Mission
Our goal is to investigate, build, and identify the opportunities to make an impact through AI.
Responsible AI at Scale
Goal is to identify the challenges and opportunities of building responsible AI systems that can be deployed at scale for truthful, fair, and equitable use.
MUGC
Machine Generated versus User Generated Content (MUGC) aims at building algorithm(s) that would automatically detect machine-generated content. The goal is twofold: first, identification of machine-generated content; and second, flag it for its appropriateness.
Music and Young Mind
The impact is more crucial when it comes to young minds — children and adolescents.
News
- Dr. Sushmita will be on the panel (Decoding Ethics: Perspectives on Responsible Data Science) at the Women in Data Science (WiDS) conference 2024.
- Team shortlisted to present during “AI in Action” week at Northeastern University. Come join us in person at the Seattle campus from 3:00 pm- 5:30 pm.
- Poster on Machine Generated versus Human Generated Content Detection accepted at RISE2024: Yaqi Xie, Anjali Rawal, Dixuan Zhao, Yujing Cen, and Shanu Sushmita
- Dr. Sushmita has been chosen as a mentor for the Women in Music Information Retrieval (WiMIR) program.
- Dr. Sushmita has been selected to serve as the Grant chair for ISMIR Conference 2024.
- Research Showcase at 10th Anniversary, Seattle Campus, Northeastern University. Our MUGC research was shortlisted to be showcased during the 10th anniversary of our Seattle Campus.
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