My research primarily focuses on leveraging big data from electronic health records (EHRs) and employing advanced health informatics methodologies, including artificial intelligence (AI) and large language models (LLMs). I am involved in projects aimed at efficiently predicting clinical outcomes, such as mortality risks, Alzheimer's disease, and substance abuse disorders, by analyzing complex medical histories. Recently, I have expanded my work into the integration of AI tools into educational practices, specifically investigating the efficacy and limitations of AI-generated content like ChatGPT in health informatics instruction.

My work addresses crucial healthcare workforce issues, analyzing job demand and supply to enhance curricula and inform workforce development strategies.

With student collaborators, I also focus on young athletes, exploring predictors and impacts of sports-related injuries on their overall health and educational outcomes.

In collaboration with the Side-Out Foundation and the College of Science, I developed a metastatic breast cancer (MBC) portal created to capture a large array of information from the Side-Out sponsored studies. Register for access to data.

I worked on Virginia Screening, Brief Intervention, and Referral to Treatment (SBIRT) project as a data scientist.

I enjoy teaching at Mason. Learning is stigmatized by the premise that learning is difficult, when in fact it is a very beautiful and vital process of life, full of excitement and pleasure. My approach is to give a problem and to encourage students to find the way to the solution. My goal is not just to explain how something works, but also why it is like that, what is behind it, and where the solution will lead them.

I serve as a research service without compensation (WOC) employee for the Department of Veterans Affairs.

In my free time, I enjoy making chocolates (not to eat them, just for the pleasure of baking) and ice skating with my family.

I am a 2023 university wide Teaching Excellence Award Winner.