Learning of leader-follower graph from time-correlated big data streams with missing entries

Nowadays we are collecting high-dimensional and large data streams, where many dimensions can be expressing basically the same information on the underlying process of interest. This redundancy is apparent, for example, if we observe mass media news outputs through time. Here, even the discovery of the leader-follower structure of the news streams is valuable information.
I will be presenting very recent work approaching the leader-follower problem with missing entries, using scalable and accurate algorithms for big data streams.

Cláudia Soares

Cláudia Soares works in Big Data and distributed processing, working at ISR Lisboa. She strongly believes in developing research with academia and industry, exchanging ideas and methods for effective, simple, and scalable algorithms — and for solving real problems. Her research dwelves on developing a generic framework for learning with large-scale, heterogeneous, and space-time dependent data. For this work, Dr. Soares developed both parallel and stochastic algorithms, where nodes compute asynchronously, mostly with outdated data. Approaching real-world problems resulted in successful projects and publications in top venues. She has been collaborating with companies to address problems that are both real and aligned with her scientific interests. In this context, she worked with national and international industries like NOS, Nomad-Tech (CP), TAP, Thales, 3Lateral, uRoboptics, and developed academic collaborations with International partners like the TU/e, U. Milano, and U. of Novi Sad. Dr. Soares has a key role in funded projects of the national agency (FCT) in Data Science and AI for predicting ECU admissions and EU funded projects in AI, namely AI4EU -- European AI On Demand Platform and Ecosystem, and a European Doctoral program, BIGMATH, for BIG data challenges for MATHematics.ISR