Model-free feature selection to facilitate automatic discovery of divergent subgroups in tabular data
- Big Data 2022
Celia Cintas is a Research Scientist at IBM Research Africa - Nairobi, Kenya. She is a member of the AI Science team at the Kenya Lab. Her current research focuses on the improvement of ML techniques to address challenges on Global Health in developing countries and exploring subset scanning for anomaly detection under generative models.
Previously, a grantee from the National Scientific and Technical Research Council (CONICET) working on Deep Learning techniques at LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for Analysis of the Diversity and Evolution of Latin America (CANDELA). During her Ph.D., she was a visiting student at the University College of London (UK). She was also a Postdoc researcher visitor at Jaén University (Spain), applying ML to Heritage and Archeological studies.
She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). Co-chair of several Scipy Latinamerica conferences and happy member of LinuxChix Argentina. Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019) and Diversity Co-Chair for SciPy (2020-2022).