Saifuddin Syed

Department of Statistics

University of Oxford

Biography

I am a Florence Nightingale Bicentennial Fellow in computational statistics and machine learning at the University of Oxford’s Department of Statistics, where I am also a member of the Algorithms and Inference Working Group for the Next Generation Event Horizon Telescope (ngEHT). Prior to this role, I completed a postdoc supervised by Arnaud Doucet and a PhD supervised by Alexandre Bouchard-Côté.

My research involves developing robust and scalable algorithms for statistical inference and generative modelling, with scientific applications in mind. My research has received numerous national and internation awards including the Pierre Robillard Award , the Cecil Graham Doctoral Dissertation Award , and the honourable mention of the Savage Award for Bayesian theory and methods.

If you have a cool, computationally challenging problem reach out!

Interests

Parallel tempering
Annealing algorithms
Scalable Bayesian inference
Generative modelling
AI for Science

Education

PhD in Statistics, 2022

University of British Columbia
MSc in Mathematics, 2016

University of British Columbia
BMath in Pure & Applied Mathematics, 2014

University of Waterloo

Research

Many of the state-of-the-art algorithms in statistics, and machine learning, utilize a technique called annealing, which involves making inferences from an intractable target problem by incrementally deforming solutions from a tractable reference problem. I’m am interested in using annealing as a tool to understand the interplay between MCMC, SMC, variational inference, diffusion models, normalizing flows, and optimal transport.

Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté (2023). autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. Arxiv Preprint.

PDF Cite Arxiv

Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté (2023). Pigeons.jl: Distributed sampling from intractable distributions. Arxiv Preprint.

PDF Cite Arxiv

Trevor Campbell, Saifuddin Syed, Chiao-Yu Yang, Michael I. Jordan, Tamara Broderick (2023). Local Exchangeability. Bernoulli.

PDF Cite Source Document Arxiv

Christopher Williams, Fabian Falck, George Deligiannidis, Chris Holmes, Arnaud Doucet, Saifuddin Syed (2023). A Unified Framework for U-Net Design and Analysis. Arxiv Preprint.

PDF Cite Arxiv

Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté (2022). Parallel tempering with a variational reference. Conference on Neural Information Processing Systems.

PDF Cite Poster Source Document Arxiv

Saifuddin Syed (2022). Doctoral thesis: Non-reversible parallel tempering on optimized paths. University of British Columbia.

PDF Cite Slides Video Source Document

Saifuddin Syed, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet (2021). Non-reversible parallel tempering: a scalable highly parallel MCMC scheme. Journal of the Royal Statistical Society (Series B).

PDF Cite Poster Video Source Document Arxiv

Vittorio Romaniello, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté (2021). Parallel tempering on optimized paths. International Conference on Machine Learning.

PDF Cite Poster Slides Video Source Document Arxiv

Notable Applications

Here are some recent examples of large-scale projects that used non-reversible parallel tempering (NRPT) as the backbone of their inference engine. Please reach out if you would like to use NRPT for your projects and have any questions. If you want to play around with NRPT check out our Julia package Pigeons.jl .

Model genetic variations in malaria

NRPT was used as an inference engine for MOIRE to model genetic variations in Malaria.