Adam Sabra

Adam Sabra

Undergraduate Researcher

University of California, Davis


Adam Sabra is an aspiring Machine Learning Engineer graduating from the University of California, Davis as a Statistics major with a track focus in Machine Learning. He utilizes signal processing and machine learning techniques to quantify and separate various aspects of songs, with his thesis focusing on vocal extraction. Adam’s main goal is to use these skills to forge a career path in audio research and even become a Professor down the line. If he’s not developing his next project or this website further, you can find Adam watching movies, collecting vinyl records, or hiking.


  • Audio Source Separation
  • Music Information Retrieval
  • Deep Learning
  • Machine Learning
  • Signal Processing


  • BSc in Statistics - Machine Learning, 2021

    University of California, Davis






Data Science

Machine Learning

Deep Learning



Undergraduate Researcher

University of Calfornia, Davis

May 2020 – Present California

Machine Learning Intern

Hindsight Technology Solutions

Aug 2019 – Feb 2020 Remote

Data Science Intern


Jan 2019 – Aug 2019 Remote


How to Build an Audio Classifier

Introduction: The field of Machine Learning is rapidly expanding into nearly every field, be it through classical machine learning techniques - such as the ones used in this project - or deep learning techniques such as neural networks.

Vocal Extractor

Introduction The challenge of audio source separation has always been a challenging one for audio engineers. Audio source separation is often referred to as the cocktail party problem, where one is attending a cocktail party and honing in on one conversation among the dozens around them.