INÊS GOMES

Data Scientist at DefinedCrowd

In this broad role, her contribution spans from exploring data, effectively communicating findings and creating visualizations, to building models, formulas, and other algorithms that help DefinedCrowd's platform become smarter and more effective. Since joining DefinedCrowd she has contributed to topics such as crowdsourcing strategies for improving the quality of training data, examining the human effort required by the crowd to complete tasks, investigating the cognitive load of tasks, and more.

Inês’ most relevant work includes her Master thesis, "Throughput Forecasting for Crowdsourced Text-Enrichment," and an accepted paper to LREC 2020, "Effort Estimation in Named Entity Tagging Tasks"

Topic:The data science secrets behind crowdsourcing success.