About Me
Biography
I am a computational and molecular biologist with a PhD from the University of Arizona, specializing in bioinformatics, genomics, and transcriptomics. My expertise lies in multi-omics data analysis, including RNA-seq, ATAC-seq, and ChIP-seq, as well as in developing computational algorithms and pipelines to uncover gene regulatory networks. Proficient in R, Python, Linux, HPC and Cloud Computing. I bring strong technical skills to tackle complex biological questions. My research interests span data science, machine learning, and the integration of multi-omics data, aiming to advance our understanding of biological systems through innovative computational approaches.
Interests
Data Science, Machine/Deep Learning, Computational Biology/Bioinformatics, Cloud Computing.
Education
PhD - University of Arizona
Master - China Agricutural University
Bachelor - Henan Agricutural University
Data Science
Programming Languages: Proficient in Python, R, and SQL for data analysis, visualization, and database management.
Machine Learning: Experienced with frameworks such as TensorFlow and scikit-learn for predictive modeling and advanced analytics.
Data Manipulation: Skilled in tools like pandas, NumPy, and dplyr for efficient data wrangling and processing.
Data Visualization: Expertise in libraries such as Matplotlib, Seaborn, and ggplot2 for creating clear and impactful visualizations.
Bioinformatics Tools: Adept in Bioconductor, DESeq2, and Seurat for genomics and multi-omics data analysis.
Workflow Management: Proficient in using Snakemake and similar tools to design reproducible data analysis pipelines.
Operating Systems: Advanced knowledge of Linux environments for high-performance computing and handling large-scale datasets.
Big Data and Database Management: Experience with SQL and other database tools to efficiently query and manage biological data.
Version Control: Proficient in Git and GitHub for version control, collaboration, and maintaining reproducibility in projects.
Cloud Computing: Experience with cloud platforms such as AWS, Google Cloud, or Azure for scalable data analysis and storage solutions.