Research Activities

Research Activities

2023 Big Data Summer Institute in Biostatistics Projects

For project work, participants are divided into small research teams and assigned to one faculty member leading a particular project area of their interest. A graduate student research assistant is assigned to each project group to facilitate the project work.  

Project Group One: Imaging

Led by: Dr. Jian Kang

Medical imaging refers to a variety of techniques for visual representations of some organs or tissue in a body for clinical analysis and medical intervention. Recent advances in technologies can generate a large amount of high resolution images in biomedical and clinical studies. It presents great opportunities and challenges for precision medicine and many other areas. One important research topic is on imaging-guided clinical diagnosis of disease, where the statistical models and machine learning algorithms play an important role.  The BDSI imaging research group will focus on the imaging-based disease classification and feature selection problem. The project will consist of using imaging data to predict the disease status or the cognitive state of subjects. A training set will be used to build a classifier and identify important imaging biomarkers; and a testing set of data will be used to validate the prediction and feature selection performance.  With the help of the instructors and graduate student assistant, the students will learn basic knowledge and computing tools for biomedical imaging data analysis;   and will decide how they wish to model the data and perform the analysis. Either traditional statistical models and/or machine learning algorithms may be used. 

Project Group Two: Data Mining/Statistics

Led by: Dr. Snigdha Panigrahi


Project Group Three: Genomics

Led by: Dr. Matt Zawistowski

The Genomics group will have multiple available projects connecting a health-related question to a large-scale genomic dataset, for example whole-genome Single Nucleotide Polymorphism data, single-cell RNA sequencing data or epigenetic methylation data. Students will form teams for a deep dive analysis on their specific project of interest with opportunity for open-ended exploration. Students will gain hands-on computing experience and valuable data manipulation skills working with the large genomic data files. We will apply many classical statistical techniques, learn about integration of complementary genomic data sources and explore machine learning and specialized genomic analysis methods. 

Project Group Four: Machine Learning

Led by: TBD