Postdoctoral researcher at Berkeley Lab, working on rare-earth nanophotonics and AI for science, from nonlinear optical materials to agentic research assistants.
I turn hard physical-science problems into working results, and increasingly into AI and automation that make discovery faster. I work at the intersection of nanophotonics, rare-earth materials, and machine learning, and I’ve taken projects from first synthesis all the way to first-author Nature papers.
Currently a postdoctoral researcher at the Molecular Foundry, Lawrence Berkeley National Laboratory (with Dr. Emory Chan and Dr. Bruce Cohen), working on photon upconversion, photon avalanche, and rare-earth nanomaterials. Before Berkeley, I was a postdoctoral research fellow in the Department of Chemistry at the National University of Singapore (2025–2026, with Prof. Xiaogang Liu). During my PhD at NUS (with Prof. Xiaogang Liu and Prof. Qian He), I pushed optical nonlinearities beyond 500 through sublattice reconstruction, a problem many considered intractable (Nature, 2025).
What I can bring to a team:
Energy transfer and ultrahigh optical nonlinearity through single-particle imaging and time-resolved analysis — toward super-resolution imaging, ultrasensitive sensing, and quantum photonics.
Functional nanostructures by conventional and automated high-throughput synthesis, with electron microscopy and synchrotron spectroscopy to link synthesis, structure, and property.
Domain-grounded LLM agents, machine-learning models, and rate-equation frameworks that understand lanthanide energy transfer and accelerate discovery.
Full list on Google Scholar.
Local-first, privacy-first PWA that tracks subscriptions & renewals, with on-device OCR and human-in-the-loop AI extraction.
Turns receipt photos into a structured, Excel-ready ledger via schema-constrained (Pydantic) LLM outputs.
Rate-equation simulator and datasets behind the Nature photon-avalanche study.
Auto-updating radar for research perks: free AI/compute credits, funding, grants, academic software, and travel support. Scanned daily, scored automatically, and matched to your CV in the browser.
Local-first AI research radar that ranks unread Inoreader articles against your CV, with private on-device storage.
An “AI CFO” for scientific R&D portfolios: models lab cashflow, FTE, resource bottlenecks, and Monte Carlo risk to help teams decide what to fund, pause, or stop.