Michela Paganini (Yale Physics PhD) is a Postdoctoral Researcher at Facebook AI Research (FAIR) in Menlo Park where she focuses on empirically and theoretically characterizing neural network dynamics in the over-parametrized and under-parametrized regimes using pruning as a tool for model compression. Michela has a broad interest in the science of deep learning, with a focus on connecting emergent behavior in constrained networks to theoretical predictions. At the same time, she is a Research Affiliate at LBNL where she collaborates with colleagues from ATLAS and NERSC (the National Energy Research Scientific Computing center) on projects at the intersection of physics and computing. During her graduate studies at Yale, Michela 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. She is also interested in fairness and interpretability in machine learning.
In this coffee chat, Michela will take you through her career path starting at Yale and answer your questions on transitioning out of academia, being a research postdoc in industry, and anything else you’d like to learn about it! Please submit your questions here or bring them to the Q&A.
Host: Yale Physics Professional Development Organization
Questions? Email emma.castiglia@yale.edu
RSVP required: https://orgsync.com/170689/events/2835707/occurrences/6811863
Sponsored by the Yale Department of Physics and the Yale Wright Laboratory