Topological Data Analysis for Medical Data
We are a team dedicated to extracting meaningful insights from medical data through rigorous analysis and algorithm development. Our goal is to bridge the gap between raw medical data and actionable knowledge by leveraging modern data analysis techniques.
TDA-Medical focuses on the full pipeline of medical data analysis — from data collection and preprocessing to validation and algorithm design. We aim to develop robust, reproducible analytical methods that can contribute to better understanding of medical datasets.
Our core areas of work include:
- Medical data preprocessing and exploratory analysis
- Statistical and topological approaches to data interpretation
- Algorithm development for pattern recognition in medical datasets
- Data quality assurance and validation
| Name | GitHub | Role | Description |
|---|---|---|---|
| Sunjun Hwang | @justinbrianhwang | Team Lead | Data analysis, algorithm development, and overall project management |
| Eunho Choi | @aghoc | Data Analyst | Data analysis, quality assurance, and validation |
| Dohyun Hwang | @ezwez1203 | Algorithm Developer | Algorithm design and implementation |
Projects will be updated here as they are released.
| Project | Description | Status |
|---|---|---|
| TBD | — | 🔜 Coming Soon |
If you have any questions or suggestions, feel free to reach out through GitHub Issues or contact the team lead directly via GitHub.
