| layout | page |
|---|---|
| title | Streamlining Data Science |
| subtitle | Exploring the Frontiers of Data Analysis |
| comments | true |
| carbonads | true |
As a data scientist, I believe in the power of open source, open science, and open data. My recent focus has been on productionizing machine learning and deep learning tools for versatile use across various datasets.
- Build well-documented, user-friendly modules for omics datasets
- Provide tutorials for early-stage data scientists in bioinformatics
- Standardize ML/DL and bioinformatics tools
- Differential Gene Expression in Kidney Cancer Subtypes
- Multi-label Classification of Kidney Cancer Subtypes
- Ensemble Classifiers for Kidney Cancer Classification
- Disease Stratification using UMAP and DBSCAN
Data source: Genomic Data Commons
Codebase: OmixHub
- Develop generalizable tools for e-commerce analysis
- Demonstrate advanced Python programming (OOP) with real-world data
Data source: Kaggle
Codebase: E-Commerce Projects
- Assemble deep learning algorithms for medical imaging analysis
- Create unified modules for 2D and 3D image processing
Data source: Contact via email
Codebase: AI for Medicine
During my masters at UC Davis, I developed skills in quantitative genetics and AI: