I am a computational biologist and AI/ML scientist with a simple conviction: messy biological data holds the answers to some of medicine's hardest questions And the right tools can unlock them.
I design and develop reproducible, interpretable, and scalable analysis systems for:
- Multi-omics integration — RNA-seq, single-cell, proteomics, GWAS/QTL, and metabolomics
- Target identification & prioritization — evidence synthesis + ML ranking
- Phenotype stratification & systems biology models
- Cloud-native, reproducible bioinformatics workflows
Open-Source Bioinformatics Tools
I am in the process of building modular, community-ready workflows and platforms to accelerate translational discovery:
- End-to-end target discovery pipelines
- Reproducible ML evaluation templates for biological datasets
- Standardized scRNA-seq and multi-data integration workflows
- Knowledge-integration frameworks for evidence-based prioritization
These tools are designed to help scientists move from data → hypothesis → validation.
- Interpretable ML for target prioritization
- Modular, reusable computational infrastructure
- Scalable multi-dataset evidence integration
- Translational genomics and precision medicine\
- 🫀 Cardiometabolic diseases & obesity
- 🧠 Neuroscience & cardiac neurobiology
- 🫁 Liver disease & hepatology
- 🧪 Functional genomics & transcriptomics
- 🤖 AI-driven target identification & validation
- 📊 Causal inference & biomarker discovery
"I enjoy building tools and algorithms that help scientists and clinicians make sense of messy biological data — because good science deserves good tools."
I believe open science accelerates discovery. Everything I build here is designed to be transparent, reproducible, and community-ready. Whether you're a PhD student running your first RNA-seq analysis or a seasoned computational biologist looking for a robust pipeline, I hope something here is useful to you.
Pull requests, issues, and collaborations are always welcome. 🤝
Based in Boston, MA | Open to collaborations in computational biology, AI/ML for drug discovery, and open-source bioinformatics