The cooperation theme is "Artificial Intelligence for Industry 4.0". It covers three complementary and nested topics: (priviledged theme with Prof. Marco Hubert's group at Fraunhofer Institute for Manufacturing Engineering and Automation (IPA), with scientific annex).
DESCARTES : Program on Intelligent Modelling for Decision-making in Critical Urban Systems
The DesCartes project is highly competitive project awarded and supported by the Singapore National Research Foundation, Prime Minister’s Office, and its Campus for Research Excellence and Technological Enterprise (CREATE) program. It is and international collaborative project conducted under CNRS@CREATE program and involves 12 French universities, 6 Singaporean Universities and 6 industrial partners. The main universities in Singapore (NUS, NTU, SUSS, SUTD, SMU) and A*STAR.
Smart Manufacturing for quality (processes and products) and maintenance
The AI4DG German-French joint research project focuses on AI on the edge for a secure and autonomous distribution grid control with a high share of renewable energies. It is a highly international competitive project awarded under the ANR/BMBF collaboration.
AI-based optimization and simulation techniques for operations and supply chain management
Within the MIAI chair : AI for data-driven and self-configurable supply chains.
(priviledged theme with Dr Sören Kerner's and Marco Motta's groups at Fraunhofer Institute for Material Flow and Logistics (IML), with scientific annex)
SUPER - Solution for Uncertainty and personalization in Emotion Recognition : MIT project MISTI Global Seed Fund
The EU-funded AI4DI project aims to transfer machine learning (ML) and artificial intelligence (AI) from the cloud to the digitising industry. It will use a seven-key-target approach to evaluate and improve its relevance within the industry. The project plans to connect factories, processes and devices within the digitised industry by utilising ML and AI. AI4DI's mission is bringing AI from the cloud to the edge and making Europe a leader in silicon-born AI by advancing Moore's law and accelerating edge processing adoption in different industries through reference demonstrators.
DESCARTES - Optimization-Driven Hybrid AI Hybrid Modelling with Effective Domain Adaptation for Robust Prediction (WP3)
Coordinators: Sihem Amer-Yahia (CNRS) & Kurt Stockinger, ZHAW School of Engineering, Switzerland/INODE develops a unified, comprehensive platform that provides extensive access to open datasets through natural language queries in the fields of Cancer Biomarker Research, Research and Innovation Policy Making and Astrophysics; for a wide range of users from larger scientific communities to public.
This project focuses on the estimation of brain connectivity graphs with an innovative spatio-temporal approach his project is in collaboration with the University of Santa Barbara in the USA and the University of Lausanne in Switzerland.
Coordinators: Sihem Amer-Yahia (CNRS) & Laks V.S. Lakshmanan, University of British Columbia, Canada.
Subjective data links people to content items and reflects who likes or dislikes what. The valuable information this data contains is virtually infinite and satisfies various information needs. Yet, as of today, dedicated tools to explore this data are lacking. In this project, we develop a framework for Subjective Data Exploration (SDE). SDE enables the joint exploration of items, people, and people's opinions on items, in a guided multi-step process.
SPRING : Socially Pertinent Robots for Gerontological Healthcare
In the past five years, social robots have been introduced into public spaces, such as museums, airports, commercial malls, banks, company show rooms, hospitals, and retirement homes, to mention a few examples. In addition to classical robotic skills such as navigation, grasping and manipulating objects, i.e. physical interactions, social robots must be able to communicate with people in the most natural way, i.e. cognitive interactions. Nevertheless, today’s Human-Robot Interaction (HRI) technology is not well-suited to fulfil these needs. Indeed, socially assistive robots (SAR) that are currently available suffer from two main bottlenecks: (i) they are limited to a handful of simple scenarios which leads to (ii) SARs not being well accepted by a large percentage of users such as elderly adults. These limitations are largely due to the fact that both their hardware and supporting software have been designed for reactive single-user interaction mostly based on keyword spotting where the robot waits to be instructed what to do based on a limited set of scripted actions. In a nutshell, SPRING’s research question is how to develop robots able to move, see, hear and communicate with several actors, in complex and unstructured populated spaces, so that they can to properly fulfil social roles and successfully execute social tasks. Overcoming these limitations raises difficult scientific and technological challenges with tremendous social impact and economic value.
The question of answering queries over ML predictions has been gaining attention in the database community. This question is challenging because the cost of finding high quality predictions corresponds to invoking an oracle such as a human expert or an expensive deep neural network model on every single item in the DB and then applying the query. We develop AQUAPRO, a unified and novel framework that enables Approximate QUery Answering with PRoxy and Oracle to minimize the cost of finding high quality answers for both precision-target and recall-target queries.
AI4HP Artificial Intelligence for Heat Pumps (Project AI4HP)
On the basis of a collaboration between the French Ministry of Higher Education, Research and Innovation (MESRI), the German Federal Ministry of Education and Research (BMBF), Fraunhofer ISE, CEA, LPNC, Stiebel-Eltron GmbH & Co. KG, EDF R&D – Dept. Technology and Research for Energy Efficiency.
UGA participates to the TAILOR network of AI research excellence centers through the MIAI chairs “Explainable and Responsible AI” and “Knowledge communication and evolution” . UGA contribute in a significant manner to the WP3 (Trustworthy AI) and WP6 (Social AI) by bringing competences on knowledge representation and reasoning, deep learning, game theory and statistical learning, and their interactions in the context of security, privacy and ethics of online systems and algorithms.
Published on October 19, 2023 Updated on August 29, 2024
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