CausalRx.AI Lab

The CausalRx.AI lab at University of Houston Department of Pharmaceutical Health Outcomes and Policy (PHOP) and Center for Population Health Outcomes and Pharmacoepidemiology Education and Research (P-HOPER Center) develops and applies AI–powered causal inference methods to evaluate drug effects. Focus areas include precision medicine, confounding control, and using multimodal data (genetics, imaging, real-world data) to assess drug effects.

I have openings for Postdocs and PhD students—feel free to contact me if you’d like to learn more about my research.

People

PI, Assistant Professor @UH · Adjunct Assistant Professor @UNC

Interests: Precision medicine, Confounding control, Multimodual data

Projects: Develops and applies AI/ML-powered causal inference methods.

Email Website Google Scholar

Biostatistic PhD student @UNC

Interests: bioinformatics, scRNA-seq data analysis.

Projects: scRNA-seq data-based drug repurposing.

Interests:

Projects:

Email Website

MS Student · Data Science / PHOP

Undergraduate / Visiting Student

Alumni & Collaborators

We are fortunate to collaborate with colleagues across UH, UNC, Baylor, MD Anderson, and beyond.

Alumni and key collaborators will be listed here soon. Stay tuned!

Causal inference & machine learning for assessing drug effects in real-world data (large claims, electronic health record, etc.).
Lead analyses, publish, and present at national/international meetings.
Contribute to grant preparation and collaborative projects.

PhD in Pharmaceutical Health Outcomes & Policy with a focus on real-world evidence and AI/ML-driven causal inference.
Opportunities to work with large datasets (Medicare, MarketScan, NACC, UK Biobank, etc.).
Collaborate with an interdisciplinary team across UH, UNC, Baylor, and Texas Medical Center partners.