Schedule

Thursday, June 2

10:45Welcome
11:00-11:45 Bayesian Inference Using Surrogate OperatorsAndrew Stuart
11:45-1:30LunchNearby options
1:30-2:15Localization Schemes: A Framework for Proving Mixing Bounds for MCMC Sampling Algorithms Yuansi Chen
2:15-3:00Thermal State Sampling for Numerical Linear AlgebraMichael Lindsey
3:00-3:30Break
3:30-4:30 Discussion Session
4:30-6:30Hosted Reception: Guests and speakers are welcome to join usLocation: Patio of Duke's Devil's Krafhouse

Friday, June 3

9:00-9:45Prior-preconditioned Conjugate Gradient Method for Accelerated Gibbs Sampling in "large n & large p" Bayesian Sparse RegressionAkihiko Nishimura
9:45-10:30The Proximal SamplerSinho Chewi
10:30-11:00Break
11:00-11:45 Learning High-dimensional Functions for Rare Events Sampling In and Out of EquilibriumGrant Rotskoff
11:45-1:30Hosted Lunch: Guests and speakers are welcome to join usPenn Pavilion
1:30-2:15Sampling Under SymmetryNisheeth Vishnoi
2:15-3:00How Much Can One Learn a PDE from its Solution Data?Hongkai Zhao
3:00-3:30Break
3:30-4:30 Discussion Session

Saturday, June 4

9:00-9:45Identifying Differential Equations with Numerical Methods: Time Evolution, Subspace Pursuit and Weak FormSung-Ha Kang
9:45-10:30Auto-differentiable Ensemble Kalman FiltersDaniel Sanz-Alonso
10:30-11:00BreakGrab-and-go fruit and snacks provided
11:00-11:45 Transport Maps for Conditional Simulation in High DimensionsBamdad Hosseini
11:45-12:30Revisiting Least-Squares Formulations for Computational Inverse ProblemsKui Ren
12:30Closing Remarks
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