Interaction of Excited States in Two-Species Bose-Einstein Condensates: A Case Study. - Todd Kapitula; Kody J. H. Law; Panayotis G. Kevrekidis (SIAM J. Appl. Dyn. Syst., 2010)

Evaluating Data Assimilation Algorithms - Kody J. H. Law; Andrew M. Stuart (CoRR, 2011)

Proposals which speed up function-space MCMC. - Kody J. H. Law (J. Comput. Appl. Math., 2014)

Dimension-independent likelihood-informed MCMC. - Tiangang Cui; Kody J. H. Law; Youssef M. Marzouk (J. Comput. Phys., 2016)

Multilevel ensemble Kalman filtering. - Håkon Hoel; Kody J. H. Law; Raúl Tempone (SIAM J. Numer. Anal., 2016)

Accelerated Dimension-Independent Adaptive Metropolis. - Yuxin Chen; David E. Keyes; Kody J. H. Law; Hatem Ltaief (SIAM J. Sci. Comput., 2016)

Deterministic Mean-Field Ensemble Kalman Filtering. - Kody J. H. Law; Hamidou Tembine; Raúl Tempone (SIAM J. Sci. Comput., 2016)

Multilevel Particle Filters. - Ajay Jasra; Kengo Kamatani; Kody J. H. Law; Yan Zhou (SIAM J. Numer. Anal., 2017)

Multilevel Sequential Monte Carlo Samplers for Normalizing Constants. - Pierre Del Moral; Ajay Jasra; Kody J. H. Law; Yan Zhou (ACM Trans. Model. Comput. Simul., 2017)

Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals. - Alexandros Beskos; Ajay Jasra; Kody J. H. Law; Youssef M. Marzouk; Yan Zhou (SIAM/ASA J. Uncertain. Quantification, 2018)

Accuracy of Some Approximate Gaussian Filters for the Navier-Stokes Equation in the Presence of Model Error. - Michal Branicki; Andrew J. Majda; Kody J. H. Law (Multiscale Model. Simul., 2018)

Bayesian Static Parameter Estimation for Partially Observed Diffusions via Multilevel Monte Carlo. - Ajay Jasra; Kengo Kamatani; Kody J. H. Law; Yan Zhou (SIAM J. Sci. Comput., 2018)

Cluster, Classify, Regress: A General Method For Learning Discountinous Functions. - David E. Bernholdt; Mark R. Cianciosa; Clement Etienam; David L. Green; Kody J. H. Law; Jin Myung Park (CoRR, 2019)

Multilevel particle filters for Lévy-driven stochastic differential equations. - Ajay Jasra; Kody J. H. Law; Prince Peprah Osei (Stat. Comput., 2019)

Correction to: Multilevel particle filters for Lévy-driven stochastic differential equations. - Ajay Jasra; Kody J. H. Law; Prince Peprah Osei (Stat. Comput., 2019)

Unbiased Filtering of a Class of Partially Observed Diffusions. - Ajay Jasra; Kody J. H. Law; Fangyuan Yu (CoRR, 2020)

Ultra-fast Deep Mixtures of Gaussian Process Experts. - Clement Etienam; Kody J. H. Law; Sara Wade (CoRR, 2020)

Randomized Multilevel Monte Carlo for Embarrassingly Parallel Inference. - Ajay Jasra; Kody J. H. Law; Alexander Tarakanov; Fangyuan Yu (SMC, 2021)

Randomized multilevel Monte Carlo for embarrassingly parallel inference. - Ajay Jasra; Kody J. H. Law; Fangyuan Yu (CoRR, 2021)

Materials Fingerprinting Classification. - Adam Spannaus; Kody J. H. Law; Piotr Luszczek; Farzana Nasrin; Cassie Putman Micucci; Peter K. Liaw; Louis Joseph Santodonato; David J. Keffer; Vasileios Maroulas (Comput. Phys. Commun., 2021)

Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions. - Neil K. Chada; Jordan Franks; Ajay Jasra; Kody J. H. Law; Matti Vihola (SIAM/ASA J. Uncertain. Quantification, 2021)

Multilevel ensemble Kalman filtering for spatio-temporal processes. - Alexey Chernov; Håkon Hoel; Kody J. H. Law; Fabio Nobile; Raúl Tempone (Numerische Mathematik, 2021)

Unbiased estimation of the gradient of the log-likelihood in inverse problems. - Ajay Jasra; Kody J. H. Law; Deng Lu (Stat. Comput., 2021)

Multilevel Bayesian Deep Neural Networks. - Neil K. Chada; Ajay Jasra; Kody J. H. Law; Sumeetpal S. Singh (CoRR, 2022)

Digital Fingerprinting of Microstructures. - Michael D. White; Alexander Tarakanov; Christopher P. Race; Philip J. Withers; Kody J. H. Law (CoRR, 2022)

Convergence Rates for Stochastic Approximation on a Boundary. - Kody J. H. Law; Neil S. Walton; Shangda Yang (CoRR, 2022)

Mixtures of Gaussian Process Experts with SMC2. - Teemu Härkönen; Sara Wade; Kody J. H. Law; Lassi Roininen (CoRR, 2022)

Certified dimension reduction in nonlinear Bayesian inverse problems. - Olivier Zahm; Tiangang Cui; Kody J. H. Law; Alessio Spantini; Youssef M. Marzouk (Math. Comput., 2022)

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security. - Joseph M. Lukens; Ali Passian; Srikanth B. Yoginath; Kody J. H. Law; Joel A. Dawson (Sensors, 2022)

Sparse Online Variational Bayesian Regression. - Kody J. H. Law; Vitaly Zankin (SIAM/ASA J. Uncertain. Quantification, 2022)

On Unbiased Estimation for Discretized Models. - Jeremy Heng; Ajay Jasra; Kody J. H. Law; Alexander Tarakanov (SIAM/ASA J. Uncertain. Quantification, 2023)

A randomized multi-index sequential Monte Carlo method. - Xinzhu Liang; Shangda Yang; Simon L. Cotter; Kody J. H. Law (Stat. Comput., 2023)

Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. - Shangda Yang; Vitaly Zankin; Maximilian Balandat; Stefan Scherer; Kevin T. Carlberg; Neil Walton; Kody J. H. Law (ICML, 2024)

SMC Is All You Need: Parallel Strong Scaling. - Xinzhu Liang; Sanjaya Lohani; Joseph M. Lukens; Brian T. Kirby; Thomas A. Searles; Kody J. H. Law (CoRR, 2024)

Fast deep mixtures of Gaussian process experts. - Clement Etienam; Kody J. H. Law; Sara Wade; Vitaly Zankin (Mach. Learn., 2024)

Multi-index Sequential Monte Carlo Ratio Estimators for Bayesian Inverse problems. - Ajay Jasra; Kody J. H. Law; Neil Walton; Shangda Yang (Found. Comput. Math., 2024)

Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles. - Xinzhu Liang; Joseph M. Lukens; Sanjaya Lohani; Brian T. Kirby; Thomas A. Searles; Xin Qiu; Kody J. H. Law (CoRR, 2025)