Aurora Cramer (she/they) is currently a PhD Candidate studying Electrical and Computer Engineering at New York University, under the direction of Juan Pablo Bello. research focuses on using natural structure in audio data from hierarchical relationships, task relationships, multi-modal dynamics, and embodied navigation to learn effective audio representations for machine listening tasks without large annotated datasets.
Aurora obtained B.S. in Electrical Engineering and Computer Sciences from U.C. Berkeley in 2015, where completed the EECS Honors program with a focus in Music/Audio. In 2017, Aurora started 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. currently involved in the SONYC and BirdVox projects.
Aurora has worked in the space of audio and machine learning in industry. Most recently, an intern at Mitsubishi Electric Research Laboratories during the Fall of 2020 working on environment source separation. Previously, an intern on the Applied Deep Learning Research team at NVIDIA during the summer of 2018 working on audio inpainting. a research engineer on the Applied Research team at Gracenote from 2015-2017 where developed audio classification systems for music. In 2014, a media engineering intern at BlueJeans Network, where worked on refactoring, testing, and improving noise suppression systems for video conferencing platforms.
Outside of academic interests, :
- - playing piano and synths (sometimes I make little ditties)
- - bullet journaling
- - listening to music
- - cuteness, wholesomeness, and whimsy
- - LGBTQ+, disability, and racial representation & justice
- - cats, dogs, and other adorable animals
- - playing rhythm games, rogue-lites and platformers (especially Celeste 💖)
- - absurdity, surrealism, and goofiness
- - cute clothes, tattoos, and piercings
- - wordplay and puns
- - cuddly plushies
- - lore
- - marveling at beauty and wonder
- - wandering through nature and large spaces
- - lists