Sanket Agrawal, postdoc
Sanket Agrawal was employed as a postdoc on 1 January 2026 at the department’s section for Statistics and Probability Theory.
Sanket’s research lies at the intersection of probability, statistics, and computation, with a focus on the theoretical foundations of Markov chain Monte Carlo methods for Bayesian computation.
He draws on tools from the theory of Markov processes, high-dimensional statistics, and Bayesian asymptotics to study the stability of these methods in high-dimensional and big-data regimes.
Before joining UCPH, Sanket completed his PhD in Statistics at the University of Warwick (UK) under the supervision of Gareth Roberts. His thesis focused on the provable scalability of sampling methods based on piecewise deterministic Markov processes in Bayesian inference for large datasets.
Sanket is originally from India, where he completed his bachelor’s and master’s degrees in Statistics.
At UCPH, Sanket will work with Jun Yang on Yang’s Sapere Aude-funded project, “Geometry-Aware Monte Carlo Sampling (GAMeS)”, developing theory and methodology for scalable Monte Carlo.
Sanket loves taking breaks. Feel free to drop by his office 04.2.11 for a chat.