Jean Fan, an assistant professor in the Department of Biomedical Engineering, is a recipient of the National Science Foundation CAREER Award, which recognizes early stage scholars with high levels of promise and excellence. Fan's research focuses on understanding the spatial-contextual and other regulatory mechanisms that shape cellular identity and heterogeneity, with a special emphasis on cancer. Her CAREER project, "Statistical Approaches and Computational Tools for Analyzing Spatially Resolved Single-Cell Transcriptomics Data," aims to provide insights into how spatial gene expression patterns intersect with the organization of distinct cell types and cell states within tissues, how cells interact within local microenvironments, and, ultimately, how such spatial organization relates to cellular function and phenotype.
Somnath Ghosh, the Michael G. Callas Professor in the Department of Civil and Systems Engineering, has received the U.S. Association for Computational Mechanics' 2021 J. Tinsley Oden Medal in recognition of his "outstanding fundamental contributions to computational mechanics of materials through the development of innovative methodologies in spatio-temporal multiscale modeling of heterogeneous materials transcending the mechanics and materials communities." Ghosh's research focuses on computational mechanics modeling, particularly multiscale structure-materials analysis and simulations, multiphysics modeling and simulation of multifunctional materials, materials characterization, process modeling, and emerging fields such as integrated computational materials engineering. He was presented with the award in a virtual ceremony held during the 16th U.S. National Congress on Computational Mechanics in July.
Vishal Patel, an assistant professor in the Department of Electrical and Computer Engineering, has won a National Science Foundation CAREER Award, which recognizes early stage scholars with high levels of promise and excellence. Supported by the $500,000, five-year award, Patel and his team will develop data-driven learning-based approaches for restoration and understanding of images degraded by atmospheric turbulence. The algorithms they develop aim to significantly enhance the quality of images and videos collected by long-range visible and infrared imaging systems. The title of his project is "Seeing Through Atmospheric Turbulence: Image Restoration and Understanding Using Deep Convolutional Neural Networks."
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