Talks and presentations

Invited talks

• 2025 Multiphase flow and scientific machine learning. Arizona State University Association for Women in Mathematics, Tempe, AZ.
• 2026 Multifidelity, domain decomposition, and symbolic regression for improving training for physics-informed networks. Computational and Applied Math / Data Science Seminar, Arizona State University, Tempe, AZ.
• 2026 Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks. Institute for Pure and Applied Mathematics, UCLA, Los Angeles, CA. Video.
• 2025 Accelerating physics discovery with machine learning. Society of Rheology Annual Meeting, Data-driven Rheology Session Keynote, Santa Fe, NM.
• 2025 More of a Good Thing: Combining Multifidelity, Domain Decomposition, and New Architectures for Improved Physics-Informed Training. Accuracy and Efficiency in Scientific Machine Learning Workshop, Montreal, Canada.
• 2025 Multiphase flow and scientific machine learning. University of Washington Association for Women in Mathematics, Seattle, WA.
• 2025 Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks. University of Washington AI Institute in Dynamical Systems, Seattle, WA. Video.
• 2025 Multifidelity, domain decomposition, and stacking for improving training for physics- informed networks. TU Delft Seminars in Numerical Analysis, Delft, Netherlands.
• 2025 Scientific Machine Learning at Pacific Northwest National Laboratory. Joint Mathematics Meetings, Seattle, WA.
• 2025 Multifidelity, domain decomposition, and stacking for improving training for physics- informed networks. USACM Technical Thrust Area on UQ and Probabilistic Modeling Seminar, Online. Video.
• 2024 Multifidelity, domain decomposition, and stacking for improving training for physics- informed networks. Washington State University Mechanical and Materials Engineering Seminar, Pullman, WA.
• 2024 More of a good(?) thing: uncertainty propagation through multifidelity deep operator net- works. Uncertainty Quantification for Machine Learning Integrated Physics Modeling, Arlington, VA.
• 2024 Machine learning for Stokes flow: from suspensions to ice sheets Advancing fluid and soft-matter dynamics with machine learning and data science, UW Madison, Madison, WI.
• 2024 Multifidelity stacking networks for physics-informed training. Data Sciences for Mesoscale and Macroscale Materials Models, IMSI, Chicago, IL.
• 2024 High performance computing for multiphase flows. Portland State University, Portland, OR.
• 2023 High performance computing for multiphase flows. Spelman College Senior Seminar, Atlanta, GA.
• 2023 Multifidelity Deep Operator Networks. Mathematical and Scientific Machine Learning Workshop, ICERM, Providence, RI.
• 2023 Multifidelity deep operator networks with applications to ice sheet modeling. Computational Fluids Conference, Cannes, France.
• 2023 Continual learning for physical systems. CRUNCH Seminar, Brown University, Providence, RI (virtual)
• 2022 Multifidelity Machine Learning Methods. CMIT Seminar, University of Liverpool, Liverpool, UK (virtual)
• 2022 High performance computing for multiphase flows. HPC Parallel Programming Workshop, Lehigh University, Bethlehem, PA (virtual)
• 2022 Multifidelity Deep Operator Networks. CRUNCH Seminar, Brown University, Providence, RI (virtual)
• 2022 Nonlocal surface tension for N-phase flows. Fluids Seminar, Brown University, Providence, RI (virtual)
• 2021 Two multifidelity approaches for machine learning. RAMSES: Reduced order models; Approximation theory, Machine Learning; Surrogates, Emulators and Simulators, Trieste Italy (virtual)
• 2021 Nonlocal models for modeling multiphase fluids. Arizona State University, Tempe, AZ (virtual)
• 2021 Nonlocal models for modeling multiphase fluids. San Diego State University, San Diego, CA (virtual)
• 2021 Nonlocal models for modeling multiphase fluids. University of Washington, Seattle, WA (virtual)
• 2018 Particle Dispersion in Non-Homogeneous Suspension Flows. National Institute of Standards and Technology, Gaithersburg, MD
• 2017 Particle Dispersion in Non-Homogeneous Suspension Flows. Computational and Applied Math Seminar, Tufts University, Medford, MA