Draft Topics of Call 2019

In the Call 2019, expected to be published in December 2019, two new and hot topics will be addressed, namely Explainable Machine-Learning-based Artificial Intelligence and Novel Computational Approaches for Environmental Sustainability.

The following topic keywords are given as illustration only. The CHIST-ERA Conference 2019 (Tallinn, June 11-13) will bring together scientists and CHIST-ERA representatives in order to identify and formulate promising scientific and technological challenges at the frontier of research with a view to refine the scientific content of the call. The conference will be open to the research community. The conference website will be opened later on and linked from this page.

The short topic descriptions below are provided in view of the conference. The full topic descriptions, to appear in the call text, will result from the conference.

Topic 1. Explainable Machine Learning-based Artificial Intelligence

Machine learning algorithms, especially deep neural networks, have become very popular in a large variety of applications. These algorithms can learn from examples to generalize classification or regression tasks and successfully apply the learned models to unknown data. Usually, these algorithms transfer input data into abstract representations that are highly effective but difficult to understand for humans, and are considered as ‘black boxes’. Hence, in most cases, neither the algorithms nor the researchers are able to explain how and why a certain prediction has been made. However, for many applications, it is essential that detailed information on the prediction is given to users so that they can understand the decisions that are derived from it. This is important for users to trust the decisions made by the system and to better use them. The objective of research on this topic is to make machine learning algorithms explainable, thereby reducing vulnerability and adding transparency by giving users detailed information why systems have arrived at a particular decision.

Application sectors: All application sectors of machine learning such as healthcare, bioinformatics, multimedia, linguistics, human computer interaction, machine translation, autonomous vehicles, etc.

Keywords: Artificial intelligence; machine learning; deep learning; explainability; transparency; accountability

Topic 2. Novel Computational Approaches for Environmental Sustainability

Our natural environment is a highly complex system. In order to anticipate the effects of concrete actions on the Earth’s ecosystems and climate and to manage the available resources in a provably sustainable way, it is essential to understand and precisely model them. While there has been significant progress in that direction over the last decades, there is still a need for more data with a better coverage and higher spatial and temporal resolutions, for improved integration of heterogeneous data into coherent models, and more generally for enhanced models and simulations. For that purpose, novel approaches to big data collection and curation, e.g. based on crowdsourcing, and to model development, e.g. based on statistics and machine learning, potentially leading to new applications, should be developed.

Application sectors: Environmental sustainability; biodiversity; climate; renewable energy; public health; public policies; green industry

Keywords: Earth System Models; model creation; model fitting; model tuning; model evaluation; model inter-comparison; uncertainty quantification; statistical methods; machine learning; simulation; big data; data integration; data curation; data quality; data visualisation; crowdsourcing

Anticipated Participation of Funding Organisations

The definitive list of the participating funding organisations will be published in the call text in December 2019. The table below provides indications only.

Note that the CHIST-ERA network of funding organisations is open to new members. Only funding organisations belonging to the current CHIST-ERA consortium appear in the table below. Interested researchers in countries not listed below are encouraged to contact their national funding organisation to express their interest.