You are excited to play a leading role in using data to identify causal relationships using irregular time series data. You are experienced in all aspects of designing, implementing and scaling the architecture to process large amounts of data.
Primary Objective: Quantify the effectiveness of treatments for specific individuals, reveal hidden factors exacerbating their illness, and determine personalized optimal daily values for these factors.
Approach: You will improve our time-series data mining algorithms to quantify correlations between every combination of variables for a given subject. We will also design algorithms capable of determining the minimum quantities of nutrient intake, sleep, exercise, medications, and other factors necessary to minimize symptom severity.
Impact: You will mitigate the incidence of chronic illness by informing the user of symptom triggers, such as dietary sensitivities, to be avoided. This will also assist patients and clinicians in assessing the effectiveness of treatments despite the hundreds of uncontrollable variables in any prescriptive experiment.