Carlos Samey
Carlos is a data scientist at IDInsight where he plays a key role in designing, testing, and improving various algorithms for partners.
Prior to joining IDinsight, he worked as a machine learning engineer for Gozem a ride-hailing startup based in Togo, implementing, improving and deploying machine learning models. He has also previously worked as a data scientist for the Togolese Ministry of Digital Economy, building dashboards.
Carlos holds a masters in Data Science from the Middlesex University of London and a bachelor’s in Mathematics and Computer Science at the Catholic University of Africa, Lome.
He has also co-authored a research paper studying extended category learning with Spiking Nets and Spike Timing Dependent Plasticity. Carlos is fluent in French, English and Ewe.
Sessions
The Togolese Ministry of Health distributes contraceptives to women across the country every year. The Ministry employs two different types of interventions (open days at district health clinics, and mobile clinics that travel to remote regions), implemented multiple times in a year and in different districts, to achieve an annual target of the number of women reached (i.e. coverage). However, the two types of intervention have varying costs and varying efficacy in reaching women in different districts. This makes planning and budgeting for the interventions while also achieving a desired coverage extremely challenging for the Ministry. In this talk, we will explore how this problem may be tackled using linear programming to optimize how the two interventions are implemented across districts and at multiple timepoints annually. We analyze historical data and leverage optimization models to tailor implementation of the two interventions to ensure cost-effectiveness while meeting coverage targets. We also discuss different variants of the optimization model to introduce flexibility and customization for managing resources, the assumptions involved, and their utility in improving intervention planning. Attendees will gain insight into the technical complexities of implementing linear optimization models, the challenges involved in using them for decision-making, and its potential to help efficiently allocate scarce resources. Suitable for data professionals interested in implementing data-driven solutions to improve resource allocations under specific constraints. No prior knowledge required.