Machine learning · Scientific computing · Physical systems
Co-founder & CSO, SuperMatics
Previously Rigetti Computing & Berkeley Lab · Berkeley, California
SuperMatics ↗ · LinkedIn ↗ · Selected work ↓
I'm a generalist drawn to the intersection of machine learning, scientific computing, and the physical world: how models learn, where they fail, and how to turn their predictions into useful decisions.
At SuperMatics, I work on accelerating superconductor R&D with quantum-mechanical simulation, machine learning, and real laboratory data.
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Optimization, representations, and the numerical details that make training and inference behave as intended. |
LLM serving, tool use, and evaluation. Correctness across streaming, batching, and persistent state. |
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Simulation, uncertainty, and experimental data to decide what to measure, test, or build next. |
Superconducting materials, quantum hardware, and cryogenics. Computational models grounded in physical constraints. |
Merged contributions and open proposals across AI/ML, generative media, and scientific computing. Status is refreshed daily from GitHub; open does not imply accepted.
How I contribute · engineering approach & AI disclosure
- Pick issues a maintainer has signaled are ready to accept, and check nobody else is on them.
- Reproduce first: add a test that fails without the fix, then make the smallest change that passes it.
- Show before-and-after evidence, run the project's own checks, and report honestly which ones fail and why.
- AI coding tools assist with research, implementation, tests, and drafting. AI assistance and verification limits are disclosed in individual pull requests; human review is not implied.