Statistical and Computational Analyses
Based on the developmental outcomes of infants in our study, PROGRESS aims to identify new genes that are associated with increased risk of autism and developmental differences. Our Statistical and Computational Analysis Core will also integrate genetic, caregiver, and infant developmental data to obtain a complete picture of a child’s risk of developing autism.
Project Leads
Melanie Wall, PhD
- Professor of Biostatistics in Psychiatry
- Statistical and Computational Analysis Core - Lead
Dr. Wall has worked extensively with modeling complex multilevel and multimodal data on a wide array of psychosocial public health and psychiatric research questions in both clinical studies and large epidemiologic studies. She is an expert in longitudinal data analysis and latent variable modeling, including structural equation modeling focused on mediating and moderating (interaction) effects where she has made many methodological contributions.
Yufeng Shen, PhD
- Associate Professor of Systems Biology and Biomedical Informatics
- Genomics of Autism - Co-Lead
- Statistical and Computational Analysis Core - Co-Lead
After completing his PhD in computational biology in 2007 at the Human Genome Sequencing Center at Baylor College of Medicine, he led the analysis of the first personal genome produced by next-generation sequencing (that of Dr. James D. Watson). In 2008, he joined Columbia University as a postdoctoral fellow, working in computational genomics and genetics of drug adverse reactions, and then joined the faculty in July 2011. Dr. Shen is interested in developing and applying computational methods to study human genetics and diseases.