Schedule and Program Book

Here is the complete program book (PDF) (latest update: August 16) containing the schedule, all abstracts, session descriptions, and practical information about the conference. Please note that we are not planning to print a complete copy of the program book for each participant, but will provide at the very least a copy of the schedule in table format found in the program book. The online schedule below will continue to be updated if there are last-minute changes to the program.

All plenary and tutorial talks are in STC 1012. Parallel sessions listed below are respectively in STC 0020, 0040, 0050, 0060. The three sessions on Tuesday afternoon are in STC 0040, 0050, 0060.

Sunday August 18

14:15-15:45: Tutorial: Fred Hickernell: Quasi-Monte Carlo Methods: What, Why, and How? (PDF)

16:00-17:30: Tutorial: Peter Frazier: Grey-Box Bayesian Optimization

Monday August 19

9:00-10:00: Plenary Talk: David Krieg: Some recent approaches to sampling recovery

Monday 10h30-12h30: Parallel Sessions

14:00-15:00: Plenary Talk: Gersende Fort: Stochastic Approximation beyond the gradient case

Monday 15h30-17h30: Parallel Sessions

Tuesday August 20

9:00-10:00: Plenary Talk: Art Owen: Recent progress in error estimation for quasi-Monte Carlo

Tuesday 10h30-12h30: Parallel Sessions

14:00-15:00: Plenary Talk: Mariana Olvera-Cravioto: Simulation algorithms for branching recursions

Tuesday 15h30-17h30: Parallel Sessions

Wednesday August 21

9:00-10:00: Plenary Talk: Chris Oates: Richardson Extrapolation meets Multi-Fidelity Modelling

Wednesday 10h30-12h30: Parallel Sessions

14:00-15:00: Plenary Talk: Frances Kuo: Lattice rules, kernel methods, DNNs, and how to connect them

Wednesday 15h30-17h30: Parallel Sessions

Thursday August 22

9:00-10:00: Plenary Talk: Takashi Goda: Randomized lattice rules for high-dimensional integration

Thursday 10h30-12h30: Parallel Sessions

14:00-15:00: Plenary Talk: Henry Lam: Bootstrap with One (or Few) Resamples: Statistical Optimality and an Integrative View on Data and Monte Carlo Uncertainties

Thursday 15h30-17h30: Parallel Sessions

Friday August 23

9:00-10:00: Panel Discussion: 30 Years of MCQMC

Friday 10h30-12h30: Parallel Sessions

Sessions Description:

Stochastic Computation and Complexity, Part I: SDEs, Stochastic optimization and neural networks

Optimization under uncertainty

  • Philipp Guth: Quasi-Monte Carlo methods for optimal feedback control problems under uncertainty
  • Helmut Harbrecht: Shape optimization under constraints on the probability of a quadratic functional to exceed a given threshold
  • Fabio Musco: Deep learning methods for stochastic Galerkin approximations of random elliptic PDEs
  • Arved Bartuska: Randomized quasi-Monte Carlo for nested integration

Efficient Bayesian Surrogate Modeling - Part 1

  • Pieterjan Robbe: Efficient surrogate construction for response surfaces with steep gradients
  • Michael McCourt: Constraint active search as an alternative to multiobjective optimization
  • John Miller: Diverse Expected Improvement (DEI): Diverse Optimization of Expensive Black-box Simulators for Internal Combustion Engine Control

Technical Session 1

  • Leszek Plaskota: Adaptive quadratures work well even for piecewise smooth functions(?)
  • Ambrose Emmett-Iwaniw: Using Adaptive Basis Search Method To Interpret Black-Box Models
  • Yunfeng Xiong: Adaptive density estimation via discrepancy estimation, and its application in quantum many-body simulations

Stochastic Computation and Complexity, Part II: Approximation of SDEs under non-standard assumptions

  • Christopher Rauhögger: Milstein-type methods for strong approximation of systems of SDEs with a discontinuous drift coefficient
  • Łukasz Stepien: On efficient approximation of SDEs driven by countably dimensional Wiener process
  • Simon Ellinger: On optimal error rates for strong approximation of SDEs with a Hölder-continuous drift coefficient

Technical Session 2

  • Nathan Kirk: Message-Passing Monte Carlo: Generating low-discrepancy points sets via graph neural networks
  • Hozumi Morohosi: Searching good permutations for low-discrepancy sequences by mixed integer programming
  • Vishnupriya Anupindi: Column reduced digital nets
  • Victor Ostromoukhov: Construction of many irreducible Sobol’ (0,2)-sequences in base b>2

Efficient Bayesian Surrogate Modeling - Part 2

  • Aleksei Sorokin: Fast Gaussian Process Regression for Smooth Functions using Lattice and Digital Sequences with Matching Kernels
  • Vishwas Rao: Rare events and their optimization
  • Xun Huan: Bayesian Optimal Experimental Design for Surrogate Model Training

Variance reduction techniques for rare events

  • Eya Ben Amar: Importance Sampling Methods with Stochastic Differential Equations for the Estimation of the Right Tail of the CCDF of the Fade Duration
  • Shyam Mohan Subbiah Pillai: Importance sampling via stochastic optimal control for rare events associated with McKean-Vlasov equation
  • Romain Espoeys: Multilevel reliability analysis: application to a flood risk estimation

Stochastic Computation and Complexity, Part III: High dimensional approximation and integration

  • Stefan Heinrich: Integration and approximation of functions by Monte Carlo and quantum methods
  • Kateryna Pozharska: Sampling recovery and sharp norm estimates of projection operators
  • Nicolas Nagel: The L2-discrepancy of latin hypercubes

Efficient methods for uncertainty quantification in differential equations Part I

  • Sebastian Krumscheid: Nonparametric Inference for Diffusion Processes
  • Elliot Addy: History Matching and Gaussian Process Emulation in High Dimensions
  • Weiwen Mo: A Universal Lattice-based Algorithm for Multivariate Function Approximation in Uncertainty Quantification
  • Vesa Kaarnioja: Revisiting high-dimensional kernel approximation of parametric PDEs over lattice point sets

Recent advances in QMC methods for computational finance and Financial Risk management

  • Michael Samet: Quasi-Monte Carlo for Efficient Fourier Pricing of Multi-Asset Options
  • Sifan Liu: Conditional Quasi-Monte Carlo with Active Subspaces
  • Sergei Kucherenko: Application of Randomised QMC for Option Pricing and Greeks
  • Zhijian He: Estimating quantile and expected shortfall via Hilbert space-filling curve sampling with confidence intervals

Technical Session 3

  • Damir Ferizović: Uniform distribution via lattices: from point sets to sequences
  • Christian Weiss: Pair Correlations in the p-adic setting
  • Peter Kritzer: QMC and nonnegative local discrepancy

Efficient methods for uncertainty quantification in differential equations Part II

  • Laura Scarabosio: Bayesian shape inversion in acoustic and electromagnetic scattering
  • Andrea Barth: The Quasi Continuous-Level Monte Carlo Method and its Applications
  • Aretha Teckentrup: Multilevel Monte Carlo Methods with Smoothing

Learning to Solve Related Integrals

  • Francois-Xavier Briol: Estimating parametric expectations through Bayesian quadrature
  • Jon Cockayne: Learning to Solve Related Linear Systems
  • Zheyang Shen: Demystifying diffusion models via their Markov semigroups

Technical Session 5

  • Bjoern Sprungk: Metropolis-adjusted interacting particle sampling
  • Max Hird: Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo
  • Rocco Caprio: Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality
  • Hwanwoo Kim: Enhanced Gaussian Process Surrogates for Optimization and Sampling by Pure Exploration

Function recovery and discretization problems - Part 1

  • Ben Adcock: Optimal approximation of infinite-dimensional, Banach-valued, holomorphic functions from i.i.d. samples
  • Winfried Sickel: Haar decompositions and Besov-type spaces
  • Mathias Sonnleitner: Entropy numbers of finite-dimensional Lorentz space embeddings
  • Fabian Taubert: Learning the solution of differential equations by sparse high-dimensional approximation

Technical Session 6

  • Jiefei Yang: Gradient enhanced sparse Hermite polynomial expansions for pricing and hedging high-dimensional American options
  • Sanket Agrawal: Large sample limit theorems for the Zig-Zag process
  • Miika Kailas: Ergodicity of No U-Turn Samplers

Testing and analysis of pseudorandom number generators

  • Michael Mascagni: Machine Learning and Random Number Generation Testing
  • Pierre L'Ecuyer: A Redesigned C++ Library to Test the Lattice Structure of Linear Generators and Search for Good Ones
  • Meltem Sonmez Turan: On NIST's Standards on Random Numbers
  • Asaki Saito: Acceleration of true orbit pseudorandom number generators using Newton's method

Technical Session 7

  • Jun Xian: Random star discrepancy based on stratified sampling
  • Leon Wilkosz: Hierarchical and Quasi Monte Carlo Techniques for McKean-Vlasov Equations
  • Philippe Blondeel: Application of quasi-Monte Carlo in Mine Countermeasure Simulations with a Stochastic Optimal Control Framework
  • Charly Andral: Combining Normalizing Flows and Quasi-Monte Carlo

Function recovery and discretization problems - Part 2

  • Ayoub Belhadji: Function reconstruction using determinantal sampling
  • Thomas Jahn: Sampling numbers of smoothness classes via $\ell^1$-minimization
  • Tino Ullrich: Constructive Sparsification of Finite Frames and Applications to Function Recovery

Universality in QMC and related algorithms

  • Josef Dick: Explicit constructions of point sets whose worst-case error in certain spaces depends polynomially on the dimension
  • Fred Hickernell: Quasi-Monte Carlo Kernel Density Estimation
  • Kosuke Suzuki: A universal median quasi-Monte Carlo integration
  • Laurence Wilkes: Using Kronecker point sets for function approximation in the Korobov space

Technical Session 8

  • Michał Sobieraj: On randomized Euler scheme for SDEs with drift in integral form and its connection with SGD
  • Kristin Kirchner: Monte Carlo convergence rates for moments in Banach spaces
  • Silei Song: WoSNN: an Effective Stochastic Solver for Elliptic Partial Differential Equations (PDEs) with Machine Learning

Technical Session 9

  • Alexander Keller: Integro-Approximation with Neural Integral Operators
  • Florian Maire: The Occluded Process
  • Matthew Li: Certifiable Low-Dimensional Structure in Bayesian Inference via Dimensional Logarithmic Sobolev and Poincaré Inequalities
  • Alessandro Mastrototaro: Online Variational Sequential Monte Carlo

Multilevel methods for SDEs and SPDEs

  • Anastasia Istratuca: Multilevel Monte Carlo Methods for Chaotic Dynamical Systems
  • Håkon Hoel: Multiindex Monte Carlo for semilinear stochastic partial differential equations
  • Emil Løvbak: Multilevel Monte Carlo for kinetic particle models
  • Josef Martínek: Mixed Precision Multilevel Monte Carlo Method

Recent Advances in Monte Carlo Methods for Forward and Inverse Problems for Stochastic Reaction Networks - Part 1

  • Hye-Won Kang: Chemical reaction networks with stochastic switching behavior and machine learning applications
  • Sophia Wiechert: Dimension Reduction via Markovian Projection for Stochastic Reaction Networks
  • Frank Meulen: Guided simulation of conditioned chemical reaction networks

Kernel approximation and cubature - Part 1 of 2

  • Ian Sloan: High Dimensional Approximation -- Making life easy with kernels
  • Robert Gruhlke: Quasi-Monte Carlo meets kernel cubature
  • Chris Oates: Sampling with Stein Discrepancies
  • Ilja Klebanov: Enhanced Lattice-Based Kernel Cubature through Weight Optimization

Technical Session 10

  • Joonha Park: Sampling high-dimensional, multimodal distributions using adaptively-tuned, tempered Hamiltonian Monte Carlo
  • Jiarui Du: Unbiased Markov chain quasi-Monte Carlo for Gibbs samplers
  • Shin Harase: Markov chain quasi-Monte Carlo simulation using linear feedback shift register generators
  • Ilhem Bouderbala: Mapping spatially varying diffusion using Gibbs-Hamiltonian Monte Carlo algorithm.

Multilevel methods for function approximation

  • Fabio Nobile: Multilevel Active Subspaces for High Dimensional Function Approximation
  • Filippo De Angelis: Multilevel function approximation I: meta-theorems and PDE analysis
  • Michael Giles: Multilevel function approximation II: SDE analysis

Recent Advances in Monte Carlo Methods for Forward and Inverse Problems for Stochastic Reaction Networks - Part 2

  • Muruhan Rathinam: Stochastic Filtering of Partially Observed Reaction Networks
  • Chiheb Ben Hammouda: Dimensionality Reduction via Markovian Projection in Filtering for Stochastic Reaction Networks: Bridging Accuracy and Efficiency
  • Ankit Gupta: Spectral Estimation of the Koopman operator for Stochastic Reaction Networks

Kernel approximation and cubature - Part 2 of 2

  • Dirk Nuyens: A comparison of lattice based kernel and truncated least squares approximations
  • Abirami Srikumar: Approximating distribution functions in uncertainty quantification using quasi-Monte Carlo methods
  • Laura Bazahica: Quasi-Monte Carlo for Electrical Impedance Tomography
  • André-Alexander Zepernick: Quasi-Monte Carlo Methods for PDEs on Randomly Moving Domains

Technical Session 11

  • Astrid Herremans: Sampling theory for regularized least squares approximations
  • Hassan Maatouk: Sampling multivariate normal distributions under nonlinear constraints
  • Xiaotian Zhu: A critical analysis of the Weighted Least Squares Monte Carlo method for pricing American options

MCMC: Convergence and Robustness

  • Michael Choi: Geometric unification of central MCMC algorithms via rate distortion theory and factorizability of multivariate Markov chains
  • Federica Milinanni: A large deviation principle for Metropolis-Hastings sampling
  • Ning Ning: On the Convergence of MCMCs with Quantum Speedup
  • Cameron Bell: Adapting the Stereographic Bouncy Particle Sampler

Continuous-time dynamics in Monte Carlo and beyond

  • Jonas Latz: Losing momentum in continuous-time stochastic optimisation
  • Alexandre Bouchard-Cote: How to choose an annealing algorithm
  • Svetlana Dubinkina: Projected ensemble data assimilation

Function spaces and algorithms for high-dimensional problems

  • Michael Gnewuch: Function space embeddings for non-tensor product spaces and application to high-dimensional approximation
  • Laura Weidensager: ANOVA-boosting for high-dimensional approximation
  • Robin Rüßmann: Tractability results for integration on Gaussian spaces

Technical Session 12

  • Miguel Alvarez: Unbiased and Multilevel Methods for a Class of Diffusions Partially Observed via Marked Point Processes
  • Chuntao Chen: Multilevel optimization-based sampling for large-scale inverse problems
  • Arne Bouillon: Single-ensemble multilevel Monte Carlo for discrete interacting-particle methods