Ciao! I work as an Applied Research Scientist at Amazon. I spend most of my time thinking about how we can make causal inference more efficient in online settings, especially when the data is noisy and interference is at play. To that end, I have been working to quantify the efficiency gains obtained by leveraging additional covariate information in online A/B testing, and how to effectively design online experiments when multiple competing subpopulations of agents are present (see also “Multiple Randomization Designs”, Bajari et al. (2021)).
I obtained my PhD in Electrical Engineering and Computer Science from MIT in 2021, working in CSAIL under the supervision of Tamara Broderick. My thesis developed novel Bayesian nonparametric methods for prediction and experimental design in the context of genomics studies (see also “Bayesian nonparametric strategies for power maximization in rare variants association studies ”). Before joining MIT, I was an Allievo student at the Collegio Carlo Alberto and at the University of Torino.
Here you can find a copy of my CV. You can contact me via email at lo [dot] masoero at gmail dot com.
PhD in Computer Science, 2021
MA in Statistics and Applied Mathematics, 2016
Collegio Carlo Alberto