I'm a computational biologist and ML practitioner working at the intersection of machine learning and biomedical data. I build reproducible pipelines and apply deep learning and classical ML methods to problems in genomics, digital pathology, and single-cell biology.
I hold an MS in Bioinformatics and Genomics from the University of Oregon.
📫 layaasivakumar@gmail.com · LinkedIn
Languages: Python · R · Bash · SQL
Machine Learning: PyTorch · HuggingFace · scikit-learn · TensorFlow
Bioinformatics: RNA-seq · Single-cell (Seurat, Signac) · Spatial transcriptomics · Variant calling
Infrastructure: Nextflow · Docker · Git/GitHub · HPC (Slurm)
| Project | Description | Tools |
|---|---|---|
| Protein Localization Classifier | Deep learning classifier for protein subcellular localization using ESM-2 embeddings and a custom PyTorch MLP | PyTorch, HuggingFace, ESM-2 |
| TFBS Classification & Interpretability | Binding site classifiers with motif recovery analysis for CTCF and SP1 | scikit-learn, LS-GKM |
| Digital Pathology Style Transfer | Neural style transfer for H&E staining harmonization using VGG16 and CLIP evaluation | PyTorch, CLIP |
| Single-Cell & Spatial Omics Workflows | End-to-end scRNA-seq, scATAC-seq, and Visium spatial transcriptomics workflows | Seurat, Signac, R |
| Digital Pathology Classification | Fine-tuned vision models for pathology slide classification, deployed on HuggingFace Spaces | PyTorch, HuggingFace |
| WGS Variant Discovery Pipeline | Reproducible Nextflow pipeline benchmarking aligners and variant callers | Nextflow, Docker, GATK |
🔍 Open to roles in computational biology, bioinformatics, and biomedical ML
🌱 Based in Canada · Open to opportunities in Toronto and beyond