Machine learning researcher and engineer working across scientific ML, uncertainty quantification, and high-performance computing. I build uncertainty-aware models, GPU-accelerated scientific computing pipelines, and reproducible ML tools.
Currently pursuing an M.S.E. in Computer Science at Johns Hopkins University and working on machine learning methods for reionization cosmology.
Seeking Summer 2027 internships.
| Platform | Link |
|---|---|
| Portfolio | robertxpearce.com |
| robert-d-pearce | |
| ORCID | 0009-0004-5143-8747 |
| PyPI | robertxpearce |
reionemu— Python package for emulating the kSZ angular power spectrum from reionization simulations.uncertainty-aware-histopathology-survival-analysis— Benchmark of MC-Dropout, Deep Ensembles, and SNGP for uncertainty quantification in an ABMIL + Cox survival model on TCGA glioma whole-slide images.Quorum iOS Accessibility— Senior design work focused on improving accessibility support for blind and visually impaired iOS developers.
- Machine Learning
- Uncertainty Quantification
- Interpretability
- Scientific Computing
- Research Software Engineering
- GPU/Supercomputing