Institute for Computational and Mathematical Engineering (ICME)

Showing 1-9 of 9 Results

  • Marta D'Elia

    Marta D'Elia

    Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)

    BioI’m a research/computational scientist working on the design and analysis of models and data-driven algorithms for the simulation of complex, multiscale and multiphysics problems. My background and training have foundations in Numerical Analysis, Scientific Computing, Inverse Problems, Control and Optimization, and Uncertainty Quantification. In the past five years I have focused on Scientific Machine Learning (SciML) and Deep Learning. I am an expert in Nonlocal/Fractional Modeling and Simulation (10 years) with application to Continuum Mechanics, Subsurface Transport, Image Processing, and Turbulence. I have a master's degree in Mathematical Engineering from Politecnico di Milano (2007) and a PhD in Applied Mathematics from Emory University (2011).

  • Stefan P. Domino

    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 elucidation, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for turbulent flow applications. Stefan also supports the 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.


    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:

    * Domino, S. P., "On the subject of large-scale pool fires and turbulent boundary layer interactions", Phys. Fluids, 2024. (Featured)

    * Domino, S. P., Wenzel, E. A, "A direct numerical simulation study for confined non-isothermal jet impingement at moderate nozzle-to-plate distances: capturing jet-to-ambient density effects", Int. J. Heat Mass Trans, 2023.

    * Benjamin, M., Domino, S. P., Iaccarino, G., "Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges", Eur. Phys. J. E., 2023.

    * Hubbard, J., Cheng, M., Domino, S. P., "Mixing in low-Reynolds number reacting impinging jets in crossflow", J. Fluids Engr., 2023.

    * Domino, S. P. “Unstructured finite volume approaches for turbulence,” in Numerical Methods in Turbulence Simulation, edited by R. Moser (Elsevier, 2023), Ch. 7, pp. 285–317.

    * Scott, S., Domino, S. P., "A computational examination of large-scale pool fires: variations in crosswind velocity and pool shape", Flow, 2022.

    * 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.

    * Hubbard, J., Hansen, M., Kirsch, J., Hewson, J., Domino, S. P., “Medium scale methanol pool fire model validation”, J. Heat Transfer, 2022.

    * Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA J., 2021,

    * 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", Phys. Fluids, 2021. (Editor's pick)

    * 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.

    * 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.

    * 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 Fluid Flow, 2019.

    * Domino, S. P., Sakievich, P., Barone, M., "An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019.

    * Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018.

    * Jofre, L., Domino, S. P., Iaacarino, G., "A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018.


  • Julia Gillespie

    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.

  • Alexander Ioannidis

    Alexander Ioannidis

    Affiliate, 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 is a researcher in the Department of Biomedical Data Science at Stanford Medical School. He earned his Ph.D. from Stanford University in Computational and Mathematical Engineering together with an M.S. in Management Science and Engineering (Optimization). 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. His research 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.

    *For John Ioannidis (no relation), see here,

  • Ashwin Rao

    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:

    My Stanford Home Page:
    CME 241 ("RL for Finance"), which I teach each Winter quarter: