I work as a Senior Applied Scientist at Amazon. I spend most of my time working on causal inference and experimental design for large-scale online systems. In particular, I am interested in settings where data is noisy and where interference is at play, like in online marketplaces. I am also exploring how agentic AI frameworks can integrate into online experimentation pipelines to accelerate and improve decision-making.

Previously, I obtained my PhD in Electrical Engineering and Computer Science from MIT in 2021, working in CSAIL under the supervision of Tamara Broderick, working on Bayesian nonparametric methods for prediction and experimental design in the context of genomics 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.

Some of my recent research includes: Improving efficiency with noisy data:

• Demonstration Experiments (2026)
• Leveraging covariate information in A/B testing (KDD 2023)
• Statistical power calculations with effect size priors (CODE@MIT 2025)
• Bayesian early termination for experiments (CODE@MIT 2025)
• Online activity prediction via Indian buffet processes (Annals of Applied Statistics, to appear)

Handling interference in two-sided marketplaces:

• Experimental design in marketplaces (Statistical Science, 2023)
• Multiple randomization designs (JRSS-B, 2026)
• Multi-armed bandits with network interference (NeurIPS 2024)
• Multiple-unit switchback experiments (2025)
• Measuring spillover effects in marketplaces (KDD 2024)
• Covariate-adjusted estimators for multiple randomization designs (2026)

Agentic AI for experimentation:

• Multi-agent documentation maintenance (IAAI 2027)
• Can AI agents simulate A/B test outcomes? (WAB@COLM 2026, CODE@MIT 2026)