2026-2027 Seminar

Following a successful slate of talks last year, we are exicted to host a series of in person and virtual seminars focused on "Inverse Problems, Uncertainty Quantification, and Digital Twins" for the 2025-2026 academic years. Below will be a list of speakers, dates, and Zoom links when applicable.

September 17th, 2026 - Mitchel Colebank, University of South Carolina "An introduction to inverse problems and UQ for mechanistic models"

October 1st, 2026 - Ponkrshnan Thiagarajan, Johns Hopkins University "Scalable Bayesian Neural Networks for Uncertainty Quantification and Applications to Inverse Problems"

October 29th, 2026 - Andrew Brown, Clemson University TBD

November 12th, 2026 - Elizabeth Qian, Gerogia Tech, TBD

2025-2026 Seminar

September 17th, 2025 - Mitchel Colebank, University of South Carolina "An overview of the working group and a summary of the National Academies report on 'Foundational Research Gaps in Digital Twins.'" Slides

October 1st, 2025 - Kristen Booth, University of South Carolina "A journey through the digital twin definition."

October 15th, 2025 - Rebekah White, Sandia National Labs. "Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion" (Virtual; manuscript, Slides)

October 29th, 2025 - Zack Morrow, Sandia National Labs. "Leveraging mathematical structure for efficient operator learning" (Virtual)

November 12th, 2025 - Sez Atamturktur Russcher, Clemson University, "Model validation and uncertainty quantification in scientific computing of engineering systems" (Virtual)

February 26th, 2026 - Mitchel Colebank, University of South Carolina, "Digital Twin proposals to the NSF: linking modeling, data science, and clinical research."

April 9th, 2026 - Pras Pathmanathan, US Food and Drug Administration, "Computational Modeling and Simulation For Medical Devices"

May 7th, 2026 - Annie Booth, Virginia Tech, "Deep Gaussian Process Surrogates: Past, present, and future"

May 14th, 2026 - Ramin Bostanabad, University of California, Irvine, "Gaussian Processes in Scientific Machine Learning: Beyond Scalability"