Building intelligent systems where hard problems have real stakes
Focused on building things that matter — from research pipelines to production systems.
I build AI systems and full-stack platforms — spanning multi-agent pipelines, real-time data infrastructure, and production-grade machine learning. I care deeply about writing software that is reliable, scalable, and actually deployed, not just prototyped.
I'm drawn to domains where software can compress decades of progress into years — medicine, finance, and scientific research are where I think that potential is greatest. Whether that means automating complex workflows, surfacing insights from messy data, or building tools that let experts move faster, I want to be the engineer who makes it work end-to-end.
I'm at my best when the technical bar is high and the problem is worth solving. That combination is what I look for in the work I take on.
A journey through research labs and high-growth companies, shipping real products.
Selected projects spanning ML, NLP, computer vision, and full-stack development.
End-to-end neuropharmacology drug discovery platform for designing, simulating, and analyzing novel ADHD therapeutics. Integrates AI-powered molecular design via Claude (Anthropic), real-time 3D visualization with 3Dmol.js, molecular docking & dynamics simulations, ADMET prediction, PubMed/ChEMBL database search, and team-based collaboration tools with regulatory pathway analysis.
Agentic operating system for biology researchers that orchestrates AI bioscience models (AlphaFold 3, ESMFold, RFdiffusion, DiffDock) into autonomous multi-agent pipelines. Researchers input a target sequence and BioOS handles everything: protein folding → binding site prediction → ligand docking → ADMET screening → FDA-grade documentation. Every step is reproducible, auditable, and compliance-ready.
Transformer-based recommendation engine with semantic vector search and LangChain orchestration. Features a Gradio dashboard with 90%+ recommendation precision, combining dense embeddings with collaborative signals for personalized results at scale.
RAG-based chatbot grounded in the Gale Encyclopedia of Medicine corpus. Combines Flask for serving, Pinecone for semantic vector retrieval, and a large language model backbone to deliver accurate, real-time medical Q&A with source attribution.
U-Net convolutional neural network for automated malaria detection from microscopic blood cell images. Achieved high sensitivity for parasitized cell identification. Research presented at the Society of Robotic Surgery Conference, demonstrating real clinical potential.
Tools and technologies I use to bring ideas to life.
Whether you have a question, opportunity, or just want to chat about AI — my inbox is always open.