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9 changes: 7 additions & 2 deletions config/_default/params.yaml
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## Biography

<div style="text-align: justify">
Welcome ! I am a Full Professor at <a href="http://www.univ-ubs.fr"> University Bretagne Sud</a> since 2018. I obtained my PhD degree in 2002 from INSA Rennes and my 'Habilitation à diriger des recherches' in. 2013, on the topic of computer graphics and animation (avatars, crowds), with a specialization in data-driven methods. I now lead the <a href="https://www-obelix.irisa.fr">Obelix</a> team in <a href="https://www.irisa.fr/">IRISA</a>, which research contributions lie at the crossroad of machine learning and Earth Observation. I am an experienced researcher in the domain of machine learning and AI. Among others, I have published several papers in top tier machine learning conferences (NeurIPS, ICLR, ICML, AISTATS, etc.), computer vision (IEEE TPAMI, ECCV, ACCV) and remote sensing (IEEE TGRS, ISPRS journal). From 2014, I developed an expertise in the domain of optimal transport and related applications to machine learning. From 2020, I am the principal invesitgator in an ANR Chair program on AI (OTTOPIA), on the topic of applied optimal transport for Remote Sensing.
Welcome ! I am a Full Professor at <a href="http://www.univ-ubs.fr"> University Bretagne Sud</a> since 2018. I obtained my PhD degree in 2002 from INSA Rennes and my 'Habilitation à diriger des recherches' in 2013, on the topic of computer graphics and animation (avatars, crowds), with a specialization in data-driven methods. I now lead the <a href="https://www-obelix.irisa.fr">Obelix</a> team at <a href="https://www.irisa.fr/">IRISA</a>, which research contributions lie at the crossroad of machine learning and Earth Observation. I am an experienced researcher in the domain of machine learning and AI. Among others, I have published several papers in top tier machine learning conferences (NeurIPS, ICLR, ICML, AISTATS, etc.), computer vision (IEEE TPAMI, ECCV, ACCV) and remote sensing (IEEE TGRS, ISPRS journal). From 2014, I developed an expertise in the domain of optimal transport and related applications to machine learning. From 2020, I am the principal investigator in an ANR Chair program on AI (OTTOPIA), on the topic of applied optimal transport for Remote Sensing.
</div>
</br>

### Scientific interests:
- Optimal transport, Statistical learning, kernel methods
- Manifold and geometric approaches to machine learning
- Applications to computer graphics, vision and remote sensing
- Applications to computer graphics, vision and remote sensing

### Code production
As a computer scientist, I definitely believe in the power of open-source and reproducibility as a basis of science. Though my current activities will not let me hand on code as much as I want to, I still enjoy coding in C++, Cuda kernels or PyTorch. Take a look at the [coding section](code/) to know more.
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>}}
To install it, simply `pip install pot`

- [TorchDR](https://github.com/TorchDR/TorchDR) is an open-source dimensionality reduction (DR) library using PyTorch. Its goal is to accelerate the development of new DR methods by providing a common simplified framework. It was mainly created by [Hugues Van Assel](https://huguesva.github.io/), and I am contributing to it.

{{< figure
src="torchdr.png"
alt="Torch Dimensionality Reduction"
>}}


### Paper codes
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<figure >
<div class="d-flex justify-content-center">
<div class="w-100" ><img alt="Python optimal transport" srcset="
/media/pot_huaac8f8eb29d86028c5241953d93f9fff_18059_e81a6c57d78488fe33913ad1c0e04d8b.webp 400w,
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width="378"
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loading="lazy" data-zoomable /></div>
</div></figure>

To install it, simply <code>pip install pot</code></p>
<ul>
<li><a href="https://github.com/TorchDR/TorchDR" target="_blank" rel="noopener">TorchDR</a> is an open-source dimensionality reduction (DR) library using PyTorch. Its goal is to accelerate the development of new DR methods by providing a common simplified framework. It was mainly created by <a href="https://huguesva.github.io/" target="_blank" rel="noopener">Hugues Van Assel</a>, and I am contributing to it.</li>
</ul>


















<figure >
<div class="d-flex justify-content-center">
<div class="w-100" ><img alt="Torch Dimensionality Reduction" srcset="
/media/torchdr_hu99f3ff49c9bca213f898f69a4e52c277_728954_2073fc1ea14396ffad7bdf2c0650b808.webp 400w,
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width="760"
height="176"
loading="lazy" data-zoomable /></div>
</div></figure>

<h3 id="paper-codes">Paper codes</h3>
<p>I am deeply committed to reproducible research. In general, most of the codes associated to my papers are available online, if not listes in the following:</p>
<ul>
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