The mission of the UCSF Computational Coma Neuroscience Lab is to accelerate innovations in brain monitoring analytics that will guide acute brain injury treatments. We are building a multimodal data platform that will help decode why and how some patients recover from coma after acute traumatic brain injury, cardiac arrest, stroke, or epilepsy. Our laboratory is located at the San Francisco General Hospital (SFGH), a teaching hospital serving the most vulnerable and underserved populations of our city. Leveraging a dataset of more than 1,000 subjects and 5TB of continuous EEG data (i.e. brain waves), we are developing an algorithm that will map and predict the evolution of neurophysiology patterns for patients recovering from acute coma. We will specifically combine functional connectivity analysis of non-invasive EEG data to machine learning and deep learning methods that can capture the evolution of EEG time-series data. We hypothesize that specific regions of the brain are involved in consciousness recovery and that we can predict when patients will wake up from a coma days in advance. Our overarching goal is to help families and clinicians understand which patients are most likely to benefit from treatments and how to individualize their care and brain health.