Environmental Health : Big Data

Program Overview

The PhD in Environmental Health: Big Data program combines statistical and computational education with a focus on biomedical research applications. It highlights the interdisciplinary nature of biostatistics and bioinformatics, exploring overlaps between fields to analyze high-dimensional datasets. The program emphasizes methodological research and interdisciplinary biomedical research projects, offering a track in Big Data for students interested in this area.

Program Outline

Degree Overview:

The PhD in Environmental Health: Big Data program is housed in the Department of Environmental Health at the University of Cincinnati, with collaborations across the College of Medicine and Cincinnati Children’s Hospital Medical Center. The program is offered through the Division of Biostatistics and Bioinformatics, which provides training and research opportunities in biomedical data sciences. The program combines rigorous statistical and computational education rooted in probability theory and computer science with exposure to a broad range of biomedical research applications. It focuses on the application of statistical reasoning in biomedical and public health research, developing statistical methodologies tailored to address specific biomedical data analysis problems. The program emphasizes the interdisciplinary nature of biostatistics and bioinformatics, combining computer science, statistics, mathematics, and engineering to manage, process, and analyze biomedical data. It explores the overlaps between these fields, particularly in the analysis of high-dimensional datasets like genomics, proteomics, and metabolomics. The program highlights the growing importance of data sciences in contemporary biomedical and public health research, addressing the challenges and opportunities presented by accessing, managing, analyzing, and integrating diverse datasets.

Other:

• Research Focus: The program emphasizes methodological research in areas like statistical methods for multiple hypothesis testing, statistical genetics, supervised and unsupervised Bayesian and machine learning methods for genomics data analysis, methods for next-generation sequencing data analysis, statistical geospatial modeling, integrative statistical models for Big Data, computational drug screening, and protein structure modeling.
• Interdisciplinary Research: The program involves faculty in interdisciplinary biomedical research projects, including studies on predictive transcriptional signatures for juvenile idiopathic arthritis, genomic determinants of kidney cancer, cancer treatment clinical trials, and numerous biomedical projects investigating gene-environment interactions.
• Program Tracks: The Division of Biostatistics and Bioinformatics offers both a doctoral program in Biostatistics and a doctoral program in Biostatistics with a track in Big Data.
Students interested in the Big Data track should indicate this in their personal statement.