UIUC’s STEM-designated MS in Biostatistics trains quantitative thinkers to wrangle omics, EHRs, and real-world evidence for actionable insights. Rigorous sequences in survival analysis, Bayesian inference, and causal modeling pair with cloud-scale computing labs where students fit mixed models on millions of patient-years. Practica with Carle Health and the Illinois Department of Public Health place theory into pandemic surveillance, health-equity audits, and precision-medicine trials.
Genome-wide association meta-analysis of rare disease cohorts
Target-trial emulation using EHRs for vaccine effectiveness
Spatial Bayesian model of opioid overdose clusters
Machine-learning survival forest predicting heart-failure readmission
Differential-expression pipeline with batch-effect correction in scRNA-seq
Synthetic-control study evaluating Medicaid expansion on mortality
Zero-inflated Poisson model of microbiome OTU counts
Privacy-preserving distributed regression across hospital networks
Policy brief on algorithmic equity in sepsis prediction scores
Grant proposal for wastewater SARS-CoV-2 trend monitoring
Interactive dashboard of maternal mortality disparities
Podcast explaining causal DAGs to clinicians
Dynamic transmission model for RSV forecasting
Principal stratification analysis of treatment compliance in RCTs
Competing-risks model for oncology trial endpoints
Meta-analysis automation tool using natural-language processing
White paper on adaptive platform trials in outbreaks
Turn complex health data into lifesaving knowledge with UIUC Biostatistics.
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