Data-centric Machine Learning Research (DMLR): Harnessing Momentum for Science

Manil Maskey, Lilith Bat-Leah, Danilo Brajovic, Paolo Climaco, Alicia Parrish, Chanjun Park, Xiaozhe Yao, Holger Caesar, Bernard Koch, Fatimah Alzamzami, Zhangyang Wang, Jerone Andrews, Praveen Paritosh, Steffen Vogler, Mayee F Chen, Sang T. Truong, Bolei Ma

Workshop Type: Hybrid

Keywords: Data-centric AI, data-centric, Data curation for AI, datasets, data, machine learning, artificial intelligence

Abstract: This workshop focuses on the increasing role of data in machine learning, particularly in the context of science research and application. It builds on existing momentum in the field and explores important topics including data quality, governance, ethics, infrastructure & tools, and community development. The main objective of the workshop is to shape the agenda for data-centric machine learning research (DMLR). Towards that end, the workshop will foster collaboration among researchers, practitioners, data producers, data consumers, and experts in DMLR. The workshop will feature keynote talks, panel discussions, poster sessions, and networking opportunities.

Submission Number: 38