I acknowledge past and current financial support from the National Science Foundation (DMS and MMS programs), the University of Michigan Rackham Grant (2007-2008), and from the Canadian Natural Sciences and Engineering Research Council (2002-2006).

Unbiased Markov Chain Monte Carlo with couplings. JRSS-B with discussion (to appear). Joint with Pierre Jacob, and John O'Leary.
Markov Chain Monte Carlo Confidence Intervals. (File in pdf). Bernoulli 22 (3), 1808-1838, (2016).
Kernel Estimators of asymptotic variance for adaptive Markov Chain Monte Carlo (file in pdf, with a supplement paper). Annals of Statistics 39, No 2, 990-1011 (2011).
Limit Theorems for some adaptive MCMC algorithms with sub-geometric kernels. With Gersende Fort. (file in pdf). Bernoulli 16 (2010), 116-154.
On adaptive Markov chain Monte Carlo algorithms, with Jeff Rosenthal, Bernoulli 11 (2005), 815-828. (file in pdf).

HIGH-DIMENSIONAL BAYESIAN ASYMPTOTICS
- An approach to large-scale quasi-Bayesian inference with spike-and-slab prior . Joint with Anwesha Bhattacharyya.
- A scalable quasi-Bayesian framework for Gaussian graphical models. (File in pdf). A Matlab implementation is available here.
- On the contraction properties of some high-dimensional quasi-posterior distributions. (File in pdf). Annals of Statistics 45, 2248-2273 (2017).

STATE SPACE MODELS
- Inference for dynamic and latent variable models via iterated, perturbed Bayes maps. With E. Ionides, D. Nguyen, S. Stoev, and A. King. PNAS 112 (3) 719-724 (2015).
- Iterated Filtering. With Edward Ionides, Anindya Bhadra and Aaron King. Annals of Statistics, Vol. 39, 1776-1802 (2011).

STOCHASTIC OPTIMIZATION
- Scalable Computation of Regularized Precision Matrices via Stochastic Optimization. (File in pdf). Joint with Rahul Mazumder, and Jie Chen.
- On perturbed Proximal Gradient Algorithms, joint with Eric Moulines, and Gersende Fort. JMLR 18, 1-33 (2017).

BOSTON UNIVERSITY