Print  Close

Music, Data, and Discovery: Innovative Approaches to Understanding Musical Trends and Recommendation

Abstract Number:

335 

Submission Type:

Invited Paper Session 

Participants:

Jo Wick (1), Donald Hedeker (2), Jo Wick (1), Mark Glickman (3), Elena Badillo-Goicoechea (4), David Hitchcock (5)

Institutions:

(1) University of Kansas Medical Center, N/A, (2) The University of Chicago, N/A, (3) Harvard University, N/A, (4) University of Chicago, N/A, (5) University of South Carolina, N/A

Chair:

Jo Wick  
University of Kansas Medical Center

Discussant:

Donald Hedeker  
The University of Chicago

Session Organizer:

Jo Wick  
University of Kansas Medical Center

Speaker(s):

Mark Glickman  
Harvard University
Elena Badillo-Goicoechea  
University of Chicago
David Hitchcock  
University of South Carolina

Session Description:

This session brings together cutting-edge research exploring the intersection of music, data science, and technology. Our invited speakers will explore innovative methodologies that leverage computational techniques to uncover hidden patterns, understand evolving trends, and enhance music recommendation systems. From analyzing the stylistic nuances of iconic musicians like John Lennon and Paul McCartney to forecasting the future of genre popularity and developing novel content-based recommendation approaches, these presentations offer valuable insights into music analysis.

Sponsors:

No Additional Sponsor 3
No Additional Sponsor 2
Council of Chapters 1

Theme: Statistics, Data Science, and AI Enriching Society

Yes

Applied

Yes

Estimated Audience Size

Medium (80-150)

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is nonrefundable.

I understand