|Speaker :||Alessandra Sala|
|Nokia Bell Labs|
|Time:||2:00 pm - 3:00 pm|
|Location:||Paris-Rennes-Nice Room (EIT Digital)|
In the post-digital era we face the risk of cognitive insufficiency in the attempt of dealing with the increasing exposure to large volume of data like chats, emails, multimedia, etc. Today, Big Data systems are still far from human level understand and so inadequate to rescue our cognitive needs. State-of-art Natural Language Processing systems are mostly successful in the understanding and linking of key entities and concepts to external knowledge repositories in specific domains. However, a higher level understanding of unstructured data is required to unveil the shades of meanings for a deeper semantic interpretation.
In order to understand and interpret the growing volume of unstructured data, we must go deeper to uncover the real meaning of content while doing it at scale and in real time. Towards this vision, this talk will present advanced machine learning algorithms to understand unstructured multi-modal contents (e.g., social posts, emails, multimedia) at a deep semantic level. This research enables users to understand, organize and absorb the ever-growing amount of information along with the different viewpoints therein. Our results are proved to be highly accurate with scalable training, to work with real-time queries, and to support fast dynamic updates. The output of this research is meant to change the way we interact, learn, teach – revolutionizing the way we communicate.
Alessandra Sala is the Head of the Bell-Labs Analytics Research. In her prior appointment, she was the technical manager for the Data Analytics and Operations Research group in Bell Labs Ireland. Before that, she held a research associate position in the Department of Computer Science at University of California Santa Barbara. During this appointment, she was a key contributor of several funded proposals from National Science Foundation in USA and her research was awarded with the Cisco Research Award in 2011. She focused her research on modeling massive graphs with an emphasis on mitigating privacy threats for Online Social Network users. Before that, she worked for two years as post-doctoral fellow with the CurrentLab research group led by Prof. Ben Y.Zhao. Before UCSB, she completed her Ph.D in Computer Science at University of Salerno, Italy.
Her research focus lies on distributed algorithms and complexity analysis with an emphasis on graph algorithms and privacy issues in large scale networks. In her previous research she has developed efficient distributed systems that support robust and flexible application level services such as scalable search, flexible data dissemination, and reliable anonymous communication.