He was part of a team recognized for their contributions to algorithm engineering, including several frameworks that revolutionized large-scale graph processing on shared-memory machines.
In a paper being presented this week, authors affiliated with the CLIP Lab argue that topic model developers should reassess the increasing use of machine learning to evaluate their work.
It was recognized as the best paper presented at SafeRL, a workshop that was part of the 35th Conference on Neural Information Processing Systems (NeurIPS), held virtually from December 6–14.
The annual award recognizes a doctoral thesis that shows great potential and aligns with the scientific mission of Drones, an international open-access journal.
The computer science graduate student specializes in affective computing—the study and development of intelligent systems that can understand, interpret and respond to human emotions and behavior.
The initiative's goal is to create a robust and accessible pool of qualified cyber professionals that can assist CYBERCOM in its mission of defending critical U.S. information networks.