Anna Chaney
Assistant Professor of Instruction
With a BS in Applied Mathematics, and a MS in computer science, Anna launched her career in engineering working on the Thirty Meter Telescope project. Over the next 12 years, she specialized in remote sensing algorithms, culminating as the principle investigator in an Office of Naval Research contract on the classification of signals. In 2014 she took her breadth of machine learning knowledge in applied research to the IBM Watson group. Within IBM Watson she led teams that created AI applications for business, and on the side hacked on The Watson Beat code base. Starting in 2019, she was a director of engineering for Resideo Technologies for one year, specializing in IoT devices.
Currently she is enjoying working with students and teaching the next generation of data scientists mad skills and pursuing her research interests in computational creativity.
She also plays bass guitar in live-band Karaoke act.
Research
Research Interests:
Anna enjoys exploring using data-driven algorithms to create aural and visual art and exploring the way we use technology to complement our creative expression.
She has released two albums as one half of the duo, "dj beep code" which used reinforcement learning and deep belief networks to generate musical stems. These stems were processed and mixed to produce digital instrumental music that is composed algorithmically and made into sound with human collaboration.
- Genesis (available on Spotify, and Apple Music) is a techno-pop inspired first forary into using code to compose music. The title track was also released with lyrics as a collaboration between dj beep code and Tayrn Southern, where Tayrn composed lyrics and remixed the stems, for her album "I AM AI".
- Aleph Null (available on Spotify, and Apple Music) is the cardinality of the infinite set of countable numbers and the inspiration for dj beep code's second album. Aleph Null contains two hours of deeply relaxing music that never ceases to put her family straight to sleep.
Her work has been presented at the NuerIPS Worksop for Machine Learning for Creativity and Design.
