The Dickson laboratory uses computational techniques such as molecular dynamics to simulate the motions of biomolecules (protein, RNA and DNA). These numerical experiments extend our knowledge beyond the "snapshots" provided by X-ray crystallography and NMR, and provide the entire landscape of conformations accessible to a molecular system. Our goal is to use simulations to gain a deep understanding of the ligand binding process, and use this knowledge to aid ongoing drug discovery efforts.
The Krishnan lab develops computational approaches for utilizing massive amounts of molecular and clinical data to unravel the genomic basis of complex disorders such as autism, diabetes, and hypertension. These approaches are at the intersection of biomedicine, statistics, applied mathematics, and computer science. We routinely build models that capture the molecular circuitry related to complex human diseases and make predictions about new, critical genes and cellular mechanisms that can help in understanding, diagnosing, and treating these diseases.
Dr. Moore’s lab build projections of climate and land use for rapidly changing landscapes, including the Amazon Basin, east Africa, and potentially in China. They seek to understand if and how humans are pushing ecosystems into new equilibria, and what impacts those equilibria might have on agriculture. Most recently they have been looking at changes in watersheds in Kenya, and trying to understand why they are broadly capturing less rainfall since the 1980s.
Dr. Elizabeth Munch is a mathematician specializing in Topological Data Analysis, a field of research combining algebraic topology, algorithms, statistics, and machine learning to quantify the shape and structure of data.
David Roy’s research interests include development of remote sensing and advanced computing methods to integrate/fuse satellite sensor data to map and characterize terrestrial global change, big satellite data processing, the causes and consequences of land cover/use change, and fire remote sensing.
The Shade Lab uses multi-omics approaches to understand microbiome resilience. Resilience is the capacity of a system to recover after it has been altered by a stress or disturbance. Our research will help us to understand and manage microbiomes towards functions that support system stability.
Our lab aims to understand and predict how biodiversity responds to change - especially climate change and land use change, in space and time. We work across scales from microcosm experiments to continental and global-scale spatial analyses with big data. We use a combination of observational data, experiments, and modeling to connect observed patterns of biodiversity and community composition with underlying mechanisms.
My research focuses on the mathematical foundation of data science. Related mathematics branches include applied harmonic analysis, convex optimization and high dimensional probability.