About Me

I am a first-year Masters student at Stanford CCRMA studying Music, Science, and Technology. My research interest is in music cognition, particularly familiarity in music. So far at Stanford, I have become involved with the Music Engagement Research Initiative (MERI) and Takako Fujioka's Neuromusic lab.

I hold a bachelor of science degree from UCLA, where I studied cognitive science and minored in mathematics and musicology. During my time at UCLA, I did memory research with Robert and Elizabeth Bjork in the Bjork Learning and Forgetting Lab and studied Balkan music with Ivan and Tzvetanka Varimezov. I also have experience in software engineering and have interned at AT&T and the startp Pathonomic, Inc. More recently, I have become interested in audio recording and production and am working on several projects in that area.

I also love to sing and arrange music. I am currently performing with Stanford's all-female a cappella group Counterpoint. Previously, I was the musical director of UCLA YOUTHphonics and sang in the UCLA Chorale .


December 2017

Happy Holidays! Enjoy Carol of The Bells a holiday song I arranged, recorded, and produced for Stanford Counterpoint.

December 2017

Finished my first quarter at Stanford! Check out my final projects below, including my "A Cappella Ambisonics" piece and a paper on using EEG to study affective priming in music.

September 2017

Started graduate school at Stanford University, working towards my master's degree in Music, Science, and Technology.

June 2017

I graduated from UCLA with a bachelor of science in cognitive science and minors in mathematics and musicology. I had the honor of singing the UCLA Alma Mater at commencement with two of my friends, here's a video of our performance!


Here are some of my recent projects:

Affective Priming in Music

My final paper for Music 451a, using Electroencephalography (EEG) to study affective priming in music.

A Cappella Ambisonics

My final project for Music 220a, involving recording a vocal piece and panning it into four channels using Chuck.

Music Genre Categorization

A project from my neural networks course at UCLA, using Mel-frequency Cepstral Coefficients (MFCCs) to categorize music into 4 genres. Coding was done in MATLAB.

Pitch Discrimination

A project from my psychophysics course at UCLA, using PsychoPy to study pitch discrimination in musicians and non-musicians.

Contact Me

Elena Georgieva
Email: egeorgie[at]stanford.edu
LinkedIn: Elena Georgieva


I am currently seeking an internship for Summer 2017, feel free to look at my current resume and contact me with any questions!