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 under the guidance of Arnaud Doucet and a PhD under the supervision of Alexandre Bouchard-Côté. My doctoral thesis won the Pierre Robillard Award from the Statistical Society of Canada (SSC), the Cecil Graham Doctoral Dissertation Award from the Canadian Applied and Industrial Mathematics Society (CAIMS), and the Savage Award (Honourable Mention) for theory and methods from the International Society for Bayesian Analysis (ISBA).

My research involves designing scalable and robust algorithms for Bayesian inference with scientific applications in mind. If you have a cool, computationally challenging problem reach out!

Interests

Annealing
Parallel Tempering
Markov Chain Monte Carlo
Sequential Monte Carlo
Scalable Bayesian inference
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.

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

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

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

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

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

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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).

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

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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 .

Discover gerrymandering in Georgia's 2021 congressional districting plan

Georgia Tech used NRPT to statistically identify gerrymandering in Georgia’s 2021 congressional districting plan.