Undergraduate Research Intern – Stanford Medicine, Radiological Sciences Laboratory (RSL)
PyTorch, NumPy, Multilayer Perceptrons (MLPs), Diffusion Tensor MRI
- Designed and implemented a novel machine learning approach using PyTorch MLPs to reconstruct high-resolution neural shape models from Diffusion Tensor MRI scans.
- Enhanced anatomical fidelity by building models that improved structural detail capture in reconstructed neural shapes.
- Built an end-to-end preprocessing and modeling pipeline in PyTorch and NumPy, including segmentation and normalization.
- Collaborated with a Stanford Medicine postdoctoral researcher, delivering reproducible, production-ready code.
Deep Learning Research Assistant – Neuromorphic Computing Group
Python, PyTorch, Spiking Neural Networks (SNNs)
- Created a Python tutorial notebook introducing Spiking Neural Networks (SNNs).
- Demonstrated benefits of SNNs for reducing model storage and energy usage.
- Collaborated with researchers developing open-source tools for SNN model conversion and optimization in PyTorch.
Computational Analysis of Polycythemia Vera Driver Mutation
Gene expression analysis, Pathway enrichment, Machine learning, Network modeling
- Developed a computational pipeline to confirm JAK2 V617F as the primary driver mutation in Polycythemia Vera.
- Integrated gene expression analysis, pathway enrichment, and machine learning classification.
- Conducted differential gene expression studies using publicly available genomic datasets (GEO, TCGA, COSMIC).
- Built and analyzed network models to investigate biological pathways and mutation effects.
