Nina Deliu | Nina :D

Latest

Artificial Intelligence-based Decision Support Systems for Precision and Digital Health
Incorporating Participants' Welfare into Sequential Multiple Assignment Randomized Trials
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Modeling considerations when optimizing adaptive experiments under the reinforcement learning framework (Invited Talk @ ICSDS2023)
Multinomial Thompson sampling for rating scales and prior considerations for calibrating uncertainty
Informing Users about Data Imputation. Exploring the Design Space for Dealing With Non-Responses
Reinforcement learning for sequential decision making in population research
On the finite-sample and asymptotic validity of an allocation-probability test for adaptively-collected data (Invited Talk @ StaTalk2023)
Enhancing patient outcomes and statistical efficiency in rare-disease phase-II trials. The StratosPHere 2 study (Invited Talk @ Italian Bayesian Day 2023)
Enhancing patient outcomes and statistical efficiency in rare-disease phase-II trials. The StratosPHere 2 study (Poster @ ISCB2023 - Joint conference with the IBS)
Efficient Inference Without Trading-off Regret in Bandits. An Allocation Probability Test for Thompson Sampling (Invited Talk @ JSM2023)
Probabilistic and Distance-based Approaches for Computing Highest-Density Regions (Talk @ EcoSta2023)
Computing Highest Density Regions with Copulae (Poster @ 13 GdR MEMOTEF)
Computing Highest Density Regions with Copulae (Talk @ SIS 2023)
Assessing the predictive accuracy of official-statistics registers. The Global Mean Squared Error measure (Invited Talk @ ITACOSM2023)
Multiculturality and Interculturality: Qualitative Analysis of the Perspective of Focus Group Participants
Development of a multivariate Bayesian methodological framework for doping detection using copulae (Talk @ University of Cambridge)
Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health
Dynamic Treatment Regimes for Optimizing Healthcare
Multi-disciplinary fairness considerations in machine learning for clinical trials