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<title> First Inria-DFKI European Summer School on AI</title>
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<strong>First Inria-DFKI European Summer School on AI (IDAI 2021)</strong><strong></strong><br>
<p><b> Trustworthy AI</b> and <b>AI for Medicine</b></p>
<strong> Palaiseau, France</strong><br>
<strong> July 20-23, 2021</strong><br>
<b> <a class="moz-txt-link-freetext"
href="https://idessai.inria.fr/">https://idessai.inria.fr/</a></b><br>
<p><b> Registration deadline: April 19, 2021</b> <br>
</p>
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<br>
<p>IDAI 2021 inaugurates a series of yearly Summer Schools organized
by the two renowned German and French AI institutes, DFKI and
Inria. It stands out from the crowd of offerings for AI students
in several respects:<br>
</p>
<ul>
<li>We ensure a good balance in the number of participants and
instructors: participants will have the opportunity to join a
community of like-minded people and, at the same time, they will
be in close contact with the experts.</li>
<li>Our program features a line-up of courses focused on two
themes, Trustworthy AI and AI for Medicine, which are at the
forefront of socio-economic issues related to AI.</li>
<li>On top of the latest methodological advances and the shared
vision of the future that both organizing institutes have to
offer, IDAI 2021 will be practically oriented. We will achieve
this through hands-on courses and the involvement of industry
practitioners and innovators.</li>
<li>Participants will be offered to the opportunity to present
their work to each other in dedicated poster/demo sessions.<br>
</li>
</ul>
<p> Trustworthy AI and AI for Medicine will take place in two
parallel tracks. There will be plenty of opportunities to exchange
between these two tracks at coffee breaks, meals and social
events, as well as through joint cross-track sessions.</p>
<br>
<strong>TARGETED AUDIENCE</strong><br>
<p>IDAI 2021 was designed for PhD students in all areas of AI,
including machine learning, knowledge representation and
reasoning, search and optimisation, planning and scheduling,
multi-agent systems, natural language processing, robotics,
computer vision, and other areas. PhD students in other fields,
MSc students, postdocs, and researchers in industry are also
welcome.<br>
</p>
<br>
<strong>VENUE</strong>
<p>IDAI 2021 is currently planned as a fully in-person event, which
will take place at the Inria Saclay Île-de-France research center,
close to Paris. Remote attendance will not be possible.</p>
<p>In case the pandemic will still not allow for an in-person event,
IDAI 2021 will take place as a fully virtual event at the same
dates instead. We are closely monitoring the situation and will
strive to make this decision as early as possible.</p>
<br>
<strong>CONFIRMED KEYNOTES AND SPEAKERS</strong><strong></strong>
<p>Cross-track keynotes: </p>
<p><strong></strong></p>
<ul>
<li>Mihaela van der Schaar (University of Cambridge) - Why
medicine is creating exciting new frontiers for machine learning
and AI</li>
<li>Joanna Bryson (Hertie School) - AI ethics</li>
</ul>
<p>Trustworthy AI track (to be completed):</p>
<ul>
<li>Serge Abiteboul (Inria) - Responsible data analysis
algorithms: a realistic goal?</li>
<li>Simon Burton (Fraunhofer IKS) - Safety, complexity, AI and
automated driving - holistic perspectives on safety assurance</li>
<li>Michèle Sebag (CNRS - LISN) - Why and how learning causal
models</li>
<li>Patrick Gallinari (Sorbonne University and Criteo AI Lab) -
Deep learning meets numerical modeling</li>
<li>Christian Müller (DFKI) - Explaining AI with narratives</li>
<li>Catuscia Palamidessi (Inria) and Miguel Couceiro (University
of Lorraine) - Addressing algorithmic fairness through metrics
and explanations</li>
<li>Guillaume Charpiat (Inria), Zakaria Chihani (CEA), and Julien
Girard-Satabin (CEA) - Formal verification of deep neural
networks: theory and practice</li>
<li>Hatem Hajri (IRT SystemX) - Adversarial examples and
robustness of neural networks<br>
</li>
</ul>
<p>AI for Medicine track (to be completed):</p>
<ul>
<li>Gerd Reis (DFKI) - AI in Medicine - An engineering perspective</li>
<li>Marco Lorenzi (Inria) - Federated learning methods and
frameworks for collaborative data analysis</li>
<li>Gaël Varoquaux (Inria) - Dirty data science: machine learning
on non-curated data</li>
<li>Thomas Moreau and Demian Wassermann (Inria) - Introduction to
neuroimaging with Python</li>
<li>Francesca Galassi (Inria) and Rutger Fick (TRIBVN Healthcare)
- Domain adaptation for the segmentation of multiple sclerosis
lesions in brain MRI.</li>
<li>Tim Dahmen (DFKI) - Bio-mechanical simulation for
individualized implants and prosthetics</li>
<li>Elmar Nöth (Friedrich-Alexander-University Erlangen-Nuremberg)
- Automatic analysis of pathologic speech – from diagnosis to
therapy</li>
<li>Pierre Zweigenbaum (CNRS - LIMSI) - NLP for medical
applications<br>
</li>
</ul>
Open discussion with industry (to be completed):<br>
<ul>
<li>Juliette Mattioli (Thales) and Frédéric Jurie (Safran) -
Industry use cases involving trusted AI</li>
<li>Boris Dimitrov (Check Point Cardio) - Real-time online patient
tele-monitoring<br>
</li>
</ul>
<strong><br>
</strong><strong>FEES AND REGISTRATION</strong><br>
<p>Our fees are all-inclusive and may optionally include
accomodation.<br>
</p>
<p>For more details and to register, see <a
class="moz-txt-link-freetext"
href="https://idessai.inria.fr/registration/">https://idessai.inria.fr/registration/</a>
(deadline: April 19).<br>
</p>
<p>To ensure a good balance in the number of participants and
instructors and maximize the chances of interaction, the number of
attendees is limited to 50 per track. Applicants will be selected
on the grounds of diversity and benefit gained from attending the
selected track.</p>
<br>
<strong>ORGANIZERS</strong><br>
<p>Co-organized by: Inria, DFKI, Dataia, IRT SystemX</p>
Contact us: <a class="moz-txt-link-abbreviated"
href="mailto:idessai-contact@inria.fr">idessai-contact@inria.fr</a>.<br>
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