| **Senior Deep Learning Researcher at Embedl | PhD in machine learning for quantum technology** |
I make large neural networks run fast on small hardware. My work spans quantization, pruning, neural architecture search, and inference optimization — taking models from PyTorch to real-time execution on edge devices such as NVIDIA GPUs (Orin, Thor), AMD Strix Halo, and Qualcomm NPUs.
I collaborate with the quantum machine learning team at Xanadu and Dr. Maria Schuld on benchmarking quantum vs. classical machine learning, running large-scale experiments on HPC clusters (NERSC) with SLURM and Ray Tune.
My PhD at Chalmers University of Technology (Wallenberg Centre for Quantum Technology) was on machine learning for quantum physics: adversarial neural networks for quantum tomography, Riemannian optimization for quantum process learning, and data analysis for the first-ever quantum state tomography of photoelectrons with the group of Nobel laureate Anne L’Huillier.
Core contributor and admin team member of QuTiP, the quantum toolbox in Python. Contributor to PennyLane. Google Summer of Code student (DIPY, 2016) and later mentor for QuTiP projects.
| CV: web version |
| Links: Google Scholar | GitHub |
Get in touch to discuss deep learning model optimization, edge AI, quantum machine learning, or open-source software.