Stanford University
Showing 21-30 of 116 Results
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Margariete Malenda
Ph.D. Student in Geophysics, admitted Spring 2020
BioPlease refer to my CV for a full listing of conference presentations, publications, research projects (internships and degree-oriented) and outreach.
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Lisa Mandle
Lead scientist
BioLisa Mandle (she/her) is Director of Science-Software Integration and a Lead Scientist with the Natural Capital Project. She works to make ecosystem service science accessible and actionable through NatCap’s data and software, overseeing our software team. Her research sheds light on how land management and infrastructure development affect ecosystem services, social equity, and human health. Lisa works with governments, multi-lateral development banks, and non-governmental organizations to incorporate this understanding into policy and finance, particularly in Latin America and Asia. She is also lead editor of the book Green Growth That Works, which provides a practical guide to policy and finance mechanisms from around the world for securing benefits from nature.
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Ali Mani
Associate Professor of Mechanical Engineering
BioAli Mani is an associate professor of Mechanical Engineering at Stanford University. He is a faculty affiliate of the Institute for Computational and Mathematical Engineering at Stanford. He received his PhD in Mechanical Engineering from Stanford in 2009. Prior to joining the faculty in 2011, he was an engineering research associate at Stanford and a senior postdoctoral associate at Massachusetts Institute of Technology in the Department of Chemical Engineering. His research group builds and utilizes large-scale high-fidelity numerical simulations, as well as methods of applied mathematics, to develop quantitative understanding of transport processes that involve strong coupling with fluid flow and commonly involve turbulence or chaos. His teaching includes the undergraduate engineering math classes and graduate courses on fluid mechanics and numerical analysis.
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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.
