I am an Assistant Professor (RTDA) in Statistics at the MEMOTEF Dept of Sapienza University of Rome (Italy), and a long-term visitor of the MRC–Biostatistics Unit of University of Cambridge (UK). Prior to joining the MEMOTEF Dept, I worked as a PostDoc Researcher at the Biostatistics Unit of University of Cambridge since 2020.
I completed my PhD in Methodological Statistics at Sapienza University, Dept of Statistical Sciences. During my PhD, I spent some time abroad visiting first the National University of Singapore (under the supervision of Bibhas Chakraborty), and University of Toronto (supervised by Joseph J. Williams). My PhD Thesis (supervised by Pierpaolo Brutti, and co-supervised by B Chakraborty and JJ Williams) was on the benefits and challenges of statistical reinforcement learning for modern healthcare applications, and included a major m-Health study. Here is a copy of my dissertation.
My primary research interest lies in exploring and developing statistical methods and theory in the context of sequential decision-making problems, intersecting areas of Bayesian statistics, reinforcement learning & design of experiments.
Currently, I focus on the use of multi-armed bandit strategies for conducting adaptive experiments and designing innovative trials, such as adaptive clinical trials and micro-randomized trials, the current gold standard in mobile health (m-Health).
As adaptive experiments such as m-Health apps or adaptive clinical trials usually have a dual goal of improving outcomes for participants enrolled into the experiment and learning about the effectiveness of interventions, inference in adaptively collected data play a central role in my research arena. I am currently exploring alternative hypothesis testing procedures for allowing reliable inference in adaptively collected data.
I also have an interest in the broad area of Bayesian statistics, especially in the context of trials and experiments.
My current research topics include, but are not limited to:
- Inferential approaches for adaptively collected data
- Sequential decision-making processes
- Design and analysis of adaptive experiments
- Multi-armed bandits and reinforcement learning
- Adaptive interventions
- Mobile and digital health
- Fairness in machine learning
- Multivariate statistics
My research is inspired by the numerous challenges arising in real-life applications (healthcare, education, or other behavioral settings) that are characterized by a sequential nature. 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”.
I actively collaborate with interdisciplinary research groups, from both academic institutions (University of Cambridge, University of Toronto, National University of Singapore), and other industry and research institutions (Mental Health America, Fondazione Brodolini, INAPP, ISTAT, NADO). Our collaborative projects have diverse goals, including statistical, machine learning, and applied studies.
I am also open to embracing alternative research areas and applications that are of interest to my current MEMOTEF Department.