Optimized our geographically scalable resource allocation model to decrease disparity in disadvantaged and advantaged groups access to limited resources. This optimization allowed us to simulate increases in fair distribution of COVID–19 Vaccines across regions, increasing accessibility by proper allocation – prioritizing members of vulnerable populations. Current work is published with ACM EAAMO’22.

Insights from our work were also presented to the Governor of Ohio. This resulted in lowering the age minimum for vaccination allowances in the state of Ohio, during age–restricted vaccine eligibility. Thanks in part to our work, more vulnerable people, particularly racial minorities who reside in Ohio, became eligible for vaccines sooner.

I conducted this research with Dr. Mesrob I. Ohannessian of Univerisity of Illinois Chicago and Dr. Tanya Berger-Wolf of Ohio State University.

Strategically applied fairness metrics (demographic parity, differential intersectional fairness, and subgroup fairness) to real–world medical data to reveal disparate levels of testimonial injustice across intersecting demographic features, such as race and gender, not otherwise detected when examining these features individually. Demonstrated that an intersectional lens uncovers deeper biases in clinical records, contributing to more inclusive, equity–focused healthcare practices. Published at ACL ClinicalNLP’23 Workshop, findings indicate that intersectional analysis offers a clearer view of healthcare inequities in clinical records.

I conducted this research with Dr. Lu Cheng of the University of Illinois Chicago.​​​

Formulated the first causal graph for analyzing testimonial injustice in healthcare, using natural language processing (NLP) to quantify and causal discovery to reveal interactions among intersecting demographic attributes. Demonstrated how demographic features can intersect to contribute to or reveal testimonial injustice, underscoring the importance of analyzing intersectionality in patient care.

I conducted this research with Dr. Mesrob Ohannessian and Dr. Elena Zheleva of the University of Illinois Chicago. This work is currently under review.

Developed a User–Interface (UI) and framework which can be used by journalists and editors in news media to detect biased text in media submitted. This framework will be using a fine–tuned BERT model to detect injustice and epistemological bias, offer explainability to connect the detected words with testimonial injustice, and provide resources for users to learn more about why the words they used were tagged. Developed Using: Python, Flask, and Vue.js. Published at AAAI Document Understanding and Intelligence 2025 Workshop.

I conducted this research project with Dr. Lamogha Chiazor as an intern with the Tech4Justice Team within IBM Research. 

Integrated a framework to automatically assess skin tone representation in documents, wrapped in a Flask App. This framework was integrated with a document ingestion tool and several machine learning tools. The end result of the pipeline allows users to see the amount of skin–tone diversity in their submitted materials. Working to expose the tool for external use. Developed using: Python, Flask, HTML, and OpenShift. Current work is published with AIMIA 2021.

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.