Guillermo Ramajo Fernández

AI Research Scientist

• Develop machine learning models for computational biology, molecular representation learning and drug discovery applications.
• Designed a self-supervised transfer learning framework for collision cross-section prediction using 61,863 experimentally measured molecular samples.
• Achieved a 3.20% mean relative prediction error while improving cross-dataset generalization through learned molecular representations.
• Develop PyTorch-based deep learning pipelines for training, evaluation and analysis of biomedical AI models.
• Published international conference research in machine learning for computational metabolomics and biomedical AI.

Computer Science Teacher

• Designed and delivered programming and computer science courses focused on software development, algorithms and computational thinking.
• Mentored students through practical programming projects and hands-on technical training.

PhD in Artificial Intelligence

Research focus: Foundation models, self-supervised learning, molecular representation learning and agentic AI systems for biomedical discovery.

Master's Degree in Artificial Intelligence

Specialization in machine learning, deep learning, computer vision, natural language processing and predictive modeling.

Master's Degree in Integrative Synthetic Biology

Applied molecular biology and genome engineering techniques to research projects in regenerative medicine, Alzheimer's disease and cancer immunotherapy.

Molecular Foundation Models Evaluation

Research framework exploring learned molecular representations and machine learning approaches for chemical property prediction.

AI Cloud Classification System

Computer vision application using artificial intelligence to identify cloud formations from images.