Des Weighill, PhD
they/them
computational biology - data science - high performance computing - data integration
Current position
Postdoctoral Associate- UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
Previous position
Postdoctoral Fellow - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University
Academic highlights:
Pope Fellowship UNC LCCC (2021)
Association for Computing Machines (ACM) Gordon Bell Prize for supercomputing (2018)
20 papers published in peer-reviewed journals, including Nature Plants, Nature Scientific Reports and Nature Communications
Distinguished Achievement Award, Biosciences Division, Oak Ridge National Laboratory
About me
I am a postdoctoral fellow working with Dr. John Quackenbush in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health, developing and applying data-driven methods in computational and systems biology to further our understanding of cancer and pulmonary disease. I hold a BSc in Mathematical Sciences, focusing in biomathematics and molecular biology, an MSc in Biotechnology in which my research focus was computational biology and a PhD in Energy Science and Engineering.
My PhD research entailed the integration of multiple -omics data types, such as genomic, transcriptomic, phenotypic, metabolomic, genome variant, genome-wide association study and epigenomic datasets, with an emphasis on extracting valuable, interpretable information from large, complex datasets. This involved using high performance computing resources at the Oak Ridge Leadership Computing Facility (OLCF). My research has included developing methods for the analysis and integration of various large -omics datasets, transforming them from data to information which can be easily interpreted and intuitively visualized. The approaches I've developed have been applied in bioenergy research to investigate the genomic basis of complex traits in the biofuels crop Populus trichocarpa. The power of quantitative methods is that they can generally be applied to the analysis of various biological systems. My current research involves the development and application of methods to increase our systems-level understanding of cancer and pulmonary disease, making use of multiple -omics data types, as well as a focus on investigating the effect of genetic variation on transcription factor regulatory networks.