Martí Llopart
About Martí Llopart
Martí Llopart is a Deep Learning Engineer for Speech Technologies at the Barcelona Supercomputing Center, where he focuses on optimizing deep learning models. He has a background in biomedical engineering and has contributed to various research projects, including published work in the Journal of Biophysical Reviews.
Work at Barcelona Supercomputing Center
Martí Llopart currently serves as a Deep Learning Engineer for Speech Technologies at the Barcelona Supercomputing Center, a position he has held since 2023. In this role, he focuses on optimizing deep learning models for faster inference within the Language Technologies Unit. Previously, he worked at the center as a Junior Researcher for five months in 2021 and as a Master Degree Student from 2022 to 2023. His contributions include conducting research that supports the official data warehousing performance record for Databricks.
Education and Expertise
Martí Llopart holds a Master's Degree in Technology and Engineering Management from Universitat Politècnica de Catalunya, which he completed from 2021 to 2023. He also earned a BEng in Biomedical Engineering with First Class honors from Queen Mary University of London, where he studied from 2017 to 2020. His educational background equips him with a strong foundation in engineering principles and management practices, particularly in the context of technology.
Background
Before his current role, Martí Llopart worked as a Research Assistant at Queen Mary University of London from 2020 to 2022. He also held a position as a Machine Learning Research Engineer at Bettair Cities for two months in 2023. His experience spans various roles in research and engineering, contributing to his expertise in deep learning and machine learning applications.
Achievements
Martí Llopart has made significant contributions to the field of deep learning and machine learning. He published a paper in the Journal of Biophysical Reviews, which received citations from Nature. He developed a machine learning program that identifies miRNA-mRNA interactions, surpassing existing classification methods. Additionally, he worked on improving neural network performance by over 10% as part of a team, with a paper currently in the publication process.