Computer science has widely branched out into a number of specialized disciplines. Focused and continuous research in each one of them has allowed a number of important scientific advances. However, it is often challenging to take advantage of the collective accumulated knowledge in a transversal, inter-disciplinary fashion.
In the current technological landscape, it is possible to identify a multi-disciplinary area of research that has swiftly emerged: data science. Naturally, data science greatly depends on machine learning and artificial intelligence techniques and mechanisms but it cannot further develop itself without taking advantage of bleeding edge distributed systems, data visualization techniques or data management systems.
Notwithstanding the effort to have specialists from different disciplines working together, there are still significant gaps of language, understanding and lack of collaboration between software infrastructure developers and machine learning algorithm designers and practitioners.
In the context of this grant, the selected scholar is expected to be an expert in the practical application of machine learning and artificial intelligence techniques for data science. In particular, the selected scholar is expected to give lectures, organize workshops and collaborate in research efforts with the objective of providing insight and training on such topics to practitioners from different backgrounds. The ultimate goal of these activities is to allow experts in areas such as data management and distributed systems to better understand the technological requirements and challenges of data science.
In a coordinated, multi-disciplinary environment the scholar is expected to engage in research activities that provide advancements in data science from a holistic and integrated perspective. To achieve this, the scholar should demonstrate mastery of the domains of machine learning and artificial intelligence from an application point of view and be able to provide training on applied technological workflow. In detail, the scholar is expected to provide training to practitioners in other areas on how to choose, configure, enhance and apply learning techniques to specific problems.