The ATLAS Experiment's first measurement of this rare process of the Higgs boson decaying into two tau leptons.
Using AI to identify remnants of ancient galaxies that can help map dark matter in the Milky Way, all without explicit labels.
Using Transformer, Deep Sets, and Graph Neural Network architectures to process pion calorimeter clusters and particle tracks as point clouds.
Creating dance through artist-centric deep learning with a semi-supervised conditional recurrent variational autoencoder.
We argue that the introduction of symmetries into an AI model's fundamental structural design can yield models that are more economical, interpretable, and/or trainable.
An AI-generated dance experiment and artistic residency at Amherst College.
I was a dancer in two performances by Kinetech Arts inspired by entropy, AI, and technology.
An interview on the Cognicast podcast, hosted by technologist and musician Robert Randolph, about my research across AI, physics, and the performing arts.
A talk at the StrangeLoop conference in St. Louis, MO, on AI, dance, and the creative process.
A 1-hour pop-up exhibit featuring AI-generated bird calls situated in nature.
As an intern with Intel's AI Lab, I developed a Graph Neural Network to learn a latent graph representation of my dancing body.
An AI-generated duet with myself.
Featured at the 2020 NeurIPS AI Art Gallery, the AI Governance Forum, and the Boston Cyberarts Gallery.
A short film of entirely AI-generated movements.
Featured at the 2019 NeurIPS AI Art Gallery for the Workshop on ML for Creativity and Design.
I led a research project using variational autoencoders to generate choreography.
Published in the proceedings of the International Conference on Computational Creativity (ICCC '19).