Decoupling for Markov Chains
How can we rigorously quantify Monte Carlo error and assess convergence in modern MCMC methods such as the No-U-Turn Sampler? This question motivates new joint work with Victor de la … Read More
Blog
Stochastic Computation Workshop at FoCM 2026 – Vienna, Austria
📅 Dates: July 13–15, 2026 📍 Location: Vienna, Austria 🔗 Workshop Information 🎲 I’m excited to be co-organizing the Stochastic Computation Workshop at FoCM 2026, alongside Mireille Bossy and David … Read More
WALNUTS = Within-orbit Adaptive Leapfrog No-U-Turn Sampler
In many Bayesian inference problems, the geometry of the posterior distribution can vary dramatically in scale. A classic example is Neal’s funnel, where the state-of-the-art algorithm, the No-U-Turn Sampler (NUTS), … Read More
A Locally Adaptive, Gradient-Free MCMC Method Inspired by the No-U-Turn Sampler
Markov Chain Monte Carlo (MCMC) methods are fundamental for sampling from complex probability distributions, but many widely used algorithms either rely on gradients (like NUTS) and/or struggle with high-dimensional, multi-scale … Read More
Why Go Coordinate-Free in Monte Carlo and Optimization?
Traditional methods like Gibbs sampling or randomized Kaczmarz rely heavily on specific coordinate systems, which can limit their efficiency—especially in ill-conditioned settings. But what happens when we step away from … Read More
Watch My Latest Talk on the No-U-Turn Sampler
Curious about the No-U-Turn Sampler and its performance in high-dimensional spaces? Watch my recent talk, where I present new insights into its reversibility and mixing time. There are still many … Read More
Online Seminar on Monte Carlo
I’m thrilled to announce that I’ll be presenting at the Online Seminar on Monte Carlo on October 22 from 11:30 AM – 12:30 PM EST! I’ll be discussing recent work … Read More
Mixing of the No-U-Turn Sampler and the Geometry of Gaussian Concentration
The No-U-Turn Sampler (NUTS) is the go-to method for sampling in probabilistic programming languages like Stan, PyMC3, NIMBLE, Turing, and NumPyro. However, due to its recursive architecture, even proving its … Read More
Penn/Temple Probability Seminar October 1st
Excited to share that I’ll be speaking at the Penn/Temple probability seminar on October 1st. Looking forward to discussing the recent progress we’ve made in understanding the reversibility of the … Read More
Recent Advances and Future Directions for Sampling Conference at Yale
Excited to attend the upcoming Recent Advances and Future Directions for Sampling conference at Yale. With so much progress happening in MCMC and related areas, this event feels perfectly timed … Read More