Summary
Manfred Gonzalez-hernandez is a PhD researcher and FWO fellow with eight years of experience applying machine learning and computer vision to real-world problems across academia and industry. He leads a funded research program on energy-efficient generative AI, blending diffusion and flow-matching models with Bayesian active learning to reduce training data and prioritize informative samples. His background spans MLOps and production ML for retail personalization, RPA, and applied agro-industrial computer vision—work that produced conference publications and deployed PyTorch object-detection models for crop counting. Manfred combines perseverance, analytical rigor, and project-management experience to translate research prototypes into validated cross-industry applications, including manufacturing anomaly detection and medical diagnostic simulations. He is comfortable moving between low-level model innovation and cloud-based production environments (Databricks, Python/PyTorch), which helps him bridge sustainability-focused research and practical deployment. An uncommon strength is his track record of securing competitive grants and turning them into multidisciplinary collaborations that impact both industry and healthcare.
8 years of coding experience
4 years of employment as a software developer
Master of Computer Science in Artificial Intelligence Artificial Intelligence, Master of Computer Science in Artificial Intelligence Artificial Intelligence at University of Ljubljana, Faculty of Computer and Information Science
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Ghent University
Bachelor's degree Computer Software Engineering, Bachelor's degree Computer Software Engineering at Universidad de Costa Rica UCR
English, Spanish