Yize Zhao · Mathematics & Statistics
Dr. Yize Zhao's research lab at Yale University focuses on developing new statistical methods to better understand Alzheimer’s disease (AD). The lab aims to integrate various brain imaging techniques with genetic data to identify how genetic factors influence the risk and progression of AD. By creating advanced analytical tools, the lab hopes to uncover new insights into the biological mechanisms behind Alzheimer’s, ultimately contributing to improved prevention and treatment strategies for this complex disease.
Donna L Spiegelman · Mathematics & Statistics
Dr. Donna L Spiegelman's lab at Yale University focuses on improving cardiovascular disease prevention through innovative research methodologies. Their main project, Learn-As-you-GO (LAGO), aims to create adaptable trial designs for complex interventions in cardiology, which allows for optimizations during the study to enhance effectiveness and reduce costs. This research is particularly relevant for managing interventions that involve multiple components and for tailoring approaches to meet diverse patient needs effectively.
Fan Li · Mathematics & Statistics
Dr. Fan Li's lab at Yale University focuses on improving statistical methods for cluster-randomized trials, which are vital for evaluating healthcare interventions. The lab's research specifically aims to enhance how we quantify treatment effects on multiple health outcomes, especially in complex scenarios like heart failure. This work involves developing new statistical techniques, creating software tools, and sharing knowledge through tutorials and case studies.
Hongyu Zhao · Mathematics & Statistics
The lab directed by Dr. Hongyu Zhao at Yale University focuses on understanding the genetic factors that influence brain responses to substance use disorders (SUD) and HIV. Through advanced statistical methods and single-cell analysis, their research aims to uncover how genetic variations affect molecular phenotypes in different brain regions, which could lead to new insights and applications in clinical settings. The lab also addresses health disparities in genetic risk prediction, emphasizing the need for models applicable to underrepresented populations.