Participants

Participants
(alphabetically listed; last updated 7/10/2024)
As participants are confirmed, we will update this page.

Ishfaq Aziz, University of Illinois Urbana-Champaign
Bahador Bahmani, Johns Hopkins University
RESOLUTION INDEPENDENT NEURAL OPERATOR (RINO)
Prasanna Balaprakash, Oak Ridge National Laboratory
SCALABLE AUTOMATED DEEP ENSEMBLE FOR UNCERTAINTY QUANTIFICATION IN SCIENTIFIC MACHINE LEARNING
Alejandro Becerra, Tufts University
Christophe Bonneville, Sandia National Laboratories
Ramin Bostanabad, University of California, Irvine
Tan Bui-Thanh, The University of Texas at Austin
Promit Chakroborty, Johns Hopkins University
Krishnanunni Chandradath Girija, The University of Texas at Austin
Agnimitra Dasgupta, University of Southern California
Marta D’Elia, Pasteur Labs & Stanford University
SCIENTIFIC MACHINE LEARNING IN INDUSTRIAL SETTINGS
Som Dhulipala, Idaho National Laboratory
Pan Du, University of Notre Dame
Danial Faghihi, University at Buffalo
Ionut Farcas, Virginia Tech
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
Georgios Georgalis, Tufts University
Roger Ghanem, University of Southern California
PHYSICS EXTRACTION PODS (PEP): AN EVOLUTION OF THE RVE
Haiwen Guan, The Pennsylvania State University
Ashwini Gupta, Johns Hopkins University
Joseph Hart, Sandia National Laboratories
Amanda Howard, Pacific Northwest National Laboratory
MORE OF A GOOD(?) THING: UNCERTAINTY PROPAGATION THROUGH MULTIFIDELITY DEEP OPERATOR NETWORKS
Chengyang Huang, University of Michigan
Nikhil Iyengar, Georgia Institute of Technology
Birendra Jha, University of Southern California
Teeratorn Kadeethum, Sandia National Laboratories
Yannis Kevrekidis, Johns Hopkins University
ON SAMPLING THE THINGS THAT DO NOT MATTER
Wesley Lao, University of Texas at Austin
Xin-Yang Liu, University of Notre Dame
Simon Mak, Duke University
Zachariah Malik, University of Colorado Boulder
Youssef Marzouk, Massachusetts Institute of Technology
Romit Maulik, Pennsylvania State University & Argonne National Laboratory
Kathryn Maupin, Sandia National Laboratories
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
Georgios Pasparakis, Johns Hopkins University
Abani Patra, Tufts University
LANDSLIDES AND ROCKETS: DATA, ML AND UQ FOR COUPLED MULTISCALE PHYSICS
Russell Philley, The University of Texas at Austin
Elizabeth Qian, Georgia Institute of Technology
MULTIFIDELITY LINEAR REGRESSION FOR SCIENTIFIC MACHINE LEARNING FROM SCARCE DATA
Weilun Qiu, University of Pennsylvania
Fernando Rochinha, Universidade Federal do Rio de Janeiro
Khachik Sargsyan, Sandia National Laboratories
EMBEDDED FRAMEWORK FOR MODEL ERROR QUANTIFICATION AND PROPAGATION
Toryn Schafer, Texas A&M University
Himanshu Sharma, Johns Hopkins University
Jim Stewart, Sandia National Laboratories
SCIENTIFIC MACHINE LEARNING AT SANDIA: A SPOTLIGHT
Pranv Sunil, Rutgers University
Jouni Susiluoto,  Jet Propulsion Laboratory, California Institute of Technology
Guoxiang Tong, University of Notre Dame
Chi (April) Tran, University of Colorado Boulder
WEAK-FORM LATENT SPACE DYNAMICS IDENTIFICATION WITH UQ
Nathaniel Trask, University of Pennsylvania
A DATA-DRIVEN EXTERIOR CALCULUS FOR PROBABILISTIC DIGITAL TWINS
Noah Wade, Naval Research Laboratory
Dongbin Xiu, The Ohio State University 
DATA DRIVEN MODELING OF STOCHASTIC SYSTEMS
Hongkyu Yoon, Sandia National Laboratories
Ruda Zhang, University of Houston
STOCHASTIC SUBSPACE VIA PROBABILISTIC PCA TO CHARACTERIZE AND CORRECT MODEL ERROR