Dr Michael Smith

MScs, PhD

School of Computer Science


Outreach, Open Days and Headstart Officer

Member of the Machine Learning research group

+44 114 222 1800

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Dr Michael Smith
School of Computer Science
Regent Court (DCS)
211 Portobello
S1 4DP

Dr Michael Smith studied Computer Science at Warwick university, then, after a few years outside academia, joined Edinburgh to take MScs in Informatics and Neuroinformatics and a PhD in computational neuroscience, looking at where self-motion cues are processed and integrating, in the human brain (using fMRI).

After a bit of travelling he went to Kampala (Uganda) to lecture (in 2014) teaching AI to students at Makerere.

He is now a Research Fellow at the University of Sheffield in the department of Computer Science in the Machine Learning group. His work encompasses Differential Privacy and its applications to Gaussian process (GP) regression and classification, bounds on attacks to GP classifiers by adversarial examples, a kernel for regression over integrals and a method for tracking bees using retroreflective tags.

His work is in particular now focused on modelling air pollution in Kampala, using data from a network of low-cost sensors.

He is currently investigating probabilistically handling the calibration of the sensors using mobile units. This system will soon be incorporated into a pipeline providing live predictions for policy makers and stakeholders in the city.

Research interests
  • Gaussian Processes
  • Air pollution
  • Differential Privacy
  • Machine Learning for International Development
  • Bumblebee tracking
  • Adversarial Examples/bounds using Gaussian Processes

Journal articles

Conference proceedings papers

  • McDonald TM, Ross M, Smith MT & Álvarez MA (2023) Nonparametric Gaussian Process Covariances via Multidimensional Convolutions. Proceedings of Machine Learning Research, Vol. 206 (pp 8279-8293) RIS download Bibtex download
  • Gahungu P, Christopher L, Alvarez M, Bainomugisha E, Smith M & Wilkinson R (2022) Adjoint-aided inference of Gaussian process driven differential equations. Conference on Neural Information Processing Systems, 29 November 2022 - 1 December 2022. RIS download Bibtex download
  • Grosse K, Smith MT & Backes M (2021) Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks. 2020 25th International Conference on Pattern Recognition (ICPR), 10 January 2021 - 15 January 2021. RIS download Bibtex download
  • Yousefi F, Smith MT & Álvarez MA (2019) Multi-task Learning for Aggregated Data using Gaussian Processes.. NeurIPS (pp 15050-15060) RIS download Bibtex download
  • Yousefi F, Smith MT & Alvarez Lopez M () Multi-task Learning for Aggregated Data using Gaussian Processes. Proceedings of the Conference on Advances in Neural Information Processing Systems 32 (2019) View this article in WRRO RIS download Bibtex download
  • Smith M, Alvarez Lopez MA, Zwiessele M & Lawrence N () Differentially Private Regression with Gaussian processes. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Vol. 84 (pp 1195-1203). Playa Blanca, Lanzarote, Canary Islands, 9 April 2018 - 11 April 2018. View this article in WRRO RIS download Bibtex download

Working papers



Pollinator: Using Data Driven Artificial Intelligence to Reveal Pesticide Induced Changes in Pollinator Behaviour, BBSRC, 02/2024 - 08/2025, £321,811, as PI