I study how neural circuits control motivational behaviour, combining genetic dissection of defined neuronal populations with quantitative, high-dimensional behavioural analysis. My current work focuses on how neuromodulatory circuits encode internal states — hunger, satiety, arousal — and regulate foraging decisions in Drosophila at the laboratory of Adam Claridge-Chang. I have previously studied serotonin regulation of intertemporal decision-making in mice at Susumu Tonegawa’s laboratory at MIT, building computational models of how internal state shapes choice behaviour under uncertainty.

I am also a contributor to DABEST, an open-source Python/R statistical framework for effect size estimation that has replaced ANOVA-based analysis in laboratories worldwide (2,000+ citations). DABEST 2.0, which I co-developed, introduces estimation methods for complex multi-group experimental designs and is currently in revision at Nature Methods.

My core expertise — modelling state-dependent decision-making, building reproducible statistical frameworks for high-volume experimentation, and extracting signal from large-scale behavioural datasets — translates directly to problems in user behaviour modelling, engagement prediction, and rigorous data-driven product experimentation. 

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