Dr. Selin Aviyente's research is in the general area of signal processing and machine learning. In particular, she is interested in signal and information processing over networks/graphs. She is also interested in applications in neuroscience.
Dr. Colbry's team develops intelligent software tools to help scientists understand large scale data in image and video formats.
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 that can take advantage of massive and heterogeneous data collections to study the molecular and genetic basis of complex human diseases. Our research is at the interface of biomedicine, computer science, statistics, and applied mathematics.
Dr. Lifeng Luo's research covers a range of topics related to land-atmosphere interaction and its impact on the global climate and hydrological cycle at various spatial and temporal scales. His research involves the use of statistical and dynamical approaches to downscale large scale atmospheric fields for hydrological applications. His recent research focuses on the predictability and prediction of climate extremes such as drought, floods, heat waves at subseasonal to seasonal scale. His current research activities are supported by NOAA, NASA and USDA.
Elizabeth Munch is an assistant professor at Michigan State University with a primary appointment in the Department of Computational Mathematics, Science, and Engineering and a secondary appointment in the Department of Mathematics. Her research specializes in Applied Topology and Topological Data Analysis.
Dr. Ravishankar directs the Signals, Learning, and Imaging (SLIM) group in CMSE. His group's research interests include computational imaging, machine learning, signal and image processing, inverse problems, and large-scale data processing and optimization.
The Computational Education Research Lab (CERL) at Michigan State University is an interdisciplinary collaboration that studies how students learn to do computational work and their attitudes about doing that work. In particular, we explore how students learn to integrate computation into their education outside of traditional computer science courses.
The Zarnetske Spatial and Community Ecology Lab (SpaCE Lab) aims to understand and predict how ecological communities respond to change in space and time. We investigate how the composition and geographic distribution of ecological communities are affected by biotic interactions, species invasions, biophysical feedbacks, geodiversity, and climate change. Our spatial modeling research on biodiversity changes in response to environmental change spans local to global scales.