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Meet the people behind knowlEdge: Marta Barroso

Marta Barroso

What is your personal background?

I obtained my computer science degree at the Polytechnic University of Barcelona and subsequently pursued a master’s degree in artificial intelligence at the same university. I conducted my undergraduate thesis while working on a patient monitoring project using biosensors at one of Barcelona’s most important psychiatric hospitals, Benito Menni. After completing my master’s degree, I joined the National Research Center, BSC, where I have been working for over three years. Since then, I have been involved in numerous national and international projects, including my master’s thesis project, in which we conducted an analysis of COVID-19 data involving more than 60 Spanish hospitals.

What is your organization’s role in knowlEdge?

BSC participates in the project as a social researcher and task leader of WP4, primarily contributing to the development of the component responsible for enabling the training, evaluation, and testing of AI models. Additionally, we conduct benchmarking analysis of various models to explore the best deployment options for these models and estimate resource consumption. Finally, we are involved in the development of models that meet the needs of the pilots.

What fascinates you about Artificial Intelligence for manufacturing?

What fascinates me about Artificial Intelligence in manufacturing is its power to revolutionize the entire industry. It’s not just about improving efficiency or reducing costs; it’s about transforming the way we create, design, and produce things. AI has the potential to make manufacturing processes smarter, more adaptable, and increasingly responsive to changing demands. It’s a bridge between traditional manufacturing and the future, enabling customization, sustainability, and innovation on an unprecedented scale. This convergence of technology and manufacturing holds the promise of reshaping industries and redefining what’s possible in the world of production.

What are your expectations in knowlEdge?

I hold great optimism for our endeavors in the realm of knowledge. Personally, I have strong convictions that our project can make a substantial mark enhancing the adoption of AI within the manufacturing field.  Within the knowledge framework, the BSC (Barcelona Supercomputing Center) components are poised to execute the most of the AI lifecycle, from calling data preprocessing until model training, evaluation and deployment of AI models in several environments such as Edge, Fog and Cloud, thereby empowering companies to fully capitalize on the advantages of AI without the need of having artificial intelligence previous knowledge.

Which target groups can benefit from knowlEdge?

KnowlEdge is tailored to cater to a broad spectrum of user groups reliant on AI models within their daily manufacturing operations. Specifically, it offers advantages to companies that employ AI models for their products or services by furnishing an infrastructure that ensures the seamless, standardized, and secure transition of these models across various environments. Furthermore, KnowlEdge extends its benefits to business analysts and shopfloor operators, granting them access to AI models and the capacity to oversee their performance. Business analysts can leverage the platform for data analysis and AI model creation, optimizing production processes and enhancing decision-making. Simultaneously, shopfloor operators can harness AI models to enhance quality control, diminish waste, and elevate productivity.

What is your vision beyond knowlEdge?

Beyond the realm of knowledge, my aspiration is to immerse myself in the advancement of AI models designed to address the specific requirements of pilots with utmost efficiency and effectiveness. This endeavor will encompass meticulous considerations including but not limited to explainability, model performance, model complexity, and model bias. My primary objective is not solely to engage actively in the progression of AI solutions within these forthcoming projects, but also to diligently oversee their comprehensive evaluation, long-term maintainability, and the facilitation of knowledge sharing surrounding these models.