Subharup Guha, Ph.D.
Subharup Guha, Ph.D., is an expert in Bayesian biostatistical modeling for cancer genomics and computing for high-dimensional datasets. Before his faculty appointment, Subharup Guha, Ph.D., completed his M.Sc., at the Indian Institute of Technology, Kanpur, India, and Ph.D. at Ohio State University.
As PI or co-Investigator of research grants from NIH and NSF, he has developed novel Bayesian models for multi-domain, high-throughput biomedical studies. He has extensive experience with statistical computing to efficiently implement these methodologies for Big Data.
The primary focus of his research has been the development of broadly applicable, nonparametric statistical methodologies that are flexible because they avoid making unrealistic assumptions about the data features and permit nonlinear dependencies. This is scalable because the procedures are capable of accommodating the ever-expanding massive, multiple-domain datasets, even on a modest computing budget and most importantly, scientifically interpretable because they are based on models that incorporate domain knowledge and provide meaningful answers to key scientific questions that motivate the research.
What are your current research interests and/or what is a project you are currently working on?
My current research interests include the development of new statistical methods for identifying racial disparities in various cancer endpoints by integrating high dimensional observational studies comprising several racial groups and behavioral, dietary, clinical, demographic and multi-omic information on a large number of patients.
Why did you decide to focus on cancer?
A major motivation was that racial minority groups endure disproportionately higher cancer death burden, likely because of lack of access to healthcare and higher stage at diagnosis among racial minorities. In addition, racial inequities in cancer screening, diagnosis and treatment are continually widening despite overall improvements in cancer outcomes, suggesting deficiencies in research designs for understanding disparities.
What do you like to do outside of work?
I like playing tennis, listening to music and watching movies.