Stanford University
Showing 31-40 of 177 Results
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Rim Malek
Postdoctoral Scholar, Molecular Imaging Program at Stanford
Current Research and Scholarly InterestsMy work is focused on the development of small molecules radiotracers for cancer imaging, and small molecules and peptides theranostics for cancer detection, targeted radionuclide therapy, and monitoring of tumor response to therapy.
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Lise Mangiante
Postdoctoral Scholar, Stanford Cancer Center
Current Research and Scholarly InterestsMy research focused on understanding the evolution and ecology of cancer, and determinants of disease progression through analysis and modeling of high-dimensional, clinically annotated datasets.
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Morgan Mann
Postdoctoral Scholar, Pathology
BioMorgan W. Mann, PhD, is a postdoctoral researcher at Stanford University and clinical chemistry DABCC fellow at the University of California – San Francisco (UCSF). His personal and professional interests involve the development of novel clinical assays to streamline medical diagnostics and address emerging challenges to our healthcare systems. Prior to his joint positions at Stanford and UCSF, Morgan earned his PhD in Cellular and Molecular Pathology at the University of Wisconsin – Madison, where he applied mass spectrometry-based proteomics to study innate inflammation signaling pathways and viral protein structure in the context of airway infection. He received dual bachelor’s degrees in Biochemistry and Mathematics from the University of Oklahoma.
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Laura Mansfield
Postdoctoral Scholar, Earth System Science
BioI am interested in how machine learning and Bayesian statistics can assist our understanding and prediction of the climate and weather. My current research focuses on improving gravity wave parameterizations in atmospheric circulation models, which are necessary to capture the subgrid-scale gravity waves that influence the middle atmosphere dynamics. Machine learning can be used to either improve existing physics-based parameterizations, i.e. through calibration, or to replace these entirely with novel machine learning alternatives. I work on both of these approaches and am particularly interested in exploring uncertainties arising from parameterizations.
Previously, I completed my PhD at the University of Reading, which focused on emulating climate models to estimate the surface temperature response to changes in anthropogenic forcings, including both long-lived greenhouse gases and short-lived aerosol pollutants. Prior to this, I completed the Mathematics of Planet Earth MRes at University of Reading, after coming from an undergraduate degree in Physics at Imperial College London. Outside of work, my interests include cycling, running and being outdoors in California.
