Research

We address this general problem in the context of image data (photos) by proposing which photos to archive to meet an online storage budget. The decision is based on factors such...

We study fully dynamic online selection problems in an adversarial/stochastic setting that includes Bayesian online selection, prophet inequalities, posted price mechanisms...

To address this issue, we propose using probabilistic reparameterization (PR). Instead of directly optimizing the AF over the search space containing discrete parameters...

Meet Dr. Srijan Kumar, Assistant Professor at the School of Computational Science and Engineering, College of Computing at Georgia Institute of Technology.

In July 2022, Meta launched the Fairness in Two-Sided Markets request for proposals (RFP). Today, we’re announcing the winners of this award.

What it’s like to be a parent on the Core Data Science team at Meta

We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content’s popularity at one or more reference horizons...