Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Overall Objectives

Context and overall goal of the project

Building upon the expertise in machine learning (ML) and stochastic optimization of the late TAO project-team, the TAU team aims to tackle the vagueness of the Big Data purposes. Based on the claim that (sufficiently) big data can to some extent compensate for the lack of knowledge, Big Data is hoped to fulfill all Artificial Intelligence commitments. This makes Big Data under-specified in three respects:

The tackling of the under-specified issues in Big Data in TAU currently relies on four core research dimensions, taking inspiration and validation in four main application domains. These research dimensions involve Causal Modelling (required to support prescriptive Big Data), Deep Learning (related to constructive representations, and their compositionality), Optimization and Meta-Optimization (including sequential decision making and categorization of problems), and Big-Data Driven Design. The application domains include the long-lasting domains of Energy Management and High Energy Physics, the more recent focus of TAO/TAU in Computational Social and Economic Sciences, and, new this year, the Autonomous Vehicle, and Population Genetics.