Accelerating the measurement of time-resolved emission line shapes with a denoising neural network Single coherent photons on demand are essential inputs for many applications of quantum optics, including for linear optical quantum computing. One critical parameter when assessing the potential of a...

My first publication as co-corresponding author, alongside my previous postdoc advisor Moungi Bawendi! We generalize our denoising neural network model for photon correlation Fourier spectroscopy, successfully applying it to >80 quantum dot experiments.
journals.aps.org/prb/abstract...

Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations Deep neural network models can be used to learn complex dynamics from data and reconstruct sparse or noisy signals, thereby accelerating and augmenting experimental measurements. Evaluating the quantu...

More quantum emitters + deep learning, accepted for the AI4Mat workshop at NeurIPS 2024! We develop latent neural ODE models that predict entire interferometric photon correlation experiments from only 10 measured inputs, allowing for up to x20 speedups in measurement time:
arxiv.org/abs/2411.11191