The Data Science Department at EURECOM focuses on current computational, statistical and mathematical challenges in Machine Learning and Artificial Intelligence, as well as novel approaches to Knowledge and Data Management.
The Data Science department was created in February 2016. The aim of creating this new department at EURECOM was to consolidate the research and teaching activities in Data Science, and expanding the breadth of application domains beyond the field of telecommunications, which has been the main focus of EURECOM since its creation.
Our work is defined through an interdisciplinary approach to research, merging contributions from computer science, machine learning, statistics, and mathematics. Our research program is centered around the disciplines to semantically integrate and enrich data, to model and understand data, to design and analyze scalable computational approaches to machine learning, and to build systems that allow storing and processing vast amounts of data. We address several applied problems, which motivate the development of novel theory and algorithms.
The main research lines underpinning our academic and industrial projects involve the development of a solid foundation of systems and theoretical tools to interact with and manipulate vast amounts of heterogeneous data.
The Bayesian treatment of statistical models with the volume and variety of data available in modern applications poses novel computational challenges. We tackle these challenges by developing novel theory and algorithms at the interface between statistical, mathematical and computational sciences.
The successful use of machine learning in healthcare is mostly challenged by three limiting factors: data complexity, low error tolerance and reliability. We address this challenges through the development of robust ML algorithms relying on human-in-the-loop learning techniques.
AI for perception and control allows developing a new breed of autonomous vehicles, with safety and sustainability objectives as primary goals. Accurate quantification of uncertainty for such tasks is what sets apart the methodologies we develop in the department with respect to current standards.
Online misinformation is negatively impacting every aspects of the social discourse, from politics to health. We are creating AI systems to identify and debunk incorrect claims with data-driven explanations.
Effective Inference of Cleaning Programs from Data Annotations.
Computational methods for verifying claims and identifying misinformation about COVID-19.