Short Bio: Dr. Gaynanova’s research focuses on the development of statistical methods for analysis of modern high-dimensional biomedical data. Her methodological interests are in data integration, machine learning and high-dimensional statistics, motivated by challenges arising in analyses of multi-omics data (e.g., RNASeq, metabolomics, micribiome) and data from wearable devices (continuous glucose monitors, ambulatory blood pressure monitors, activity trackers). Her research has been funded by the National Science Foundation, and recognized with a David P. Byar Young Investigator Award and an NSF CAREER Award.

Abstract: Multi-view data, where different data types are collected from the same samples, are increasingly prevalent due to advances in omics and wearable technologies. For instance, The Cancer Genome Atlas provides omics data from multiple platforms, while wearable devices such as continuous glucose monitors (CGMs) and accelerometers record high-frequency signals alongside tabular clinical characteristics. Integrating these views can enhance scientific insights but also presents significant analytic challenges. In this talk, I will start with multi-omics data, briefly reviewing our work on handling mixed variable types (continuous, binary, zero-inflated) and on distinguishing between joint and individual signals. I will then turn to settings where one of the views is representing wearable signal. Standard analyses typically reduce these recordings to a handful of scalar summaries. An appealing alternative is to treat each subject’s measurements as a sample from a subject-specific distribution and to take that distribution itself as the unit of analysis. I will illustrate regression with distribution-valued responses, including extensions beyond the univariate case where optimal transport becomes computationally prohibitive, and conclude with ongoing work using these distributional representations in genetic association studies. The methods will be illustrated on data from multi-omics and CGM studies.