I’m a computational behavior scientist modeling complex behavior and physiology systems. I specialize in neuromodulation, longitudinal behavior and physiology tracking, learning mechanisms, estimation statistics, large-dimensional data analysis and visualization, reproducible analysis workflows, and I think about AI agent workflows and evaluation. My work asks how we infer hidden states from noisy, high-dimensional evidence. Hunger, satiety, impulsivity, attention, social influence, and model capability are not directly visible.
I am currently a Senior Research Fellow at the Adam Claridge-Chang Lab. Previously I was a PhD student at the Tonegawa Lab at MIT.
Hot Off the Press · Nature Methods 2026
See the effect, not just the P value.
Modern biology produces complex experiments: many groups, repeated measurements, factorial designs, binary outcomes. Much of it still gets reduced to one question — is it significant? DABEST 2.0 plots the raw data, the effect size, and the uncertainty around that effect together, so the analysis matches the question the experiment was designed to ask.
Neuroscience · Methods
When one metric lies, the whole behavioral profile tells the truth.
Visualization · Uncertainty
An uncertainty-aware heatmap cell that keeps the distribution visible.
AI Workflows · Evaluation
Designing an inspectable graph for scientific judgment, critique, and repair.
Quantified Self · Measurement
Health is multidimensional; recovery is not a single number.
