Biography

I am a data science, and machine learning researcher. My work focuses on analyzing large-scale clinical datasets (e.g. OASIS, N3C, AIM-AHEAD) to better understand chronic disease management, readmission risks, and healthcare disparities. I have studied how biological markers, geographical distribution, and environmental factors contribute to chronic conditions, applying advanced modeling techniques to identify risk factors and affected populations. With my expertise in predictive analytics, data processing, and clinical feature engineering, I have developed and evaluated models ranging from baseline classifiers to advanced neural architectures, incorporating interpretability tools such as SHAP values to ensure fairness and transparency. I have also explored data sampling methods, geospatial features, and feature selection strategies to optimize clinical prediction tasks. My long-term goal is to bridge the gap between machine learning innovations and real-world healthcare applications, supporting clinicians and policymakers in delivering more equitable, data-driven care.

Academic Status

PhD Student - 4th

Research Area/Department

Computer Science; Data Science; Machine Learning/AI

Major/Specialty

Doctor of Philosophy (Ph.D.) Major in Computer Science (Software Systems Concentration Entering with Bachelor's Degree)

Degrees Earned or in Progress

Bachelors Of Arts in Computer Science with a Minor in Mathematics

Academic Preparation

I’ve built my preparation for a summer internship through both classes and hands-on experiences. In school, I’ve taken courses like Introduction to Data Science, Machine Learning, Database Systems, and Biostatistics, which gave me a strong foundation in programming, statistics, and working with real-world data. I also took Health Informatics, which showed me how data connects to patient care and healthcare decision-making. Outside the classroom, I worked as a Data Science Intern at OpenLending (Sept-Dec 22-23), where I learned how to approach problems from a business perspective. More recently, I was part of the NCATS AIM-AHEAD Cohort 2 Program (Jan–Sept 2024), where I collaborated with a multidisciplinary team to analyze National COVID Cohort Collaborative (N3C) data. We focused on understanding Long COVID and respiratory complications, and I helped engineer features, run analyses, and uncover disparities. These experiences taught me not only technical skills but also how to work on a team and connect data science to real-world impact.

Research/Publications

My work has been published in peer-reviewed venues, including a study on predicting Long COVID using National COVID Cohort Collaborative (N3C) data presented at the IEEE International Conference on Healthcare Informatics. I have also worked on projects analyzing CMS OASIS data to predict diabetic readmission and presented this work at the 2024 IEEE International Conference on Big Data. A full list of my publications and citations is available on my google scholar: https://scholar.google.com/citations?user=aI-1yXEAAAAJ&hl=en

Research/Academic Interests

My research and academic interests focus on the intersection of data science, machine learning, and healthcare, particularly in understanding and improving outcomes for patients with chronic diseases such as diabetes and heart failure. I am interested in using large-scale clinical datasets to identify risk factors, uncover health disparities, and develop predictive models. I am also passionate about unbiased and fair AI, ensuring that predictive insights are actionable and equitable.

Computational and Data Science Areas

Computer Science

Motivation

I want to participate in the Sustainable Research Pathways program because it aligns perfectly with my passion for applying data science and AI to real-world problems while engaging with a diverse community. I am excited by the opportunity to work on NSF NAIRR projects and contributing my own experience in healthcare analytics and machine learning. I hope to explore new approaches to creating fair and interpretable models. Beyond the technical experience, I value the program’s emphasis on mentorship and community building. Ultimately, I hope this program will strengthen my ability to conduct impactful, inclusive research.

Lightning Talk Title

AI & Data Science for Health and Learning

Keywords (Maximum 20 words)

machine learning, AI, data science, healthcare analytics, prediction models, graph clustering, topic modeling, chronic conditions, clinical data, big data