Silas Kati

Silas Kati is a Research Scientist at the University of Minnesota’s Institute for Health Informatics. His research focuses on developing and applying advanced machine learning and causal inference methodologies to address complex problems in health data analysis.

He is particularly interested in leveraging data-driven models to uncover causal mechanisms in biomedical and clinical datasets, improving predictive performance while enhancing interpretability and robustness. His work integrates statistical learning, causal modeling, and computational analytics to support evidence-based decision-making in healthcare.

Silas Kati collaborates across interdisciplinary teams involving clinicians, computer scientists, and statisticians to design reproducible analytic frameworks for biomedical informatics research.

Expertise

• Machine Learning
• Statistical Modeling
• Data Mining and Knowledge Discovery
• Parallel Algorithms and Scalability
• Causal Discovery
• Big Data