University of Florida · Mathematics & StatisticsDaniels Lab
Michael J Daniels · Gainesville, Florida
Research Focus
Michael J. Daniels' research lab focuses on developing advanced statistical methods to handle complex data issues in healthcare, particularly regarding cardiovascular health and obesity. The lab utilizes Bayesian machine learning to improve how missing data is addressed in clinical studies, which is crucial in accurately evaluating treatment effectiveness. The research aims to enhance our understanding of how different treatments work and to create better interventions in public health.
Bayesian machine learningmissing datacausal inferencecardiovascular studiesobesity studieselectronic health recordsmultivariate longitudinal datacluster randomized trialsbehavior change
Research Aims
5AIM 01Develop Bayesian machine learning techniques for handling missing data in clinical studies
AIM 02Conduct causal inference analyses to understand treatment effects in cardiovascular health
AIM 03Evaluate the impact of behavioral interventions on weight management
AIM 04Investigate the relationship between bariatric surgery types and cancer outcomes
AIM 05Create accessible software tools for researchers in related fields.
Skills the Lab Values
Bayesian modelingstatistical codingmachine learning algorithmsMCMC methodsdata analysisclinical trial designlongitudinal data analysisR programmingPython programming
Output against the department
Publications415top 21% of dept.28th of 131
this lab
most publishedleast published
Citations18ktop 25% of dept.33rd of 131
this lab
most citedleast cited
Career totals from OpenAlex, compared against labs that share a department. A department spans everything from one paper to tens of thousands, so bar heights are on a log scale — otherwise the largest lab would flatten every other bar to a line.