Humane AI Net
Jan 2021 - Present
Our vision is built around ethics values and trust (Responsible AI). These are intimately interwoven with the impact of AI on society, including problems associated with complex dynamic interactions between networked AI systems, the environment, and humans.
Dec 2018 - Jun 2021
EnviroLENS bridges the gap for the utilization of the European Satellite capacities provided by Copernicus for environmental law enforcement and related cross-cutting sectors. The project is responding to the demands of the jurisdictional sector for ready-to-access evidence and scenario information on environmental situations.
The main aim of the project is to deliver Earth observation-based services providing evidences on environmental incidences and legal violations in order to support the evidence data gathering process and to foster data-driven decision-making.
Sep 2017 - Dec 2020
We are leading a breakthrough EU H2020 project, creating a solution that will help users/students find what they need not just in OER repositories, but across all open educational resources on the web. This solution will adapt to the user’s needs and learn how to make ongoing customized recommendations and suggestions through a truly interactive and impactful learning experience.
This new AI-driven platform will deliver OER content from everywhere, for the students’ need at the right time and place.
Feb 2015 - Present
QMiner is a data analytics platform for processing of large-scale real-time streams containing structured and unstructured data. The platform provides support for preprocessing, feature engineering, data mining and machine learning.
European Data Science Academy (EDSA)
Sep 2015 - Jan 2018
The European Data Science Academy (EDSA) designs curricula for data science training and data science education across the European Union (EU). EDSA establishes a virtuous learning production cycle whereby we: a) analyse the required sector specific skillsets for data scientists across the main industrial sectors in Europe; b) develop modular and adaptable data science curricula to meet industry expectations; and c) deliver data science training supported by multi-platform and multilingual learning resources. The curricula and learning resources are continuously evaluated by pedagogical and data science experts during both development and deployment.