Daniel Ibáñez

Daniel Ibáñez has a diverse work experience in the fields of deep learning, machine learning, and data science. Their most recent role is as a Deep Learning Engineer at Merlyn Mind, where they apply their expertise in developing and implementing deep learning models. Prior to that, they worked as a Machine Learning Researcher at dMetrics and as a Senior Machine Learning Engineer (NLP) at ModMed, where they specialized in text classification, clustering, summarization, and question-answering tasks.

Daniel also has experience as a Data Scientist (Research) at the Instituto Complutense de Ciencias Musicales. Daniel has worked extensively in the field of Natural Language Processing (NLP) as a Machine Learning Engineer, both at the Translation Centre for the Bodies of the European Union and at ReadableAI, where they focused on text simplification and the implementation of research papers.

Prior to their work in NLP, Daniel held roles as a Machine Learning Technical Writer at Baeldung and as a CTO Partner at ApeLucy. Daniel also has experience as a Senior Backend Software Engineer at Pentasoft Group and as a CTO & Software Development Engineer at relevante.me. Throughout their career, Daniel has demonstrated strong technical skills in machine learning, deep learning, and software engineering, while also showcasing leadership abilities in various managerial positions.

Daniel Ibáñez obtained a Computer Engineer Degree in Information Science/Studies from Universidad Carlos III de Madrid, where they studied from 2002 to 2007. Prior to that, they attended Real Conservatorio Superior de Música de Madrid from 1994 to 2001, where they pursued a degree in French Horn interpretation.

In terms of additional certifications, Daniel has obtained several in the field of technology. These include certifications such as "Generative AI with Large Language Models" from DeepLearning.AI in June 2023, "dbt Fundamentals" from dbt Labs in May 2023, "Fundamentals of Machine Learning for Healthcare" from Stanford Online in April 2023, "Introduction to Clinical Data" from Stanford Online in April 2023, "Introduction to Healthcare" from Stanford Online in February 2023, "Understanding and Visualizing Data with Python" from the University of Michigan in July 2022, "Fundamentals of Kubernetes Deployment" from LearnQuest in April 2022, "Introduction to Kubernetes" from edX in April 2022, "Machine Learning Data Lifecycle in Production" from DeepLearning.AI in April 2022, "Apply Generative Adversarial Networks (GANs)" from DeepLearning.AI in February 2022, "Introduction to Machine Learning in Production" from DeepLearning.AI in February 2022, "Artificial Intelligence Algorithms Models and Limitations" from LearnQuest in January 2022, "Artificial Intelligence Data Fairness and Bias" from LearnQuest in January 2022, "Artificial Intelligence Privacy and Convenience" from LearnQuest in January 2022, "Build Basic Generative Adversarial Networks (GANs)" from DeepLearning.AI in January 2022, "Build Better Generative Adversarial Networks (GANs)" from DeepLearning.AI in January 2022, "Custom and Distributed Training with TensorFlow" from DeepLearning.AI in November 2021, "Custom Models, Layers, and Loss Functions with TensorFlow" from DeepLearning.AI in April 2021, "Natural Language Processing with Attention Models" from DeepLearning.AI in April 2021, and "Sentiment Analysis with Deep Learning using BERT" from Coursera in February 2021.