December 2020

Carnegie Mellon University (CMU) Unified Framework for the Competing Constraints in Federated Learning Principal investigator: Virginia Smith CMU will focus on federated learning systems with competing constraints, such as accuracy, fairness, robustness, and privacy. CMU will investigate statistical heterogeneity as a root cause for tension between these constraints. In particular, CMU will tackle heterogeneity via a unified framework for robust, privacy-preserving multi-task learning — unlocking a new generation of FL systems that can holistically address the constraints of realistic federated networks.

Smith Joins Private AI Collaborative Research Institute

Thursday, December 10, 2020
CMU faculty members Leman Akoglu and Christos Faloutsos were two of three authors on a paper that received the Most Influential Paper Award at the 2020 Pacific-Asia Conference on Knowledge Discovery and Data Mining. Mary McGohon, now at Google, was the third author.

SCS Team Wins Most Influential Paper Award at Data Mining Conference

Wednesday, December 9, 2020
COVIDcast Real-time COVID-19 Indicators by the Delphi Group | A new agreement with Change Healthcare will help CMU's COVIDcast researchers understand the current state of the pandemic as they continue to map real-time indicators of the virus.

Delphi Enhances COVIDcast With Change Healthcare Claims Data

Tuesday, December 1, 2020

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