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  • My work and research interests lie at the intersection of healthcare and data science, with a focus on predictive modelling, AI bias and fairness, and health disparities research.
  • Iā€™m currently pursuing a doctoral degree in Information Science at the Drexel University College of Computing and Informatics, and I'm also affiliated with the Health Informatics Lab at Boston University Metropolitan College.
  • You can connect with me on Twitter or via LinkedIn

Mary M Lucas's Projects

aif360 icon aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

balance icon balance

The balance python package offers a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.

citation_styles icon citation_styles

Official repository for Citation Style Language (CSL) citation styles.

covid-19 icon covid-19

Exploring different aspects of the COVID-19 pandemic

covid_inequities_project icon covid_inequities_project

This repository contains data on COVID-19 and COVID-19 Inequities in BCHC cities. Data are preliminary and subject to change. Information on this page will change as data and documentation are updated.

ehr-stigma icon ehr-stigma

Official repo for "Characterizing Stigmatizing Language in Medical Records" (ACL 2023)

fairlearn icon fairlearn

A Python package to assess and improve fairness of machine learning models.

fairsurv icon fairsurv

Equitable Allocation of Healthcare Resources with Fair Survival Models

flower icon flower

Flower - A Friendly Federated Learning Framework

handson-ml2 icon handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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