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Meeting with Narimane Gassa, PhD student in the modeling team at Liryc and winner of the Machine Learning Challenge 2021

Narimane, what is your position at Liryc?

I am a PhD student at the University of Bordeaux, part of the CARMEN team at INRIA and in the Modeling team at Liryc.

When did you join the institute?

I joined Liryc on September, 2020 when I started my thesis in applied mathematics in cardiology.

Could you tell us more about your professional background?

I studied in Tunisia starting with a math/physics preparatory class and I joined the engineering school of Tunis in applied mathematics with a specialization in modeling, optimization and scientific computing. I completed my last year of engineering school with an internship at INRIA Bordeaux Sud-Ouest, in the team working on Liryc's research work. Afterwards, I started my thesis.

What does your work consist of at Liryc?

My thesis work consists in developing new methods for the diagnosis and prognosis of atrial arrhythmia.

At Liryc, my research focuses on the development of simplified mathematical models to describe the electrical activity of the heart. This simplification and a reduced computation time will allow a direct application to clinical cases.

We are also working on the inverse problems of electrocardiography. We reconstruct the cardiac mechanisms using non-invasive data collected directly from the torso surface.

In your opinion, what is the main challenge of your research?

All the modeling work is about figuring out what's going on in the heart to contribute at the end to a better patient management.

You are the winner of the Machine Learning Challenge 2021, can you tell us more about it?

This challenge coordinated by the Professor Rémi Dubois at Liryc with the cardiac pacing teams of the Bordeaux University Hospital was created to develop a Machine Learning or Deep Learning model that would be able to predict if the recorded signals of the implantable cardiac prostheses are meaningful. It is important to know that different noises, such as extracardiac muscle activity or interference from electrical devices, can modify the ECG curve and disturb the quality of cardiac monitoring. The purpose of this project is to improve the remote monitoring of pacemakers and implantable defibrillators, thanks to an artificial intelligence algorithm, to optimize patient management by helping medical teams to focus only on the important signals.

What did participating in the challenge mean to you and your research work?

The Machine Learning Challenge was the very first challenge of my thesis. Until then I was working on models that described the electrical activity of the heart using mathematical equations and computer science. This was the first time I worked with real anonymized clinical data.

This was a great opportunity for me! I am passionate about mathematics and I have always wanted to apply it in medicine to make a real difference in the care and lives of patients!

After my thesis, I would like to continue working in the research field as an R&D engineer.

What advice would you give to younger generations who would like to become researchers?

In research you have to be patient and determined. You will often start looking in a direction without being sure of the result, but the interesting part is to understand why things work or don't, to step back and to look at what is going on, that's how you learn.

What does it mean to be a woman in science?

This is a big theme. Women are not as prevalent as men in STEM (science, technology, engineering and math), but I'm glad to see that this is happening.

I'm lucky enough to have grown up in a society where women are doctors, scientists and engineers, so I am convinced that women could do anything they wanted to be. Stereotypes change when we get new observations!

We should talk more and more about successful women to break these stereotypes.

What is your greatest pride?

My greatest pride which is more a plan for the future, would be to contribute to the improvement of the well-being of patients through our current research work. 

To conclude with a more “personal fun fact”, what was your favorite subject at school when you were a child?

It was mathematics! Since I was a little girl, I loved mental arithmetic.

Published on 10 August 2021