Abhi develops statistical and machine learning methods for large spatial datasets as well as Bayesian models for multi-source epidemiological datasets.

Aki uses Bayesian methods and statistical computing to tackle methodological challenges in healthcare analytics and large-scale biomedical applications.

Characterizing how neural organoids respond to electrical stimulation is essential for understanding their computational and adaptive properties. In this study, we develop a Bayesian hierarchical framework to analyze spike activity recorded from organoids using multi-electrode array (MEA) platforms under varying stimulation intensities. Spike counts are modeled as stochastic processes, allowing us to quantify stimulation effects while accounting for electrode-level variability, repeated trials, and parameter uncertainty.

Using Bayesian generalized linear modeling and posterior inference, we estimate stimulation-dependent changes in firing rates and obtain uncertainty quantification. The hierarchical structure enables partial pooling stimulation levels, improving statistical efficiency and robustness.

Our results show systematic modulation of spike activity with increasing stimulation intensity, alongside substantial heterogeneity across recording sites. This probabilistic framework provides a principled and flexible approach for analyzing organoid electrophysiology data and supports inference in novel biological neural systems.