About Aurora

Aurora Cramer (she/her) is currently a PhD Candidate studying Electrical and Computer Engineering at New York University, under the direction of Juan Pablo Bello. Her current research focuses mainly on audio source classification as part of the SONYC and BirdVox projects. Aurora obtained her B.S. in Electrical Engineering and Computer Sciences from U.C. Berkeley in 2015, where she completed the EECS Honors program with a focus in Music/Audio. In 2017, Aurora started her graduate studies in Electrical and Computer Engineering in the Masters program at New York University as a Samuel Morse Fellow, before transferring to the PhD program in 2018.

Her research interests lie in the intersection of machine learning and audio signal processing, focusing on learning informative audio representations by exploiting structure in small labeled audio datasets or large unlabeled audio datasets. This research occurs primarily in the spaces of machine listening and music information retrieval. Research problems of interest include self-supervised learning, audio source classification, unsupervised clustering of audio, generative models of audio, audio timbre/style translation, and audio source separation.

Aurora has worked in the space of audio and machine learning in industry. Most recently, she was an intern at Mitsubishi Electric Research Laboratories during the Fall of 2020 working on environment source separation. Previously, she was an intern on the Applied Deep Learning Research team at NVIDIA during the summer of 2018 working on audio inpainting. She was as a research engineer on the Applied Research team at Gracenote from 2015-2017 where she developed audio classification systems for music. In 2014, she was a media engineering intern at BlueJeans Network, where she worked on refactoring, testing, and improving noise suppression systems for video conferencing platforms.

Outside of academic interests, her interests include music, wordplay, whimsy, cuteness, UNIX, and cats and dogs.