The MC2 papers at IEEE S&P cover timely topics that have important real-world implications for improving security and privacy in a wide range of contexts.

UMD researchers will analyze everyday conversations for signs of trust—and distrust—in AI.

UMIACS researchers came up with an innovative project to satisfy their appetite for advancing computer vision systems based in machine learning.

New UMD research suggests organizations should cap cybersecurity spending at 37% of potential breach costs.

UMD and George Mason researchers developed a faster way to transfer locomotion skills across different robot designs.

The award supports the development of advanced computational methods to uncover deeper insights into species’ evolutionary relationships.

The funding supports Pop's efforts to build software and develop algorithms that will help scientists better understand bacteria.