Research

Research in Progress

• Adaptive Design of Clustered Experiments, 2026+ . Cluster designs depend on the correlation of observations within clusters. I propose two algorithms that implement a near-optimal design without knowing this correlation and do not affect inference.
• Signaled Bandits, 2026+ . I present the signaled bandit game, a generalization of bandit and experts' problems through the notion of signal informativeness.
• Sequential Bias Identification, 2026+ . I propose a framework for adaptive experimentation in the presence of observational estimates with unknown biases.
• Joint and Independent Sampling in Clustered Survey Designs, 2026+ . I characterize optimal online experiments with clustered data when the analyst is trying to identify the best group and can observe the entire cluster at a premium.
• Escaping Adverse Selection through Learning, 2026+ (joint with Hubert Wu) . In an Akerlof-style model, we show agents can escape market unraveling when they are constrained to learn through prices.
• Sequencing and Information Provision in Search Games, 2026+ . I characterize a search recommendation model where the designer can only learn from the search behavior of the agent given the recommendation.

Additional Notes

• Likelihood Ratios and Large Deviations: A Not Surprising Connection, 2024.

Pre-PhD Research

Please bear in mind that the following papers have not been peer reviewed. Moreover, my training was faulting when I wrote them (it still is), so I hope that you are not too harsh on me. ;)

• Adaptive Wage Setting: A Prior-free Theory of Adverse Selection and Monopsony Markets, 2023.
• An Econometric Insight into Health Insurance Demand: Aporophobia and Racism as Drivers of Health Insurance Demand in the UK (1991–2008), 2021.
• A Narratological Approach to Chinese Politics: Developing a Narrative-Based Theoretical Framework in South East China Sea and Hong Kong Mainland Conflicts (1974–2020), 2021.