BCAM hosts the 1st IN-DEEP week

• The workshop was celebrated from the 21 to the 25 of October
• In – Deep is a MSCA Doctoral Network project for training PhD students in Deep Learning techniques
• The workshop was organized by Judit Muñoz-Matute, Postdoctoral Researcher at the Basque Center for Applied Mathematics.

The workshop of the IN-DEEP project, a a MSCA Doctoral Network project for training PhD students in Deep Learning techniques, recently brought together students and consortium members in an exclusive training and scientific collaboration event. IN-DEEP, focused on providing high-level training to nine PhD students, seeks to develop explainable, knowledge-based Deep Learning algorithms to rapidly solve inverse problems governed by partial derivative equations (PDEs). This area of research has grown significantly in the last five years, due to its promising results in applications such as image recognition and natural language processing. The workshop was organized by Judit Muñoz-Matute, Postdoctoral Researcher at the Basque Center for Applied Mathematics.

The event, structured over five days, included a variety of activities that fostered collaboration and the acquisition of key knowledge in different application areas. During the first and second days, scientific advances in artificial intelligence applications, high-performance computing and inverse problems applied to geophysics, smart cities and health were presented. On the third day, participants took part in a technical course focused on the implementation of deep solvers of PDEs. On the fourth day, the PhD students received training in research methodology and cross-cutting skills tools. Also, on Thursday, the workshop dedicated part of the day to various trainings, addressing critical issues in gender equality and open science.

In the gender session, the importance of fostering equality in the work environment was discussed, highlighting the rights and responsibilities that each person has to contribute to an environment of respect and equity. The value of creating a diverse and respectful space that fosters collaboration between male and female colleagues was also highlighted. The persistent gender gap in STEM disciplines and the lack of female role models to inspire new generations was also brought to the table, recalling the need to work on solutions that promote a more equitable representation.

On the other hand, a training on Open Science took place within the framework of the 1st IN-DEEP Week, coinciding with the International Open Access Week. This meeting included a talk led by Miguel Benítez, BCAM Project Manager, in which an overview of Open Science and its implications was given. The session focused on the relevance of datasets, the difference between publishers and repositories, and the need to make appropriate acknowledgements in scholarly works, thus contributing to a more accessible and collaborative scientific knowledge.

This workshop has allowed to consolidate the collaboration between academic institutions and participating technology centers and companies, thus boosting European research activity in Deep Learning technologies applied to inverse problems.

IN-DEEP is a European Doctoral Network composed of nine doctoral candidates (DCs) and top scientists with complementary areas of expertise in applied mathematics, artificial intelligence, high-performance computing, and engineering applications. Its main goal is to provide high-level training to the nine DCs in designing, implementing, and using explainable knowledge-driven Deep Learning algorithms for rapidly and accurately solving inverse problems governed by PDEs. This area of research has experienced tremendous growth worldwide in the last lustrum due to its promising results in a plethora of applications, such as image recognition and natural language processing. IN-DEEP will focus on real-life high-risk problems arising from applications related to geophysics, smart cities, and health. By doing so, we will boost Europe’s training and research activities in new Deep Learning technologies for inverse problems.

Project Management Team: David Pardo (Project Coordinator, UPV/EHU), Izaskun Ibarbia (Administrative Coordinator, UPV/EHU), Magdalena Strugaru (Project Manager, UPV/EHU)

Ethics Manager: Francisco Chinesta (ENSAM)

Scientific Committee: David Pardo (UPV/EHU), Alessandro Reali (UniPV), Judit Muñoz-Matute (BCAM), Maciej Paszynski (AGH), Kristopher Van der Zee (UoN), Stefano Berrone (POLITO), Javier Del Ser (Tecnalia), Gwendal Jouan (Siemens), and Francisco Chinesta (ENSAM) 

Supervisory Board: David Pardo (UPV/EHU), Alessandro Reali (UniPV), Judit Muñoz-Matute (BCAM), Maciej Paszynski (AGH), Kristopher Van der Zee (UoN), Stefano Berrone (POLITO), Javier Del Ser (Tecnalia), Gwendal Jouan (Siemens), Francisco Chinesta (ENSAM), Ivan Bioli (DC representative, UniPV)