Fall 2022 Speaker Line-Up
Atlanta Chapter Governer, The Recording Academy
Date: Friday 2 December 2022
Daniela Rivera is an EMMY and 2x GMA nominated mixing engineer. She is a 2x GRAMMY participant (Rihanna “Unapologetic” 2014 WIN and Wiz Khalifa “Blacc Hollywood” 2015 Nominated) and multi-platinum audio engineer with credits on Billboard charting hits and Motion Picture Soundtracks (Mariah Carey, Justin Bieber, Zootopia). In 2022, she received her 2nd technical award nomination for Best Engineered Album/Vocal for Taiwan’s Golden Melody Awards. She was highlighted for her mixing work on Karencici’s sophomore album “99% Angel”. Daniela holds an Associate’s in Audio Technology (SAE Institute) and a Bachelor’s in Entertainment Business (Full Sail University). Her early career includes assistant engineering at the famed Silent Sound Studios (Atlanta, GA) under the mentorship of Thom “TK” Kidd, then furthering her experience under a 4 year term for multiple GRAMMY winning Mixer, Phil Tan. Daniela has been a mentor in music education since 2016, and is currently serving as a Governor for the Recording Academy – Atlanta Chapter.
Senior Research Scientist, Spotify
Dr Rachel Bittner is a senior research scientist at Spotify, and did her Ph.D. at New York University in the Music and Audio Research Lab (MARL) working with Juan Bello. Previously, she was a research assistant at NASA Ames Research Center working with Durand Begault in the Advanced Controls and Displays Laboratory. She did my master’s in math at NYU’s Courant Institute, and her bachelor’s in music performance and math at UC Irvine.
Her research interests are at the intersection of audio signal processing and machine learning, applied to musical audio. Her research interests include automatic music transcription, source separation, musical version identification, and open-source software and datasets for music research.
Postdoctoral Scholar in Human-Centered Artificial Intelligence, NYU Steinhardt
Originally from Uriangato, Mexico, Dr Iran R. Roman is a post-doctoral researcher working at NYU’s Music and Audio Research Laboratory. He holds a Ph.D. from Stanford University in Computer-based Music Theory and Acoustics. Iran aims at developing machines that can listen to music and speech like humans do. With this goal in mind, Iran has developed mathematical models that explain how the human brain synchronizes with the rhythms present in music and speech. In parallel to his PhD studies, Iran pursued industry research in machine listening at Apple, Tesla, Osillo biosciences, and Plantronics.