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Francisco Utrera Antón is a distinguished AI researcher and software engineer with extensive experience in Machine Learning, Deep Learning, Natural Language Processing (NLP), and MLOps. He made significant contributions to Google's TensorFlow framework, particularly focusing on the tf.data API for efficient data input pipelines, and also worked on large-scale NLP models. Currently, he is an Associate Professor (Profesor Contratado Doctor) at the University of Málaga, where he continues his research and teaching in artificial intelligence, focusing on efficient and scalable machine learning systems, NLP, and the intersection of AI with other scientific domains. He is passionate about open-source software and bridging the gap between cutting-edge research and real-world applications.
Francisco Utrera Antón's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
As a Software Engineer at Google, Francisco was a core contributor to the TensorFlow Extended (TFX) ecosystem, notably leading efforts and making significant contributions to the `tf.data` API, enhancing its performance, flexibility, and usability for building highly efficient input pipelines for ML models.
Contributed to the development and optimization of large-scale Natural Language Processing models during his tenure at Google, working on projects that pushed the boundaries of language understanding and generation.
Holds an academic position contributing to AI research and education, mentoring students, and leading projects in areas like efficient deep learning, NLP, and AI applications. He actively publishes research in reputable venues.
Strongly advocates for and actively contributes to open-source AI projects, including TensorFlow and other related libraries, fostering community collaboration and knowledge dissemination. His work is visible on platforms like GitHub.
Authored and co-authored multiple research papers and articles in the field of Artificial Intelligence and Machine Learning, presented at international conferences and published in recognized journals, contributing to the scientific advancement of the field.
University of California, Berkeley - Year 2008
Universidad Autónoma de Madrid - Year 2002
Università degli Studi di Milano-Bicocca - Year 2005
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