Ciao! I work as an Applied Scientist (III) at Amazon. I spend most of my time thinking about how we can make causal inference more efficient in online settings. In particular, I am interested in settings where data is noisy:

• Leveraging additional covariate information in online A/B testing (KDD, 2023)
  
• A Nonparametric Bayes Approach to Online Activity Prediction, arXiv preprint
• Improved prediction of future user activity in online A/B testing, arXiv preprint

and where interference is at play, like in online marketplaces:

• Experimental Design in Marketplaces (Statistical Science, 2023) (see also “Multiple Randomization Designs”, Bajari et al. (2021) and “Multiple Randomization Designs: Estimation and inference with interference”)
• Multi-Armed Bandits with Network Interference, NeurIPS 2024 (upcoming), with A. Agarwal² and J. Whitehouse.
• How do Advertisers React to Changes in Ad Auction Pricing? Evidence from a Clustered Multiple Randomization Experiment, TSMO at KDD 2024, Hut et al. 2024.

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.