Computational Statistics and Data Analytics Lab

The CSDA Lab develops statistical learning, trustworthy AI, and decision-support methodologies for applications in health, engineering, and society.

Our research integrates statistical modeling, uncertainty quantification, machine learning, and domain expertise to improve prediction, monitoring, and decision making in complex systems.

Research Programs

• Trustworthy AI and Statistical Learning: uncertainty quantification, conformal prediction, robust statistical methods, and interpretable AI.

• Monitoring and Quality Analytics: statistical process monitoring, multivariate control charts, IoT monitoring, image analytics, and health-system monitoring.

• Precision Health Analytics: sports medicine and orthopedics, cancer survivorship, flow cytometry, clinical and population health analytics, predictive modeling, and causal inference.

• AI-Driven Decision Support: educational AI, RAG systems, large language models, intelligent agents, and decision-support applications.

CSDA Lab students and collaborators during a research meeting.

Student Research and Training

Students in the CSDA Lab participate in collaborative research projects, publications, conference presentations, software development, and funded research experiences in statistics, data science, and artificial intelligence.

Research Collaborations and Funding

The CSDA Lab collaborates with academic, healthcare, government, and industry partners to develop data-driven solutions for scientific and operational challenges.

• 📈 Air Force Research Laboratory
• 🩺 NIH All of Us Research Program
• 📈 IHMC and ARL
• 🩺 Mayo Clinic
• 🩺 Andrews Research & Education Foundation

Research Guidelines

Please review the research guidelines.

Joining CSDA Lab

If you are interested in joining, please go to the recruitment page.