I'm a physicist & Chamberlain Postdoctoral Research Fellow at Lawrence Berkeley National Lab building new AI tools to help scientists make discoveries in particle physics and astrophysics.

I'm also a performing artist engaged in contemporary dance and theater.

Since 2017, I've led independent teams of researchers to generate choreography with AI models trained on my own movements.

My thesis work with the ATLAS Experiment at CERN involved the search for a rare interaction with the Higgs boson decaying to a pair of tau particles.

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Recent Projects

mememormee

2023

An AI-generated dance experiment and artistic residency at Amherst College.

Kinetech Arts Shows

2022

I was a dancer in two performances by Kinetech Arts inspired by entropy, AI, and technology.

  • Sublimation, at David Ruth Glass Studio, Oakland, CA.
  • Detour, at Zellerbach Playhouse, Berkeley, CA.

    • Cognicast Interview

    2022

    An interview on the Cognicast podcast, hosted by technologist and musician Robert Randolph, about my research across AI, physics, and the performing arts.

  • Episode webpage
  • Listen on Apple Podcasts

    • Dancing With Myself

    2021

    A talk at the StrangeLoop conference in St. Louis, MO, on AI, dance, and the creative process.

    Untitled AI Birdsong Project

    2021

    A 1-hour pop-up exhibit featuring AI-generated bird calls situated in nature.

  • Artist notes

    • Choreo-Graph

    2020

    As an intern with Intel's AI Lab, I developed a Graph Neural Network to learn a latent graph representation of my dancing body.

  • Github repository
  • Paper

    • Mirror Exercise

    2020

    An AI-generated duet with myself.

    Featured at the 2020 NeurIPS AI Art Gallery, the AI Governance Forum, and the Boston Cyberarts Gallery (cancelled due to COVID-19).

    SIGMA

    2019

    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.

    Beyond Imitation

    2019

    I led a research project using variational autoencoders to generate choreography.

  • Paper
  • Website with interactive demo
  • Github repository
  • Feature in YaleNews
  • Live performance featuring Raymond Pinto
  • First work-in-progress performance at Yale

    • Published in the proceedings of the International Conference on Computational Creativity (ICCC '19).

    Publications

  • A. Bogatskiy, S. Ganguly, T. Kipf, R. Kondor, D. W. Miller, D. Murnane, J. T. Offermann, M. Pettee, P. Shanahan, C. Shimmin, and S. Thais. Symmetry Group Equivariant Architectures for Physics. Snowmass 2021 White Paper. (2022).
  • Mariel Pettee. Interdisciplinary Machine Learning for Particle Physics. [PhD Thesis, Yale University]. (2021).
  • Mariel Pettee, Santiago Miret, Somdeb Majumdar, and Marcel Nassar. Choreo-Graph: Learning Latent Graph Representations of the Dancing Body. NeurIPS Workshop on Machine Learning for Creativity and Design. (2020).
  • Mariel Pettee, Chase Shimmin, Douglas Duhaime, and Ilya Vidrin. Beyond Imitation: Generative and Variational Choreography via Machine Learning. Proceedings of the 10th International Conference on Computational Creativity. (2019).
  • Co-writer & editor, Expected Performance of the ATLAS Detector at the High-Luminosity LHC, 2019.
  • Full list of papers with the ATLAS collaboration.