Francisco J. R. Ruiz

Francisco J. R. Ruiz is a Postdoctoral Research Scientist at Columbia University's Department of Computer Science and affiliated with the University of Cambridge's Engineering Department. He holds a Marie-Sklodowska Curie Fellowship under the EU Horizon 2020 program. His research focuses on statistical machine learning, Bayesian modeling, and inference, with notable contributions to stochastic inference for large categorical distributions.

He completed his Ph.D. and M.Sc. at the University Carlos III in Madrid. His work addresses challenges in large-scale machine learning, including computational efficiency for high-dimensional categorical data. Recent contributions include methods like 'Augment and Reduce' for optimizing categorical distribution inference.

• Key Collaboration: Works with David Blei (Columbia) and Zoubin Ghahramani (Cambridge)
• Technical Expertise: Probabilistic modeling, neural networks, and scalable algorithms

His seminar presentation at Columbia highlighted advancements in reducing computational complexity for large categorical distributions through latent variable augmentation and variational inference techniques.