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Living in Perfect Harmony – Where Music and Machine Learning Meet
Data Scientist – Meta
Living in Perfect Harmony – Where Music and Machine Learning Meet
Data Scientist – Meta
Yama is a data scientist at Meta, fighting fraud in financial services. Prior to her work at Meta, she worked at MyPart, an Israeli startup in the music industry, developing algorithms and researching lyrical and musical song features. She is an activist both in the social world, fighting violence against women and children, and in the technological world, as a public speaker and a mentor to women taking their first steps in the data science world.
Yama has a BSc in Mathematics and Physics from Tel Aviv University, where she also expressed her passion for music by playing the saxophone in the TAU Wind Band.
Yama is a data scientist at Meta, fighting fraud in financial services. Prior to her work at Meta, she worked at MyPart, an Israeli startup in the music industry, developing algorithms and researching lyrical and musical song features. She is an activist both in the social world, fighting violence against women and children, and in the technological world, as a public speaker and a mentor to women taking their first steps in the data science world.
Yama has a BSc in Mathematics and Physics from Tel Aviv University, where she also expressed her passion for music by playing the saxophone in the TAU Wind Band.
The revolution of machine learning is reaching into every aspect of our lives – including art and music.
This talk dives into the world of song analysis and the extraction of lyrical and musical features, discussing existing approaches both in machine learning – natural language processing and digital signal processing – and in music theory and linguistics. The talk will explore how to use these features in different kinds of machine learning models, and how these models can be used to solve problems in the music industry, such as song tags and song similarity.
Attend this talk to learn how technical skills can be useful in extracurricular pursuits and hobbies.
The revolution of machine learning is reaching into every aspect of our lives – including art and music.
This talk dives into the world of song analysis and the extraction of lyrical and musical features, discussing existing approaches both in machine learning – natural language processing and digital signal processing – and in music theory and linguistics. The talk will explore how to use these features in different kinds of machine learning models, and how these models can be used to solve problems in the music industry, such as song tags and song similarity.
Attend this talk to learn how technical skills can be useful in extracurricular pursuits and hobbies.
| 8:45 | Reception |
|---|---|
| 9:30 | Opening words by WiDS TLV ambassador Nitzan Gado and by Lily Ben Ami, CEO of the Michal Sela Forum |
| 9:50 | Prof. Bracha Shapira – Data Challenges in Recommender Systems Research: Insights from Bundle Recommendation |
| 10:20 | Juan Liu – Accounting Automation: Making Accounting Easier So That People Can Forget About It |
| 10:50 | Break |
| 11:00 | Lightning talks |
| 12:20 | Lunch & poster session |
|---|---|
| 13:20 | Roundtable session & poster session |
| 14:05 | Roundtable closure |
| 14:20 | Break |
| 14:30 | Merav Mofaz – “Every Breath You Take and Every Move You Make…I'll Be Watching You:” The Sensitive Side of Smartwatches |
| 14:50 | Reut Yaniv – Ad Serving in the Online Geo Space Along Routes |
| 15:10 | Rachel Wities - It’s Not Just the Doctor’s Handwriting: Challenges and Opportunities in Healthcare NLP |
| 15:30 | Closing remarks |
| 15:40 | End |
WiDS Tel Aviv is an independent event that is organized by Intuit’s WiDS TLV ambassadors as part of the annual WiDS Worldwide conference, the WiDS Datathon, and an estimated 200 WiDS Regional Events worldwide. Everyone is invited to attend all WiDS conference and WiDS Datathon Workshop events which feature outstanding women doing outstanding work.
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