Institute for Computational and Mathematical Engineering (ICME)
Showing 1-10 of 12 Results
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Corinne Beck
Affiliates & Partners Program Manager, Institute for Computational and Mathematical Engineering (ICME)
Current Role at StanfordPrograms Manager
Institute for Computational & Mathematical Engineering (ICME)
School of Engineering -
Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Domino’s research interest rests within low-Mach 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. Stefan also supports the co-teaching of ME469, Computational Methods in Fluid Mechanics, while continuing his primary career at Sandia National Laboratories as a Distinguished Member of the Technical Staff.
Education:
University of Utah
Ph.D. Department of Chemical Engineering, 1999
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Scott, S., Domino, S. P., "A computational examination of large-scale pool fires: variations in crosswind velocity and pool shape", Flow, 2022, https://doi.org/10.1017/flo.2022.26.
* Domino, S. P., Horne, W., "Development and deployment of a credible unstructured, six-DOF, implicit low-Mach overset simulation tool for wave energy applications", Renew. Energy, 2022, https://doi.org/10.1016/j.renene.2022.09.005.
* Hubbard, J., Hansen, M., Kirsch, J., Hewson, J., Domino, S. P., “Medium scale methanol pool fire model validation”, J. Heat Transfer, 2022, https://doi.org/10.1115/1.4054204.
* Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA Journal, 2021, https://doi.org/10.2514/1.J060919.
* 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.
CV: https://github.com/spdomin/Present/blob/master/cv/dominoCV.pdf -
Julia Gillespie
Director of Finance and Operations, Institute for Computational and Mathematical Engineering (ICME)
Current Role at StanfordI am the Director of Finance and Operations for the Institute for Computational Mathematics and Engineering within the School of Engineering.
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Alexander Ioannidis
Instructor, Biomedical Data Science
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)BioDr. Alexander Ioannidis is an Adjunct Professor in Computational and Mathematical Engineering, where he teaches machine learning and data science, and a researcher and Instructor in the Department of Biomedical Data Science. He earned his Ph.D. from Stanford University in Computational and Mathematical Engineering together with an M.S. in Management Science and Engineering (Optimization). Prior to Stanford, he worked in superconducting computing logic and quantum computing at Northrop Grumman. He graduated summa cum laude from Harvard University in Chemistry and Physics and earned an M.Phil at the University of Cambridge from the Department of Applied Math and Theoretical Physics in Computational Biology, and a Diploma in Greek. As a current researcher in the Stanford School of Medicine, Department of Biomedical Data Science his work focuses on the design of algorithms and application of computational methods for problems in genomics, clinical data science, and precision health with a particular focus on underrepresented populations in Oceania and Latin America.
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Ashwin Rao
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioMy current research and teaching is in Machine Learning (specifically RL) with applications to Financial Markets and Retail businesses. My academic origins are in Algorithms Theory and Abstract Algebra. More details on my background are here: https://www.linkedin.com/in/ashwin2rao/
My Stanford Home Page: https://stanford.edu/~ashlearn
CME 241 ("RL for Finance"), which I teach each Winter quarter: http://cme241.stanford.edu
