About Me
Education
2022 ~ 2024
GPA: 3.86
2017 ~ 2021
GPA: 3.84
Experience
Skills
My expertise spans across diverse domains including Bioinformatics, specializing in techniques such as RNA-seq, scRNA-seq, Methylation array, and NGS. I am proficient in bioinformatics tools like GATK4, BWA, Samtools, Bedtools, FastQC, and MultiQC.
Additionally, I have extensive experience with programming languages and computational frameworks, including R, Linux, Docker, Git, Bash, and Python. My skillset also extends to AI, with a strong focus on Machine Learning and Deep Learning.
(Created using WordCloud, Matplotlib, Python)
Portfolio
This repository contains a collection of R scripts for bioinformatics and genomic data analysis. Key features include workflows for RNA-seq analysis, gene set enrichment analysis, transcriptional regulatory network analysis, and pathway analysis. It also includes specialized scripts for working with The Cancer Genome Atlas (TCGA) data and preprocessing genomic datasets. The repository provides a comprehensive toolkit for researchers aiming to perform advanced analyses of high-throughput sequencing data.
This repository contains scripts for initial processing and analysis of methylation array data, primarily utilizing the sesame package in R. Key scripts include sesame_methylation_analysis.R for processing methylation data, methy_heatmap.R for visualizing methylation patterns, and plotArg.R for generating custom plots. These scripts were developed as part of a project at National Taiwan University to streamline methylation analysis workflows.
This repository contains the final project from a transcriptomics course at National Taiwan University. It demonstrates the creation of a custom analysis workflow for single-cell RNA sequencing (scRNA-seq) data based on a published paper. Key files include final_project.R and scRNA-seq.R, which detail the analytical process, and the rendered report final_project.html. The project highlights the use of tools like loupeR and RMarkdown for documenting and visualizing results.
This repository contains assignments and exercises from the Transcriptomics course at National Taiwan University. It includes weekly homework files focusing on transcriptomics analysis (e.g., HWweek2.R, HWweek3.R) and a data visualization folder with scripts for creating heatmaps, ggplot2 visualizations, and analyzing cell viability. Additionally, a package directory provides relevant resources and documentation for deeper exploration of transcriptomics workflows and R programming fundamentals.
This repository is a comprehensive collection showcasing data science and programming skills. It includes Python basics and advanced concepts like regular expressions, unit testing, and concurrent programming, along with practical implementations of machine learning algorithms. It also features topics on object-oriented programming (OOP), data visualization using Matplotlib, and data manipulation with tools like NumPy, Pandas, and scikit-learn. This resource serves as a solid foundation for aspiring data scientists and developers, providing hands-on examples and structured learning for practical applications.
This repository focuses on fundamental concepts of computer science, including Bash scripting, Docker containerization, Git version control, and HTML development. Each folder contains practical examples and exercises to build proficiency in essential tools and technologies for software development and systems operations.
This repository is dedicated to Next-Generation Sequencing (NGS) data analysis, featuring tools and workflows for genomic studies. It includes directories for popular tools such as BCFtools, BEDtools, and SAMtools, as well as scripts for Whole Exome Sequencing (WES) analysis. The repository provides a practical script for WES practice, enabling users to effectively process and analyze genomic data, making it a valuable resource for bioinformatics professionals.
This repository focuses on deep learning concepts and applications. It includes key resources like Jupyter notebooks on neural networks and image classification, offering hands-on examples and implementations. The repository also contains curated learning materials to support in-depth understanding of deep learning fundamentals and practical applications, making it a valuable resource for anyone exploring artificial intelligence and machine learning.
This repository hosts my personal website built with HTML and CSS. It features interactive visualizations, such as word clouds, and highlights my projects and experiences. The repository is deployed using GitHub Pages and serves as a showcase of my technical skills and portfolio.
Certifications
Certified by the Examination Yuan, Taiwan, demonstrating expertise in medical laboratory science, including clinical practices, diagnostic techniques, and quality assurance. Proficient in collaborating with healthcare teams and operating advanced diagnostic equipment.
Advanced certification in genomic data analysis, including NGS data processing, DNA sequencing, bioinformatics tools (BWA, Samtools, Bedtools), and statistical modeling using command line tools and R.
This certification demonstrates proficiency in using SAS software for statistical modeling and data analysis in a business context. Key skills include hypothesis testing, linear and logistic regression, and model fitting to make data-driven decisions.
The Google Data Analytics certification showcases expertise in data cleaning, data visualization, and analysis using tools like Google Sheets, SQL, Tableau, and R. It focuses on preparing, processing, and analyzing data to drive strategic business decisions.
The Machine Learning/AI Engineer program from Codecademy provides a strong foundation in machine learning concepts, neural networks, natural language processing, and AI model deployment. It focuses on using TensorFlow and scikit-learn to build intelligent systems.
This program from Codecademy focuses on constructing and training deep learning models. It covers neural networks, convolutional networks, and recurrent networks using TensorFlow, enabling practical applications of deep learning in real-world scenarios.
This course by Codecademy equips data scientists with essential software engineering skills. It covers version control, modular code development, testing, and deployment, enabling data scientists to write clean, efficient, and scalable code for real-world applications.
This course provides a comprehensive introduction to the core concepts and tools of data science. It covers data manipulation, statistical analysis, data visualization, and fundamental programming skills, laying a solid foundation for aspiring data scientists.
I have successfully completed the R for Data Science program, which provided comprehensive training in R programming, data analysis, and data science techniques. This certification demonstrates my ability to apply data-driven approaches to solving complex problems using R.