My main research focuses on developing a machine learning model that will increase the accuracy and fair distribution for COVID-19 Vaccine accessibility amongst vulnerable populations. I am conducting this research with Dr. Mesrob I. Ohannessian of Univerisity of Illinois Chicago and Dr. Tanya Berger-Wolf of Ohio State University.

This research project allowed me to conduct an empirical study that proves there are differences in treatment towards individuals that can only be seen when their membership to multiple subgroups is observed. I conducted this research project with Dr. Lu Cheng and Bhuvani Shah of the University of Illinois Chicago.

This research project allowed me to conduct a study that shows the degree to which different demographic features (i.e., age, race, gender) contribute to persons experiencing testimonial injustice. It also shows the pertinence of considering intersectionality when looking for injustices or fairness - particularly in medical settings. I conducted this as an independent research study. It is currently under review.

This research project allowed me to develop a framework that leveraged multiple machine learning models to identify epistemological bias in News Media and offer users explainability for the detected bias. We also conducted a user study to verify the claims of our work. I conducted this research project with Dr. Lamogha Chiazor as an intern with the Tech4Justice Team within IBM Research. This work is currently under review.

This research project allowed me to integrate a framework to automatically assess skin tone representation in documents, wrapped in a Flask App. This framework was integrated with an IBM document ingestion tool and several machine learning tools. The ending result of the pipeline allows users to see the amount of skin-tone diversity in their submitted materials. We used dermatology as a use case to develop the tool. I conducted this research project with Dr. Girmaw Tadasse Abebe and Dr. Celia Cintas of IBM as an intern with the Tech4Justice Team within IBM Research.

My research focused on the low powered implementation of neural networks on an FPGA, in hopes of implementing them on Internet of Things (IoT) devices. Through this experience, I have been able to explore my interest in understanding how AI agents learn through Neural Networks in Machine Learning and am currently looking at the implementation of Neural Networks using TensorFlow. I conducted this project with Dr. Christopher Harris of Auburn University.

My research allowed me to analyze research on data trends relevant to public political knowledge. My focus for the semester was in discovering data models that will best visualize such data to help people understand what is influencing their political decisions. This opportunity also afforded me the opportunity to work on a literature review. Through this experience I was able to understand various methods of data collection and how the data you are collecting affects the models that explain the data. I conducted this project with Dr. Jakita O. Thomas of Auburn University.