Ricardo Baptista

Ricardo Baptista is an Assistant Professor at the University of Toronto's Department of Statistical Sciences and a Faculty Affiliate at the Vector Institute. He joined the University of Toronto in Fall 2025 after serving as an instructor at Caltech in Computing and Mathematical Sciences since 2022.

Education:

• PhD in Computational Science and Engineering from MIT (advised by Youssef Marzouk)
• BASc in Engineering Science from University of Toronto

Dr. Baptista's research focuses on developing mathematical foundations for probabilistic machine learning models with applications to science and engineering problems. He specializes in computational measure transport, with particular interest in generative models, inverse problems, and statistical computing. His work bridges theoretical statistics with practical applications in complex systems.

His recent publications demonstrate a strong emphasis on the mathematical underpinnings of machine learning, particularly in contrastive learning frameworks, conditional simulation techniques, monotone transport maps, and nonlinear ensemble filtering methods. These works reveal a consistent theme of developing rigorous theoretical frameworks for probabilistic modeling while maintaining practical applicability to real-world problems.

Dr. Baptista has collaborated with prominent researchers including Andrew Stuart, Houman Owhadi, and Youssef Marzouk, and has presented his work at significant venues such as the CIRM-Marseille Digital Twins for Inverse Problems Workshop and the USNCCM Large-Scale TTA colloquium. Prior to his faculty position, he was a von Kármán instructor at Caltech and a Postdoctoral Scientist at Amazon Search.