Africa is the founder and CEO of benshi.ai, a non-profit funded by the Bill & Melinda Gates Foundation, that focuses on reducing health inequalities through behavioral machine learning in low-income countries. Prior to that, África was the chief analytics officer at Inditex (world’s largest fashion retailer, Zara owner). She led the research, application, and strategic development of data science across the organization. Previously, África founded Yokozuna Data, an AI company based in Tokyo that built a machine learning platform to predict the individual behavior of video game players. Africa led Yokozuna Data’s acquisition.
África holds a PhD in Mathematics from the University of Reading, and MSc’s in String Theory and Theoretical Physics from CERN and the Autonomous University of Madrid. África has been Marie Curie EU research fellow at CERN and scientist at RIKEN, Japan and the German Weather Service (focus on satellite data). She is co-author of multiple peer-reviewed articles.”
May 28, 2021 10:30 AM PT
The rapid expansion of mobile phone usage in low-income and middle-income countries has created unprecedented opportunities for applying AI to improve individual and population health.
In benshi.ai, a non-profit funded by the Bill and Melinda Gates Foundation, the goal is to transform health outcomes in resource-poor countries through advanced AI applications. We aim to do so by providing personalized predictions and recommendations to support diagnosis to medical care teams and frontline workers, as well as to nudge patients through personalized incentives towards an improvement in disease treatment management and general wellness.
To this end, we have built an operational machine learning platform that provides personalized content and interventions real-time. Multiple engineering and machine learning decisions have been made to overcome different challenges and to build an experimentation engine and a centralized data and model management system for global health. Databricks served as a cornerstone upon which all our data/ML services were built. In particular, MLflow and dbx (an opensource tool from Databricks) have been crucial for the training, tracking and management of our end-to-end model pipelines. From the data science perspective, our challenges involved causal inference analysis, behavioral time series forecasting, micro-randomized trials, and contextual bandits-based experimentation at the individual level.
This talk will focus on how we overcome the technical challenges to build a state-of-the-art machine learning platform that serves to improve global health outcomes.