Danielle Van Boxel
Graduate Associate
With a background in statistical computing, Danielle's research focuses on ensemble machine learning. Her PhD dissertation developed novel regression and classification algorithms that show consistent precision in a wide range of datasets. Danielle also mentors junior graduate students in scientific computing and contributes to various current lab studies. She is currently a Research Staff Mathematician at an FFRDC.
- In the lab
- 2022 to 2024
- Education
- PhD
- Co-advisor
- Dr. Xueying Tang
- After the lab
- Research Staff Mathematician at an FFRDC
Papers 3
Machine learning in biological research: key algorithms, applications, and future directions
BMC Biology · 2025 Student-led
11 authors
BARMPy: Bayesian additive regression models Python package
Computational Statistics · 2024 Student-led
1 author
Bayesian Additive Regression Networks
arXiv · 2024 Student-led
1 author
