Samaneh Azadi is a fourth-year PhD student at UC Berkeley, advised by Trevor Darrell. Her research focuses on deep learning and computer vision. She is particularly interested in improving object recognition systems in challenging scenarios such as noisy supervision by considering label- and instance-level relations in the scenes.

Samaneh’s current research aims at bridging the gap between generative networks, which have made fundamental changes in data generation, and recognition systems to have a more enriched representation of the world.

For more information, please visit her website.