DATE & TIME: Monday, April 18, 5:00 – 6:00 PM
LOCATION: Paino Lecture Hall
In these years marked by physical distance, Dr. Pettee’s primary dance partner has been a machine learning (ML) model. Inspired by her applications of ML in the domain of high-energy particle physics during her PhD, over the past few years she has led several independent teams of researchers across academia, industry, and the arts to create state-of-the-art ML-generated choreography using techniques including Variational Autoencoders and Graph Neural Networks. In this talk, she will discuss the research trajectories leading to her models, which were designed to understand dynamic many-body systems, along with their generative implications for her creative practice.
Mariel Pettee (Physics PhD, Yale University) is a Chamberlain Postdoctoral Research Fellow at Lawrence Berkeley National Laboratory. Her research encompasses the development of custom machine learning techniques for high-energy particle physics as well as astrophysics. She is particularly interested in creating generic techniques that have broad applicability across other areas of fundamental science and art. Since 2017, she has also led independent teams of researchers using machine learning to generate choreography based on 3D motion capture of her own movements. As a choreographer, director, and performer, she also uses theater and dance work to research audience activation, duration, power, self-documentation, authenticity, fear, and playfulness. Most recently, she has worked with companies including Kinetech Arts, LeeSaar, Urban Bush Women, Paul Taylor 2, and the Bill T. Jones/Arnie Zane Company. She was a choreographer-in-residence at Harvard University’s Dance Center in 2014. Prior to her PhD, she earned her Bachelors in Physics & Mathematics from Harvard University and her Masters in Physics at the University of Cambridge (Trinity College) as a Harvard-Cambridge Scholar.