Staff scientific ML engineer and computational chemistry lead building practical AI systems for molecular discovery.
My work spans molecular docking, protein-ligand modeling, generative molecular design, ADMET/QSAR, chemical LLMs, spectra workflows, scientific benchmarks, and reproducible Python/HPC infrastructure.
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- Matcha — multi-stage Riemannian flow matching for physically valid molecular docking.
- HEDGEHOG — stage-based evaluation for generative molecular design.
- Bento — reproducible protein-ligand docking benchmark and HPC workflows.
- SynthLadder — chemistry benchmark and evaluation package for agentic LLMs.
- posecheck-fast — high-throughput docking pose validation.
- Burrete — macOS molecular previews and Quick Look tooling.
- AI-driven drug discovery and molecular modeling
- Scientific ML benchmarks, reproducible pipelines, and research infrastructure
- Chemical LLMs, tool-enabled evaluation, and structured scientific workflows
- Product-grade research tooling for computational chemistry teams
Open to research and engineering collaborations in computational chemistry, molecular modeling, drug discovery AI, chemical LLMs, molecular spectra, and scientific software infrastructure.