Name: Mary M Lucas
Type: User
Company: Drexel University
Bio: Data nerd, healthcare, Python, R, ML, wrangling all the things. Doctoral candidate at Drexel University College of Computing & Informatics.
Twitter: mary_m_lucas
Location: Philadelphia, PA
Blog: https://marylucas.com/
Mary M Lucas's Projects
Tutorials for the 2019 Aarhus Datathon
to host the animation
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.
A curated list of awesome Fairness in AI resources
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.
Official repository for Citation Style Language (CSL) citation styles.
Final Project INFO623 Spring 2021/2022
Exploring different aspects of the COVID-19 pandemic
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.
Repository containing files for DSCI 521 Project @ Drexel University
Exploring data on deaths due to drug overdoses in Connecticut
Code from live exploratory analyses of data in R
Jupyter notebooks for preprocessing DICOM medical images across modalities (CT, MRI, X-ray). Streamlines workflows for researchers and clinicians working with medical imaging data.
Efficient Large Language Models: A Survey
Official repo for "Characterizing Stigmatizing Language in Medical Records" (ACL 2023)
Data descriptor and sample notebooks for the Emory Breast Imaging Dataset (EMBED) hosted on the AWS Open Data Program
A Python package to assess and improve fairness of machine learning models.
Healthcare-specific tools for bias analysis
Equitable Allocation of Healthcare Resources with Fair Survival Models
Flower - A Friendly Federated Learning Framework
Implementation and experiments of graph embedding algorithms.
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.
3rd ed.