I am Riccardo, a Research Fellow at Harvard University conducting research in Causal Inference and Machine Learning. My aim is to combine the strengths and potentials of both these fields to shed new light on some of the most important open challenges in Artificial Intelligence --i.e., Robustness, Transferability, and Fairness-- with high and direct impact on medical applications and, more generally, Public Health. Among these challenges, I am currently focusing on Interpretable Inference of Heterogeneous Treatment Effects and corresponding studies on the causal effect of air pollution on our health.
This is my personal website where you can read about my research interests and background through my past experiences and accomplished projects. Enjoy your reading and do not hesitate to contact me for any discussion.
Presenting `Projecting the climate penalty on PM2.5 pollution with spatial deep learning` in the workshop on `Tackling Climate Change with Machine Learning' at ICLR 2023.
[New Position] Research Fellow at Harvard University.
Successfully defending my Master's Thesis titled `Causal Rule Ensemble: interpretable discovery and estimation of Heterogeneous Treatment Effects'.
Volunteering at NeurIPS 2022.
Releasing Causal Rule Ensamble package on CRAN [1700+ downloads].