Mariano leads algorithmic development for the Brain Health accelerator, building machine learning methods that map the trajectory of neurodegenerative diseases. His team integrates clinical, neuropathological, and cellular and molecular data to reveal how genes become dysregulated in the earliest stages of disease and to identify how biomarkers shift alongside those first changes.

Mariano joined the Allen Institute as an assistant investigator in 2021. He has co-led the data analysis efforts of the Seattle Alzheimer’s Disease Atlas (SEA-AD) consortium, aiming to characterize the neuropathological, molecular, and spatial changes taking place in Alzheimer’s. Previously, Mariano held a research scientist position at UC Berkeley, where he helped develop machine learning algorithms to integrate diverse types of genomic data and Bayesian statistical models to interpret super-resolution microscopy data, work for which he and his collaborators received the Michell Prize in applied statistics.

Before this, Mariano was a joint postdoctoral fellow at the Simons Foundation and Broad Institute, where he helped investigate how cortical interneurons acquire their identity by developing statistical tools to study gene regulation and which parts of the genome are active. Mariano completed his PhD in Neuroscience at Columbia University, where he helped create computational tools to decipher the coding mechanisms of cortical taste sensation Bayesian models to catalog spinal cord interneurons.