Javier Burroni

I have a PhD in Computer Science from the University of Massachusetts Amherst. My earlier degrees in Economics and Actuarial Science gave me a strong foundation in probability, which strengthened my research and sparked my interest in real-world problems. Over the past two decades, I have contributed to machine learning, data science, programming languages, and information security through research, teaching, and industry projects. I began this work in southern South America, where it has since gained international recognition. I currently focus on two research areas: developing more efficient machine learning algorithms and advancing programming languages as powerful tools of thought, particularly in auto formalization.

"Sample Average Approximation for Black-Box VI". Javier Burroni, Justin Domke, and Daniel Sheldon In Uncertainty in Artificial Intelligence, 2024 

"U-Statistics for Importance-Weighted Variational Inference"
Javier Burroni, Kenta Takatsu, Justin Domke, and one more author
Transactions on Machine Learning Research, 2023 

"Compiling Stan to generative probabilistic languages and extension to deep probabilistic programming"
Guillaume Baudart, Javier Burroni, Martin Hirzel, and two more authors in PLDI, 2021 

(*) Links to sites outside smith.edu may not conform to Smith College accessibility requirements (WCAG 2.1 AA standard)