Bioengineering + Computer Science @ UC Berkeley
I work on turning biological data into systems we can model, understand, and use.
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Research
- Stanford: Single-cell foundation models, cross-species alignment, and interpretability
- Ian Holmes Lab (UC Berkeley): Sequence modeling for transposons using transformer-based approaches
- NASA GeneLab (ADBR AWG): Multi-omics pipelines for studying brain resilience and neurodegeneration
- MD Anderson Cancer Center: Deep learning for clinical imaging and cancer treatment analysis
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Engineering
- Building reproducible pipelines for large-scale biological data
- Working across cloud and HPC environments
- Focused on clean systems, evaluation, and usability
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Interest
- AI in biology and healthcare
- Foundation models + multi-omics data
- Making biological research more computational and scalable
Languages
Python · Java · SQL · R
Machine Learning
PyTorch · TensorFlow · JAX · scikit-learn · Hugging Face · Weights & Biases
Bioinformatics
Scanpy · AnnData · Biopython · ViennaRNA · Bowtie2 · FastQC
Data & Infrastructure
Pandas · NumPy · Cloud · HPC (Slurm) · CUDA · Git · FastAPI
- Fine-tuning and evaluating single-cell foundation models
- Cross-species representation learning
- Building scalable pipelines for multi-omics data
- Improving model interpretability in biological systems
Build tools that make biological data easier to work with
Bridge machine learning and real-world biomedical applications
- LinkedIn: https://www.linkedin.com/in/parshawngerafian/
- GitHub: https://github.com/parshawn
- Email: parshangeraf81@berkeley.edu