Michela Paganini is a Postdoctoral Researcher at Facebook AI Research in Menlo Park and an affiliate at Lawrence Berkeley National Lab. She joined Facebook in 2018 after earning her PhD in physics from Yale University. During her graduate studies, she worked on the design, development, and deployment of deep learning algorithms for the ATLAS experiment at CERN, with a focus on computer vision and generative modeling. Prior to that, in 2013, she graduated from the University of California, Berkeley with degrees in physics and astrophysics. Her current research focuses on empirically and theoretically characterizing neural network dynamics in the over-parameterized and under-parameterized regimes using pruning as a tool for model compression. She is broadly interested in the science of deep learning, with a focus on connecting emergent behavior in constrained networks to theoretical predictions.