Nina Deliu

Continuous Stats Learner, Piecewise Teacher.

MEMOTEF, Sapienza University

MRC-BSU, University of Cambridge

I am an Assistant Professor (RTDA) in Statistics at the MEMOTEF Department of Sapienza University of Rome (IT), and a 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) and NADO Italia (on doping detection). I am in the editorial board of YoungStatS, the blog of Young Statisticians Europe (YSE), 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, subject areas. My primary attention is 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. Conformal prediction is a new door I am opening.

My research is inspired by the numerous challenges arising in real-life applications, particularly in biostats, 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.


  • PhD in Methodological Statistics, 2021

    Sapienza University of Rome

  • MSc in Bayesian Statistics and Decision Sciences (double degree), 2017

    Universitè Paris Dauphine & University of Rome La Sapienza


  • STATS sequential decision-making, Bayesian inference, copula models, conformal prediction
  • ML reinforcement learning, multi-armed bandits
  • APP biostats, design of experiments, digital education