+ in cancer outcomes. In my daily work, I frequently addressed questions like:
+
+
+ - Are there disparities in cervical cancer survival rates among different race/ethnicity groups?
+ - Which mRNA expressions are highly associated with cancer behavior, and could they serve as new biomarkers?
+ - Does the cancer intervention effectively reduce patients' burdens and improve diagnosis?
+
+
+
+ Similar cancer-related problems often arose in my work. Regardless of the specific question, statistical analysis played a crucial role in
+ providing evidence to answer scientific inquiries. I employed various statistical methods, such as survival and longitudinal models,
+ machine learning, experimental designs, and many other statistical models, integrating them to formulate effective solution pathways for each scientific question.
+ In addition to my cancer-related research, I also conduct statistical analysis in computational neuroscience to explore the relationship
+ between neuro signals and animal behaviors.
+
+
+
What I am interested…
+
+ My research journey began with the application of variable importance, such as permutation importance and Shapley values, to investigate
+ the impact of chemical compositions on the strength of concrete/cement. This experience sparked my interest in exploring variable importance for
+ both semi-parametric and non-parametric models. As I delved into high-dimensional data from respiratory infectious diseases and cancer studies,
+ which included RNA-seq, proteomics, and cytokine data, I became fascinated with understanding how variable importance can be incorporated into statistical
+ models for such data types. Given the longitudinal nature and survival-related aspects of clinical and biomedical studies, I developed a specific
+ interest in utilizing longitudinal and survival models to effectively handle high-dimensional data in practical scenarios. As I immersed myself in
+ public health studies, I regularly encountered data analysis related to population science and epidemiology. I was intrigued by the prospect of
+ adapting statistical models to accommodate data from diverse study designs and answer scientific inquiries. Regardless of the aforementioned research
+ questions, I strongly believe that data visualization plays an integral role in explanatory data analysis. As a biostatistician, I am consistently passionate
+ about enhancing data visualization techniques to suit various types of data.
+
+
+ - Theory: variable importance, longitudinal and survival analysis, high-dimensional statistics, non-parametric and semi-parametric regression, time-varying models, causal inferece in observational studies
+ - Application: biomarker discovery, infectious disease modeling, health disparity, observational and epidemiological studies, genomics data analysis, cancer, neuronal data analysis, data visualization
+
+
+ Additionally, I have been practicing Chinese calligraphy for 19 years and continue to cherish this art form : )
+
+