Participants

Participants
(alphabetically listed; last updated 4/3/2024)
As participants are confirmed, we will update this page.

Prasanna Balaprakash, Oak Ridge National Laboratory
SCALABLE AUTOMATED DEEP ENSEMBLE FOR UNCERTAINTY QUANTIFICATION IN SCIENTIFIC MACHINE LEARNING
David Bortz, University of Colorado Boulder
WEAK-FORM LATENT SPACE DYNAMICS IDENTIFICATION WITH UQ
Marta D’Elia, Pasteur Labs & Stanford University
SCIENTIFIC MACHINE LEARNING IN INDUSTRIAL SETTINGS
Charbel Farhat, Stanford University
A NONPARAMETRIC PROBABILISTIC APPROACH FOR MODELING AND QUANTIFYING MODEL-FORM UNCERTAINTY IN CFD WITH TURBULENCE MODELING
Krishna Garikipati, University of Southern California
INFERENCE OF FOKKER-PLANCK EQUATIONS FOR THE DYNAMICS OF POPULATIONS
Roger Ghanem, University of Southern California
PHYSICS EXTRACTION PODS (PEP): AN EVOLUTION OF THE RVE
Somdatta Goswami, Johns Hopkins University
GRAPH EMBEDDED DEEP NEURAL OPERATOR FOR STRESS FIELD PREDICTION IN FIBER-REINFORCED COMPOSITES
Amanda Howard, Pacific Northwest National Laboratory
MORE OF A GOOD(?) THING: UNCERTAINTY PROPAGATION THROUGH MULTIFIDELITY DEEP OPERATOR NETWORKS
Yannis Kevrekidis, Johns Hopkins University
ON SAMPLING THE THINGS THAT DO NOT MATTER
Youssef Marzouk, Massachusetts Institute of Technology
Habib Najm, Sandia National Laboratories
APPROXIMATE BAYESIAN MODEL CALIBRATION WITH SUMMARY STATISTICS
Audrey Olivier, University of Southern California
EMBEDDING PHYSICS-DRIVEN UNCERTAINTY IN NEURAL NETWORKS WITH ANCHORED ENSEMBLES
Abani Patra, Tufts University
LANDSLIDES AND ROCKETS: DATA, ML AND UQ FOR COUPLED MULTISCALE PHYSICS
Elizabeth Qian, Georgia Institute of Technology
MULTIFIDELITY LINEAR REGRESSION FOR SCIENTIFIC MACHINE LEARNING FROM SCARCE DATA
Khachik Sargsyan, Sandia National Laboratories
EMBEDDED FRAMEWORK FOR MODEL ERROR QUANTIFICATION AND PROPAGATION
Fei Sha, Google
Jim Stewart, Sandia National Laboratories
SCIENTIFIC MACHINE LEARNING AT SANDIA: A SPOTLIGHT
Jouni Susiluoto,  Jet Propulsion Laboratory, California Institute of Technology
Nathaniel Trask, University of Pennsylvania
A DATA-DRIVEN EXTERIOR CALCULUS FOR PROBABILISTIC DIGITAL TWINS
Dongbin Xiu, The Ohio State University 
DATA DRIVEN MODELING OF STOCHASTIC SYSTEMS
Ruda Zhang, University of Houston
STOCHASTIC SUBSPACE VIA PROBABILISTIC PCA TO CHARACTERIZE AND CORRECT MODEL ERROR