Anastasios Angelopoulos
Ph.D. student in Electrical Engineering and Computer Science
at the University of California, Berkeley
Student of Michael I. Jordan
and of Jitendra Malik
Research interests
Building out Distribution-Free Uncertainty Quantification, motivated by applications to medicine and computational imaging.
Recent News
March, 2022 Part Two of our YouTube tutorial on conformal prediction and distribution-free uncertainty quantification has been posted! This one focuses on conditional coverage and diagnostics.
February, 2022 We just posted a new paper, Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging, which does super-resolution of transmission electron microscopy of a Drosophila melanogaster brain, fast MRI reconstruction of knees, and quantitative phase imaging of white blood cells with deep learning and rigorous uncertainty.
February, 2022 We just posted a new paper, Conformal Prediction for the Design Problem, addressing uncertainty quantification under distribution shift caused by feedback loops.
February, 2022 Our paper Rigorous Uncertainty Estimation for MRI Reconstruction was accepted as an oral at the Conference of the International Society for Magnetic Resonance in Medicine.
January, 2022 Our paper Private Prediction Sets was accepted to the Harvard Data Science Review.
January, 2022 We posted Online Active Learning with Dynamic Marginal Gain Thresholding on arXiv!
