Publications and talks

Research awards

Microsoft Research Dissertation Grant, 2018.

1st Place Innovation Award on Data Science at Data for Policy, 2016.

Best Student Presentation Award at Domestic Nuclear Detection Office (DNDO) Academic Research Initiative Grantees Conference, 2015.

Papers

M. De-Arteaga, A. Romanov, H. Wallach, J. Chayes, C. Borgs, A. Chouldechova, S. Geyik, K. Kenthapadi, A. Kalai, “Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting”, ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*), 2019. [To appear]

N. Swinger*, M. De-Arteaga*, N. Heffernan IV, M. Leiserson, A. Kalai, “What are the biases in my word embedding?”, Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES), 2019. [To appear] (*Indicates equal contribution)

M. De-Arteaga, P. Huggins, J. Elmer, G. Clermont, A. Dubrawski, “Predicting Neurological Recovery with Canonical Autocorrelation Embeddings”, In submission.

M. De-Arteaga, A. Dubrawski, A. Chouldechova, “Learning under selective labels in the presence of expert consistency”, Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2018. [arXiv]

M. De-Arteaga, W. Herlands, D. Neill, A. Dubrawski, “Machine Learning for the Developing World”, ACM Transactions on Management Information Systems, 2018. [ACM]

M. De-Arteaga, A. Dubrawski, “Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador”, Data for Policy, 2016. [Zenodo]

M. De­-Arteaga, A. Dubrawski, P. Huggins, “Canonical Autocorrelation Analysis for Radiation Threat Detection”.  Heinz First Paper, Heinz College / Data Analysis Project, Machine Learning Department, 2016. Carnegie Mellon University. [CMU]

W. Herlands, M. De-­Arteaga, D. Neill, and A. Dubrawski, “Lass0: Sparse Non­Convex Regression by Local Search”, Neural Information Processing Systems (NIPS), Optimization for Machine Learning Workshop, 2015. [arXiv]

M. De­-Arteaga, I. Eggel, C. Kahn and H. Müller, “Analyzing Image Search Behaviour of Radiologists: Semantics and Prediction of Query Results” Journal of Digital Imaging, 2015. [Springer]

M. De­-Arteaga, I. Eggel, B. Do, D. Rubin, C. Kahn Jr. and H. Müller, “Comparing Image Search Behaviour in the ARRS GoldMiner Search Engine and a Clinical PACS/RIS” Journal of Biomedical Informatics, 2015. [Elsevier]

A. Riveros, M. De-­Arteaga, F. Gonzalez, S. Jimenez and H. Müller, “MindLab­UNAL: Comparing Metamap and T­-mapper for Medical Concept Extraction in SemEval 2014 Task 7”, SemEval, 2014. [ACL]

M. De-­Arteaga, S. Jimenez, J. Baquero, G. Dueñas and S. Mancera, “Author Profiling Using Corpus Statistics, Lexicons and Stylistic Features” 10th PAN Evaluation Lab on Uncovering Plagiarism, Authorship. and Social Misuse, CLEF, 2013. [UniWeimar]

M. De­-Arteaga, “Vector models in text mining and its applications to author profiling tasks” (in Spanish), Undergraduate thesis, 2013. [PDF]

Talks

Guiding public service agencies with machine learning: Opportunities and risks, AMS Sectional Meeting, Special Session on Social Change In and Through Mathematics and Education, 2018.

Fairness In Prediction Models Used By Public Service Agencies, INFORMS, 2018.

“Challenges of data-driven decision making with humans in the loop”, Google Fairness in ML Workshop, 2018.

“Machine learning, sexual violence crimes and decision-support systems”, Office of the Attorney General of Colombia, 2018.

“Learning under selective labels in the presence of expert consistency”, Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2018.

“Machine Learning for the Developing World”, Data science and machine learning for development and humanitarian action session, UNESCO Tech4Dev Conference, 2018. Closing Keynote.

“Machine Learning for the Developing World”, École polytechnique fédérale de Lausanne (EPFL), 2018.

“Using expert consensus to learn under selective labels”, Google Women in Tech Summit, 2018.

“Challenges of Child Maltreatment Prediction Models”, Allegheny County Department of Human Services, 2017.

“Leveraging Multidimensional Autocorrelations to Boost Sensitivity of Spectral Anomaly Detection”, DNDO Annual Academic Research Initiative Grantees Conference, 2015. Best Student Presentation Award.

“Author Profiling, an Application of Computational Linguistics”, Colombian Congress of Young Linguists, 2013.

Posters

Maria De­-Arteaga, Peter Huggins, Jonathan Elmer, Gilles Clermont, Artur Dubrawski, “Canonical Autocorrelation Embeddings for Comatose Patient Characterization”, Neural Information Processing Systems (NIPS), Women in Machine Learning Workshop, 2017.

Maria De-Arteaga, Artur Dubrawski, “Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador”, Data for Policy, 2016. 1st Place Innovation Award on Data Science.

Maria De­-Arteaga, Artur Dubrawski, Peter Huggins, “Canonical Autocorrelation Analysis for Radiation Threat Detection”, CRA­-W Grad Cohort Workshop, 2016.

William Herlands, Maria De-­Arteaga, Daniel Neill, and Artur Dubrawski, “Lass0: Sparse Non-­Convex Regression by Local Search”, Neural Information Processing Systems (NIPS), Optimization for Machine Learning Workshop, 2015.

Maria De­-Arteaga, Artur Dubrawski, Peter Huggins, “Canonical Autocorrelation Analysis for Radiation Threat Detection”, Neural Information Processing Systems (NIPS), Women in Machine Learning Workshop, 2015.

Maria De-­Arteaga, Sergio Jimenez, Julia Baquero, George Dueñas and Sergio Mancera, “Author Profiling Using Corpus Statistics, Lexicons and Stylistic Features” 10th PAN Evaluation Lab on Uncovering Plagiarism, Authorship. and Social Misuse, CLEF, 2013.