Minisymposia

The conference will feature five themes organized in 10 minisymposia (two per theme). Minisymposia are by invitation only. 

Below, we list the themes and the minisymposia organizers of the two, separate sessions associated with each theme.
Speakers and abstracts will be posted together with the final program.

Theme 1: Mathematical analysis

MS1.1: Jon Wilkening (UC Berkeley, LBNL)

MS1.2: Mayya Tokman and Andy Wan (UC Merced)

Theme 2: Optimization, inverse problems, and experimental design

MS2.1: Robert Bassett (Naval Postgraduate School)

MS2.2: Roummel Marcia and Chrysoula Tsogka (UC Merced)

Theme 3: Scientific and high performance computing

MS3.1: Cody Balos (LLNL)

MS3.2: Chao Yang (LBNL)

Theme 4: Uncertainty quantification and prediction

MS4.1: Habib Najm and Cosmin Safta (Sandia), and Noemi Petra (UC Merced)

MS4.2: Daniel M. Tartakovsky (Stanford University)

Theme 5: Scientific machine learning, AI and digital twins

MS5.1: Shima Alizadeh (Amazon)

MS5.2: Youngsoo Choi (LLNL)