Institute for Computational and Mathematical Engineering (ICME)
Showing 21-40 of 203 Results
-
Carlos Bustamante
Professor of Biomedical Data Science, of Genetics and, by courtesy, of Biology
Current Research and Scholarly InterestsMy genetics research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. We focus on novel methods development for complex disease genetics and risk prediction in multi-ethnic settings. I am also interested in clinical data science and development of new diagnostics.I am also interested in disruptive innovation for healthcare including modeling long-term risk shifts and novel payment models.
-
Emmanuel Candes
Barnum-Simons Chair of Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. -
Gunnar Carlsson
Ann and Bill Swindells Professor, Emeritus
BioDr. Carlsson has been a professor of mathematics at Stanford University since 1991. In the last ten years, he has been involved in adapting topological techniques to data analysis, under NSF funding and as the lead PI on the DARPA “Topological Data Analysis” project from 2005 to 2010. He is the lead organizer of the ATMCS conferences, and serves as an editor of several Mathematics journals
-
Eric Darve
Professor of Mechanical Engineering
Current Research and Scholarly InterestsProfessor Darve's research is focused on the development of numerical methods for high-performance scientific computing, numerical linear algebra, fast algorithms, parallel computing, anomaly detection, and machine learning with applications in engineering.
-
Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioStefan's research interest rests within low-Mach turbulent fluid mechanics methods development for complex systems that drive the coupling of mass, momentum, species and energy transport. His core research resides within the intersection of physics model development, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for low-Mach turbulent flow applications. Dr. Domino also supports the co-teaching of ME469 while continuing his primary career at Sandia National Laboratories.
Select Publications:
* Domino, S. P., Hewson, J., Knaus, R., Hansen, M., "Predicting large-scale pool fire dynamics using an unsteady flamelet- and large-eddy simulation-based model suite", Physics of Fluids, 2021, https://doi.org/10.1063/5.0060267 (Editor's pick: August 4, 2021).
* Domino, S. P., " A case study on pathogen transport, deposition, evaporation and transmission: linking high-fidelity computational fluid dynamics simulations to probability of infection", Int. J. CFD, 2021, https://doi.org/10.1080/10618562.2021.1905801.
* Domino, S. P., Pierce, F., Hubbard, J., " A multi-physics computational investigation of droplet pathogen transport emanating from synthetic coughs and breathing", Atom. Sprays, 2021, https://doi.org/10.1615/AtomizSpr.2021036313.
* Jofre, L., Domino, S. P., Iaacarino, G., " Eigensensitivity analysis of subgrid-scale stresses in large-eddy simulation of a turbulent axisymmetric jet", Int. J. Heat Mass, 2019, https://doi.org/DOI:10.1016/J.IJHEATFLUIDFLOW.2019.04.014.
* Domino, S. P., Sakievich, P., Barone, M., " An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019, https://doi.org/10.1016/j.compfluid.2018.12.002.
* Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018, https://doi.org/10.1016/j.jcp.2018.01.007.
* Jofre, L., Domino, S. P., Iaacarino, G., " A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018, https://doi.org/10.1007/s10494-017-9844-8. -
David Donoho
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
On Leave from 09/01/2021 To 12/31/2021BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
Research Statement:
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems. -
Ron Dror
Associate Professor of Computer Science and, by courtesy, of Molecular and Cellular Physiology and of Structural Biology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
-
Eric Dunham
Associate Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
