Maria De Arteaga
Maria De Arteaga is a Ph.D. student at Carnegie Mellon University.
Maria is a joint PhD candidate in Machine Learning and Public Policy at Carnegie Mellon University’s Machine Learning Department and the Heinz College of Information Systems and Public Policy.
Machine learning (ML) is increasingly being used to support decision-making in critical settings, where predictions have potentially grave implications over human lives. Examples include healthcare, hiring, child welfare, and criminal justice. Maria's research focuses on the risks and opportunities of ML-based predictions to support decision-making in the context of sustainable societies. As part of her work on algorithmic fairness and accountability, she characterizes how societal biases encoded in historical data may be reproduced and amplified by ML models, and develops algorithms to mitigate these risks. Moreover, even if data does not encode harmful societal biases, many challenges still prevent the effective use of predictions to improve decision-making, such as omitted payoff bias and the selective labels problem. In her research, Maria seeks to understand the limits and risks of using machine learning in these contexts, and to develop human-centered ML that can improve expert decision-making.
She holds a M.Sc. in Machine Learning from Carnegie Mellon University (2017) and a B.Sc. in Mathematics from Universidad Nacional de Colombia (2013). She was an intern at Microsoft Research, Redmond, in 2017 and at Microsoft Research, New England, in 2018. Prior to graduate school, she worked as a data science researcher and as an investigative journalist. Her work has been awarded the Best Thematic Paper Award at NAACL’19, the Innovation Award on Data Science at Data for Policy’16, and has been featured by UN Women and Global Pulse in their report Gender Equality and Big Data: Making Gender Data Visible. She is a co-founder of the NeurIPS Machine Learning for the Developing World (ML4D) Workshop, and a recipient of a 2018 Microsoft Research Dissertation Grant.