Hi there! I'm Ricardo, a Machine Learning researcher and engineer. I currently live in SF and work at Cartesia. Thanks to a fellowship by la Caixa Foundation, I earned a Master in Machine Learning at Carnegie Mellon University, where I worked under the supervision of Albert Gu and Andrej Risteski.

Recently, I'm deeply engaged in two areas:

• Inference Engineering: How can we deploy complex Machine Learning pipelines in production? I'm working a lot on optimizing inference, as well as building validation frameworks to ensure quality.
• Bridging fundamental understanding and applied research: How can great fundamental principles lead to better Machine Learning products, and vice versa? The Machine Learning community often emphasizes either practice or theory, and both perspectives bring valuable insights. Practitioners excel at building systems that work in the short term, yet might lack clarity and cleanliness in the long term. Theorists offer guiding principles that help us understand why things work, but they sometimes rely on simplifying assumptions and do not give full credit to what empirically works best. I’m particularly interested in doing research that connects these two worlds.