I'm an ML Engineer interested in how machine learning can improve the tools we already use. Currently, I work on the AI for Workspace team at Google, integrating advanced models and popular tools like Drive, Docs, and Slides.
Previously, I worked on ML for fundamental nuclear and high energy physics. I collaborated with teams to create new ML-based methods for physics experiments like differentiable hardware design optimized with gradient descent, Graph Neural Networks for feature regression, and fast transformer-based point cloud diffusion models.
ML Engineering
Model Eval & QA
Tinkering
AI for Improving (not replacing) work
PhD in Physics, 2021
BS in Physics, 2016
PyTorch & TensorFlow
Python, C/C++, Lua
Diffusion, GNNs, Transformers
HPC, UNIX, SLURM
I'm currently a machine learning engineer working on the AI for Workspace team at Google, where I'll work on improving popular services like Drive, Docs, Sheets, and more.
I was a postdoctoral research fellow in the machine learning for fundamental physics group, led by Benjamin Nachman. We worked at the intersection of physics and deep learning. I worked on detector data deconvolution, differentiable detector design, and generative modeling for collider data.
I conducted my PhD research as a member of the ALICE collaboration at CERN. I worked on identifiyng 'deep photons' where we used modern CNNs to identify photons produced in the initial hard scattering. My theses focused on studying parton fragmentation by measuring isolated γ-hadron correlations in pp and p–Pb collisions.
Take a look below at some of my favorite projects from the past few years.
