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Anne-Sophie Taillandier: New member of the Academy of technologies

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Director of Teralab, IMT’s Big Data and AI platform, since 2015, Anne-Sophie Taillandier was elected member of the Academy of technologies in March 2022. This election is in recognition of her work developing projects on data and artificial intelligence at national and European level.

Newly elected to the Academy of Technologies, Anne-Sophie Taillandier has been Director of Teralab for seven years, a platform created by IMT in 2012 that specializes in Big Data and Artificial Intelligence. Anne-Sophie Taillandier was drawn towards a scientific occupation as she “always found mathematics enjoyable,” she says. “This led me to study science, first in an engineering school, at CentraleSupélec, and then to complete a doctoral thesis in Applied Mathematics at the ENS, which I defended in 1998,” she adds.

Once her thesis in Artificial Intelligence was completed, she joined Dassault Systèmes. “After my thesis, I wanted to see an immediate application of the things I had learned, so I joined Dassault Systèmes where I held various positions,” says Anne-Sophie Taillandier. During the ten years she spent at the well-known company, she contributed to the development of modeling software, worked in Human Resources, and led the Research & Development department of the brand Simulia. In 2008, she moved to an IT security company, and then in 2012 became Director of Technology at LTU Technologies, an image recognition software company, until 2015, when she took over the management of Teralab at IMT.

“It was the opportunity to work in a wide variety of fields while focusing on data, machine learning, and its applications that prompted me to join Teralab,” says Anne-Sophie Taillandier. Working with diverse companies requires “understanding a profession to grasp the meaning of the data that we are manipulating”. For the Director of Teralab, this experience mirrored that of her thesis, during which she had to understand the meaning of data provided by automotive engineers in order to manipulate it appropriately.

Communicating and explaining

In the course of her career, Anne-Sophie Taillandier realized “that there were language barriers, that there were sometimes difficulties in understanding each other”. She has taken a particular interest in these problems. “I’ve always found it interesting to take an educational approach to explain our work, to try to hide the computational and mathematical complexity in simple language,” says the Teralab director. “Since its inception, Teralab has aimed to facilitate the use of sophisticated technology, and to understand the professions of people who hold the data,” she says.

Teralab positions itself as an intermediary between companies and researchers so that they may understand each other and cooperate. In this project, it is necessary to make different disciplines work together. A technology watch is also important to remain up to date with the latest innovations, which can be better suited to a client’s needs. In addition, Teralab has seen new issues arise during its eight years of existence.

“We realized that the users who came to us in the beginning wanted to work on their own data, whereas today they want to work in an ecosystem that allows the circulation of their data. This raises issues of control over the use of their data, as well as of architecture and exchange standards,” points out Anne-Sophie Taillandier. The pooling of data held by different companies raises issues of confidentiality, as they may be in competition on certain points.  

European recognition

“At TeraLab, we asked ourselves about data sharing between companies, which led us to the Gaia-X initiative”. In this European association, Teralab and other companies participate in the development of services to create a ‘cloud federation’. This is essential as a basis for enabling the flow of data, interoperability, and avoiding confining companies to ‘cloud’ solutions. Europe’s technological independence depends on these types of regulations and standards. Not only would companies be able to protect their digital assets and make informed choices, but they would also be able to share information with each other, under suitable conditions according to the sensitivity of their data.

In the development of Gaia-X federated services and the creation of data spaces, Teralab provides its technological and human resources to validate architecture, to prototype new services on sector-specific data spaces, and to build the open-source software layer that is essential to this development. “If EDF or another critical infrastructure, like banking, wants to be able to move sensitive data into these data spaces, they will need both technical and legal guarantees.”.

Teralab, since the end of the public funding that it received until 2018, has not stopped growing, especially at European level. “We currently have a European project on health-related data on cardiovascular diseases,” says the Teralab director. The goal is for researchers in European countries who need data on these diseases to be able to conduct research via a DataHub – a space for sharing data. In the future, Teralab’s goal is to continue its work in cloud federation and to “become a leading platform for the creation of digital ecosystems,” says Anne-Sophie Taillandier.

Rémy Fauvel

interference

Interference: a source of telecommunications problems

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The growing number of connected objects is set to cause a concurrent increase in interference, a phenomenon which has remained an issue since the birth of telecommunications. In the past decade, more and more research has been undertaken in this area, leading us to revisit the way in which devices handle interference.

“Throughout the history of telecommunications, we have observed an increase in the quantities of information being exchanged,” states Laurent Clavier, telecommunications researcher at IMT Nord Europe. “This phenomenon can be explained by network densification in particular,” adds the researcher. The increase in the amount of data circulating is paired with a rise in interference, which represents a problem for network operations.

To understand what interference is, first, we need to understand what a receiver is. In the field of telecommunications, a receiver is a device that converts a signal into usable information — like an electromagnetic wave into a voice. Sometimes, undesired signals disrupt the functioning of a receiver and damage the communication between several devices. This phenomenon is known as interference and the undesired signal, noise. It can cause voice distortion during a telephone call, for example.

Interference occurs when multiple machines use the same frequency band at the same time. To avoid interference, receivers choose which signals they pick up and which they drop. While telephone networks are organized to avoid two smartphones interfering with each other, this is not the case for the Internet of Things, where interference is becoming critical.

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Different kinds of noise causing interference

With the boom in the number of connected devices, the amount of interference will increase and cause the network to deteriorate. By improving machine receivers, it appears possible to mitigate this damage. Most connected devices are equipped with receivers adapted for Gaussian noise. These receivers make the best decisions possible as long as the signal received is powerful enough.

By studying how interference occurs, scientists have understood that it does not follow a Gaussian model, but rather an impulsive one. “Generally, there are very few objects that function together at the same time as ours and near our receiver,” explains Clavier. “Distant devices generate weak interference, whereas closer devices generate strong interference: this is the phenomenon that characterizes impulsive interference,” he specifies.

Reception strategies implemented for Gaussian noise do not account for the presence of these strong noise values. They are therefore easily misled by impulsive noise, with receivers no longer able to recover the useful information. “By designing receivers capable of processing the different kinds of interference that occur in real life, the network will be more robust and able to host more devices,” adds the researcher.

Adaptable receivers

For a receiver to be able to understand Gaussian and non-Gaussian noise, it needs to be able to identify its environment. If a device receives a signal that it wishes to decode while the signal of another nearby device is generating interference, it will use an impulsive model to deal with the interference and decode the useful signal properly. If it is in an environment in which the devices are all relatively far away, it will analyze the interference with a Gaussian model.

To correctly decode a message, the receiver must adapt its decision-making rule to the context. To do so, Clavier indicates that a “receiver may be equipped with mechanisms that allow it to calculate the level of trust in the data it receives in a way that is adapted to the properties of the noise. It will therefore be capable of adapting to both Gaussian and impulsive noise.” This method, used by the researcher to design receivers, means that the machine does not have to automatically know its environment.

Currently, industrial actors are not particularly concerned with the nature of interference. However, they are interested in the means available to avoid it. In other words, they do not see the usefulness of questioning the Gaussian model and undertaking research into the way in which interference is produced. For Clavier, this lack of interest will be temporary, and “in time, we will realize that we will need to use this kind of receiver in devices,” he notes. “From then on, engineers will probably start to include these devices more and more in the tools they develop,” the researcher hopes.

Rémy Fauvel