Research
Research Philosophy
My work is grounded in pragmatic innovation. I focus on advancing methodological research in applied artificial intelligence, with an emphasis on model evaluation, fairness, and uncertainty quantification. My goal is to bridge cutting-edge AI methods with real-world clinical impact by developing frameworks that assess when AI systems are trustworthy enough to inform patient care, and identify where they fall short for specific populations and individual patients. This work is ultimately in service of ensuring that the benefits of clinical AI reach all patients and communities equitably.
Current Work
PhD Candidate, Information Science | Drexel University | Expected 2026
- Advisor: Christopher C. Yang, PhD
- Dissertation: A Framework for Fair and Robust Clinical Risk Prediction through Collaborative Learning and Localized Uncertainty Quantification
Funding & Fellowships
NIH AIM-AHEAD Research Fellowship (2022)
Supporting research in health equity, algorithmic fairness, and trustworthy AI.Edith Peterson Mitchell, MD Health Equity Travel Scholarship (2023, 2024)
ECOG-ACRIN Group Meetings.
Core Focus Areas
Health AI & Predictive Modeling
Developing robust predictive models for clinical decision support using multi-modal health data, with emphasis on uncertainty quantification for safe and reliable deployment.Algorithmic Fairness & Bias
Designing novel frameworks to detect, quantify, and mitigate bias in high-stakes clinical AI systems.Health Disparities
Using large-scale EHR and registry data to quantify systemic inequities and evaluate real-world care pathways.Clinical LLMs
Evaluating and improving the safety, reliability, and reasoning performance of Large Language Models in healthcare.
Selected Projects
Reliable and Robust Clinical AI
Algorithmic Fairness & Uncertainty Quantification
- Designed fairness auditing frameworks for clinical risk prediction models across chronic kidney disease, substance use disorder, and oncology datasets, integrating novel mitigation techniques and benchmarking against existing approaches (JCO CCI, AIME 2025, IEEE ICHI 2024, IEEE ICHI 2023).
- Leveraged over 10 years of clinical nursing experience to guide feature engineering and problem formulation, distinguishing true physiological signal from artifacts of clinical workflow.
- Quantified disparities in readmission prediction and treatment completion, demonstrating how commonly used fairness metrics fail to capture systemic healthcare inequities.
- Developed a Neighborhood-Adaptive Difficulty Score combining k-nearest neighbor topology with conformal prediction to distinguish model limitations from inherent case complexity.
(Manuscript in preparation)
Real-World Evidence & Health Disparities Modeling
- Developed a mixed-order Markov chain simulation using VA Corporate Data Warehouse data to model prostate cancer treatment sequences and identify empirically observed pathways associated with elevated mortality risk across racial groups.
(Manuscripts in preparation) - Conducted a national registry study using the AAO IRIS® Registry to quantify racial and gender disparities in retinal vein occlusion treatment via multivariable logistic regression, highlighting inequities in access to anti-VEGF therapy (Ophthalmology Retina).
Clinical NLP & Large Language Model Evaluation
- Developed EnsReas, an iterative prompting framework that improved medical question-answering accuracy and consistency on USMLE datasets (+3-5%) across both closed (GPT-4) and open-source clinical LLMs (JAMIA).
- Applied open-source clinical LLMs to extract pathologic TNM cancer stage from real-world pathology reports without labeled training data, demonstrating that prompting and ensemble-based reasoning can achieve competitive performance and improved consistency relative to fine-tuned BERT baselines (AIME 2024, IEEE ICHI 2024).
- Designed and evaluated an LLM-assisted pipeline to automate extraction of population demographics from biomedical literature, enabling scalable equity analysis (International Journal of Digital Curation).