I am an Assistant Professor (RTDA) in Statistics at the MEMOTEF Department of Sapienza University of Rome (IT). I also hold joint positions as Visiting Faculty Researcher at Google and as Visiting Researcher at the MRC - Biostatistics Unit, University of Cambridge (UK), where I was a PostDoc in 2020 — 2021.

I collaborate with various academic and nonacademic research institutions worldwide, some of which I visited during my PhD. Active collaborations include University of Cambridge, National University of Singapore and University of Toronto, where I am also part of the IAI Lab. In Italy, I am leading some work in collaboration with ISTAT (on accuracy estimation of multisource official statistics), NADO Italia (on doping detection), and FAO (on quantifying the impact of disasters on economic loss). I am in the editorial board of YoungStatS, the blog of Young Statisticians Europe (YSE), serve as as associate editor of Trials, and curate the Women in Statistics and Data Science Twitter account. I am also a member of IMS, ACM, ISCB, IBS, SIS and YoungSIS, among others.

I love stats and I embrace diverse, and perhaps too many to mention, therein areas. My main attention so far has been devoted to sequential decision-making problems, intersecting areas of statistical inference, Bayesian statistics, reinforcement learning (RL) & multi-armed bandits (MABs), and adaptive design of experiments, main focus of my PhD thesis. More recently, I became interested in copula models for representing data dependencies, and how these can be used for improving statistical problems such as estimating highest density regions or anomaly detection. Uncertainty quantification, especially conformal prediction, is a new door I am opening, with excitement, and shall constitute one of my future research lines.

My research is inspired by the numerous challenges arising in real-life applications and directed towards providing concrete benefits in these areas. I strongly believe that the theoretical and methodological progress should go along with the concrete real-world needs, and be not simply good, but also good for something.