Biotechnology undergrad working across ML, DL, and model engineering.
Currently building expertise in PyTorch, reproducible workflows, and Bio-ML foundations.
- Strengthening ML/DL fundamentals with emphasis on PyTorch architecture
- Prototyping CNN/attention modules to build model-level intuition
- Exploring sequence/protein representations to prepare for Bio-ML
- Developing reproducible training + evaluation pipelines
- Packaging deployable model services using FastAPI + Docker
- ml-classification-pipeline: End-to-end ML workflow (data → training → evaluation → metrics dashboard).
- cnn-training-system: PyTorch CNN pipeline + modular training loops + augmentation stack.
- pytorch-training-framework: Config-driven training system for reproducible ML experiments.
- ml-deployment-service: FastAPI + Docker deployment for inference-ready ML models.
| Domain | Core Competencies |
|---|---|
| Molecular Biology | PCR · cDNA synthesis · DNA/RNA extraction · Protein extraction · Primer design · Restriction enzyme cloning · Plasmid isolation · SDS–PAGE · Gel electrophoresis · Column chromatography |
| Imaging & Quantification | Confocal microscopy · Fluorescence microscopy · ImageJ (FIJI) quantitative analysis |
| Bioinformatics | MEGA X · BLAST · Primer-BLAST · NEBcutter · ApE (plasmid editor) · GraphPad Prism |