Saifuddin Syed

University of British Columbia

My research is generally focused on computational methods for Bayesian inference and machine learning. My most recent project involves analysing the optimality of a class of Monte Carlo algorithms called parallel tempering (PT) or replica exchange models (REM). PT was developed by physicists in the 80's to efficiently simulate from spin-glasses. It was later adapted as a general framework to improve mixing of MCMC algorithms sampling from complicated multimodal distributions by exploiting parallel computing architectures. PT is still considered to be the state-of-the-art technique in the toolbox of computational scientists, statisticians, and data scientists.

I am currently analysing the scaling properties of reversible and non-reversible variants of PT and using this to detmine their efficiency. We have established tight upper bounds on the optimal performance of these algorithms, as well as give a black-box approach to hyper-parameter tuning.

We are currently in the process of submitting a manuscript outlining this work. If you are inerested in learning more, please feel free to contact me!

Masters Thesis

My masters research was in probability theory and stochastic analysis. In particular, I studied a class of stochastic partial differential equations that arise when studying limiting behaviour of evolutionary systems with a spatial interactions.


I wrote some expository papers meant as projects for various graduate courses I have taken. I will post them here in the hopes that someone might find them interesting.


Here are notes from some of the presentations I have given in the last few years.