Stanford University
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Quinn Dombrowski
Academic Technology Specialist in Literatures, Cultures, and Languages, Center for Interdisciplinary Digital Research
BioQuinn Dombrowski (non-binary, any pronouns are fine) is the Academic Technology Specialist in the Division of Literatures, Cultures, and Languages, and in the Library, at Stanford University. Prior to coming to Stanford in 2018, Quinn’s many DH adventures included supporting the high-performance computing cluster at UC Berkeley, running the DiRT tool directory with support from the Mellon Foundation, writing books on Drupal for Humanists and University of Chicago library graffiti, and working on the program staff of Project Bamboo, a failed digital humanities cyberinfrastructure initiative.
Quinn has a BA/MA in Slavic Linguistics from the University of Chicago, and an MLIS from the University of Illinois at Urbana-Champaign. Since coming to Stanford, Quinn has supported numerous non-English DH projects, taught courses on non-English DH, started a Textile Makerspace, developed a tabletop roleplaying game to teach DH project management, explored trends in multilingual Harry Potter fanfic, and started the Data-Sitters Club, a feminist DH pedagogy and research group focused on Ann M. Martin’s 90’s girls series “The Baby-Sitters Club”. Quinn is currently co-VP of the Association for Computers and the Humanities along with Roopika Risam, and advocates for better support for DH in languages other than English. -
Ben Domingue
Associate Professor of Education and, by courtesy, of Sociology
Current Research and Scholarly InterestsI'm interested in models for psychological measurement and their uses alongside applied statistical projects of all kinds.
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Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Stefan Domino’s research interest rests within low-Mach fluid mechanics methods development for marine ethology-based 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. Through his ICME Adjunct Professor appointment, Stefan supports the teaching of Computational Methods in Fluid Mechanics, is a former Distinguished Member of the Technical Staff at Sandia National Laboratories, and is the CEO of the 501(c)(3) Computational Marine Ethology Research Institute, https://www.comeri.org
Education: University of Utah
Ph.D. Department of Chemical Engineering, 2000
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Domino, S. P., Scott, S., Hubbard, J., "Structural uncertainty assessment for fire-engulfed objects in crosswind: Establishing credibility for a multiphysics wall-modeled large-eddy simulation paradigm", Phys. Rev. Fluids, 2025.
* Domino, S. P., "On the subject of large-scale pool fires and turbulent boundary layer interactions", Phys. Fluids, 2024.
* 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.
* 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.
CV: https://github.com/spdomino/cv/blob/main/dominoCV.pdf -
Pablo Domizi, Ph.D.
Senior Research Scientist-Basic Life, Pediatrics - Hematology/Oncology
BioI am passionate about translational research and how single cell technologies could open up new avenues for better and more accurate predictive models. Currently, I am focus on integrating single cell RNA and protein expression data to develop models to predict patient at risk for Antigen Loss relapse after CAR T cells immunotherapy.
