àlex Padrós Zamora

Research Engineer & PhD Student @ Barcelona Supercomputing Center

About àlex Padrós Zamora

Àlex Padrós Zamora is a Research Engineer and PhD student at the Barcelona Supercomputing Center, specializing in Explainable Artificial Intelligence. He has a background in data science and mathematics, with previous experience in product data analysis and business intelligence.

Current Role at Barcelona Supercomputing Center

Currently, Padrós Zamora works as a Research Engineer and PhD Student at the Barcelona Supercomputing Center. He has held this position since 2024, focusing on advanced research in the field of Explainable Artificial Intelligence. His work involves the development of AI models that prioritize transparency and comprehensibility in decision-making processes.

Previous Experience in Data Analysis and AI Engineering

Prior to his current role, Padrós Zamora gained experience as a Product Data Analyst at Zitro from 2021 to 2022. He also worked as a Business Intelligence professional at Schneider Electric from 2020 to 2021. Additionally, he served as an AI Engineer and Data Scientist at BDcare - Big Data Research from 2022 to 2023, where he applied his expertise in data science and AI.

Educational Background in Data Science and Mathematics

Padrós Zamora holds a Master's degree in Data Science from the Universitat de Barcelona, which he completed in 2021. He also earned a Bachelor's degree in Mathematics from the Universitat Autònoma de Barcelona, graduating in 2020. Furthermore, he participated in a Summer Course on Introduction to Nanotechnology and Nanoscience at the same university in 2015.

Specialization in Explainable Artificial Intelligence

Padrós Zamora specializes in designing self-explanatory neural network models aimed at enhancing transparency in AI systems. His research combines academic insights with practical applications, focusing on the development of AI models that provide clear explanations for their decision-making processes. This work contributes to the advancement of Explainable Artificial Intelligence.

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