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Information and Statistics for Nuclear Experiment and Theory workshop (ISNET-9)

May 22 – 26, 2023
Washington University in St. Louis
US/Central timezone

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Contribution List

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  1. 54. Welcome and Intro
    Maria Piarulli (Washington University)
    5/22/23, 9:00 AM
  2. 53. Gaussian Process emulation using surmise
    Moses Chan (Northwestern), Özge Surer (Miami University)
    5/22/23, 9:05 AM
  3. 55. Reduced Basis Methods for emulation and the ROSE software package
    Daniel Odell (Ohio), Pablo Giuliani (MSU/FRIB)
    5/22/23, 11:15 AM
  4. 56. Bayesian Model Mixing using Taweret
    Alexandra Semposki (Ohio University), John Yannotty (Ohio State), Matt Pratola (Ohio State)
    5/22/23, 2:15 PM
  5. 57. The Bayesian Mass Explorer, BMEX
    Kyle Godbey (MSU/FRIB)
    5/22/23, 4:15 PM
  6. 58. Experimental Design and the BAND software framework
    Dick Furnstahl (Ohio State), Witek Nazarewicz (MSU/FRIB)
    5/22/23, 4:55 PM
  7. 8. Recent tools and developments in Bayesian statistics
    Yuling Yao (Flatiron Institute)
    5/23/23, 9:00 AM
  8. 9. A statistical exploration of CEMP star classification with s-process models
    Andrés Yagüe López (Los Alamos National Lab)
    5/23/23, 9:30 AM
  9. 10. Global fits and Bayesian inference in "Beyond the Standard Model" physics (virtual)
    Anders Kvellestad (University of Oslo)
    5/23/23, 10:00 AM
  10. 14. Applications of novel chiral interactions to quantum Monte Carlo methods and astrophysical data analysis
    Rahul Somasundaram (Syracuse University)
    5/23/23, 11:00 AM
  11. 30. Sequential Bayesian experimental design for calibration of expensive physics models
    Ozge Sürer
    5/23/23, 11:30 AM
  12. 11. History matching for nuclear ab initio calculations
    Christian Forssén (Durham University)
    5/23/23, 12:00 PM
  13. 13. Bayesian probability updates using sampling/importance resampling: applications in nuclear theory
    Weiguang Jiang (Chalmers University)
    5/23/23, 1:30 PM
  14. 42. Hamiltonian Monte Carlo computation in spatial statistics
    Debashis Mondal (WashU)
    5/23/23, 2:00 PM
  15. 38. Bayesian model calibration for nuclear decays with the Skyrme finite-amplitude method
    Tong Li (LLNL)
    5/23/23, 2:30 PM
  16. 12. Accounting for material and experimental variability using a random effects Bayesian inferential framework
    Denielle Ricciardi (Sandia National Laboratories)
    5/23/23, 3:30 PM
  17. 39. Short talk: Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions
    Dananjaya Liyanage
    5/23/23, 4:00 PM
  18. 40. Short Talk: How Uncertain Am I? Theoretical errors in Bayesian model calibration for EFTs
    Jason Bub
    5/23/23, 4:15 PM
  19. 61. Prof. John W. Clark
    Henric Krawczynski
    5/24/23, 8:55 AM
  20. 15. John Clark: Physicist at the computational frontier (virtual)
    Henrik Bohr
    5/24/23, 9:00 AM
  21. 16. Machine learning for nuclear physics
    Witek Nazarewicz (MSU/FRIB)
    5/24/23, 9:45 AM
  22. 60. Machine learning of nuclear properties
: a brief tribute to Prof. John Walter Clark (recording)
    Eirene Mavrommatis
    5/24/23, 11:00 AM
  23. 47. Surrogate models of nuclear density functional theory with gaussian processes and autoencoders
    Marc Verriere
    5/24/23, 11:15 AM
  24. 45. Machine learning for heavy-ion accelerators (virtual)
    Yue Hao (Michigan State University)
    5/24/23, 11:45 AM
  25. 17. Nuclear masses learned from a probabilistic neural network
    Amy Lovell (Los Alamos National Lab)
    5/24/23, 1:15 PM
  26. 18. Machine learning for the many-body problem
    Alessandro Lovato (Argonne National laboratory)
    5/24/23, 1:45 PM
  27. 19. Mapping out the thermodynamic stability of a QCD EOS with a critical point using active learning
    Debora Mroczek (University of Illinois)
    5/24/23, 2:15 PM
  28. 46. Predicting nuclear masses with product-unit networks (virtual)
    Babette Dellen
    5/24/23, 3:15 PM
  29. 25. Machine learning for Deeply Virtual Compton Scattering (virtual)
    Manal Almaeen
    5/24/23, 3:45 PM
  30. 23. Short Talk: Deep learning pairing correlations from neural-network quantum states
    Jane Kim
    5/24/23, 4:15 PM
  31. 26. Overview of emulators for nuclear physics
    Dick Furnstahl
    5/25/23, 9:00 AM
  32. 34. Known Boundary Emulation (virtual)
    Ian Vernon
    5/25/23, 9:30 AM
  33. 28. Gaussian process regression constrained by boundary value problems
    Mamikon Gulian (Sandia National Laboratories)
    5/25/23, 10:00 AM
  34. 29. Multi-output gaussian processes for inverse uncertainty quantification in neutron noise analysis (virtual)
    Paul Lartaud
    5/25/23, 11:00 AM
  35. 27. Quantification for a covariant energy density functional emulated by the reduced basis method
    Pablo Giuliani (Michigan State University)
    5/25/23, 11:30 AM
  36. 44. Hamiltonian Monte Carlo & eigenvector continuation for ab initio nuclear physics
    Andreas Ekström (Chalmers University of Technology)
    5/25/23, 12:00 PM
  37. 31. Eigenvector continuation emulators for the ab initio symmetry-adapted framework
    Kevin Becker
    5/25/23, 1:30 PM
  38. 32. Bootstrap for multivariate time series and gravitational wave detection
    Soumen Lahiri (Washington University)
    5/25/23, 2:00 PM
  39. 33. Data integration using constrained Gaussian process models with applications to nuclear physics
    Shuang Zhou (Arizona State University)
    5/25/23, 2:30 PM
  40. 35. Short Talk: Potential energy surface emulation and impact on fission trajectories
    Daniel Lay
    5/25/23, 3:30 PM
  41. 36. Short Talk: Ex fissio ad astra: extending optical models to the fission fragment region
    Kyle Beyer
    5/25/23, 3:45 PM
  42. 37. Short Talk: Reduced Basis Methods and Scattering
    Daniel Odell
    5/25/23, 4:00 PM
  43. 3. AI/ML+data science tools for detector design at the Electron Ion Collider (virtual)
    Cristiano Fanelli (William & Mary)
    5/26/23, 9:00 AM
  44. 4. Excavating insights from sparse data with statistics and machine learning
    Kyle Godbey
    5/26/23, 9:30 AM
  45. 5. Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities
    Marcus Noack (Lawrence Berkeley National Laboratory)
    5/26/23, 10:00 AM
  46. 6. Deep learning techniques in ground-based imaging gamma-ray observatories (virtual)
    Daniel Nieto (Madrid University)
    5/26/23, 11:00 AM
  47. 49. Accounting for Material and Experimental Variability Using a Random Effects Bayesian Inferential Framework
    Denielle Ricciardi
  48. 48. Bayesian parameter estimation of QGP viscosities in partial chemical equilibrium
    Andrew Gordeev
  49. 50. Gaussian processes for the nuclear equation of state
    Hannah Göttling
  50. 59. Machine Learning of Nuclear Properties
: A Brief Tribute to Prof. John Walter Clark
    Eirene Mavrommatis
  51. 51. Nuclear Binding energy prediction using Deep Learning on AME2020 dataset
    Ramesh Bhandari
  52. 24. Short Talk
    Yehu Chen
  53. 2. Statistical tools and ML for accelerator design
  54. 7. TDB
  55. 20. TDB
  56. 21. TDB
  57. 22. TDB
  58. 41. TDB
  59. 43. TDB
  60. 52. Variational Bayes Computer Models
    Mookyong Son
  61. 1. Welcome
    Brad Joliff (WashU)