Research
Quantum and other experiments
Side projects. The quantum ones are learning projects and benchmarks, run on simulators. Where a classical baseline matched or beat the quantum model, the write-up says so.
Quantum Kernels on Synthetic ICU Data: Tracing a Chance-Level Result to the Encoding
A seeded Qiskit benchmark on synthetic ICU data. Every quantum model sat at chance; rescaling the angle encoding lifted the quantum SVMs to 0.70 to 0.80 ROC-AUC, level at best with logistic regression.
Decoding the Surface Code: MWPM vs BP+OSD in Simulation
A self-study quantum error correction curriculum whose capstone is a Stim benchmark of two decoders. The fitted thresholds overlap within fit error, the accuracy edge is significant over part of the grid in the committed sweep, and the most useful lessons came from the numbers that later checks took back.
QML-Essentials: a quantum machine learning curriculum, with classical baselines where they apply
Twenty-seven PennyLane and Qiskit scripts, one per curriculum week and all run on simulators, from a Bell pair to a quantum autoencoder. The tier 2 ledger holds no clear quantum win in its seven rows, and the results that taught me the most were the ones that fell apart under a control.
Stellar Spectra with Gradient Origin Networks
The cross-survey data-engineering layer for a Gradient Origin Network project on stellar spectra: survey-specific FITS readers, a ~30,000-star cross-match across APOGEE DR17, GALAH DR3, and Gaia-ESO DR4, and a DVC-tracked HDF5/Parquet store. The generative model itself is upstream; this is the pipeline that feeds it.