Dr Michael Smith

MScs, PhD

School of Computer Science

Senior Lecturer

Outreach Lead

Member of the Machine Learning research group

Mike Smith
Profile picture of Mike Smith
m.t.smith@sheffield.ac.uk
Regent Court (CS)

Full contact details

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

Dr Michael Smith currently works at the intersection of (flying) insect behaviour ecology and computer science, in particular on the development of methods to track their location and behaviour in the landscape.

This depends on the development of tags and machine learning techniques for efficient sampling and handling of the sparse data the tags collect, as the bee flys across the landscape.

Previous work focused on the probabilistically handling calibration of air pollution sensors using mobile PM2.5 sensors (transported by motorbike taxis in Kampala), and the problem of source-inference in the resulting datasets. He also worked in the field of Differential Privacy and its applications to Gaussian process (GP) regression and classification and developed an approach to bound all future attacks on GP classifiers within the framework of adversarial examples.

He studied his undergraduate in Computer Science at Warwick university, then in Edinburgh completed his masters 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). He next went to Kampala (Uganda) to lecture (in 2014) teaching AI to students at Makerere, and is now a senior lecturer at the 爆料TV in the School of Computer Science in the Machine Learning group.

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

Journal articles

  • Yao M, Smith M & Peng C (2025) . Urban Forestry & Urban Greening, 105.
  • Chapman KE, Smith MT, Gaston KJ & Hempel de Ibarra N (2024) . Biology Letters, 20(4).
  • Smith MT, Ross M, Ssematimba J, 脕lvarez MA, Bainomugisha E & Wilkinson R (2023) . Journal of the Royal Statistical Society Series C: Applied Statistics, 72(5), 1187-1209.
  • Smith MT, Grosse K, Backes M & 脕lvarez MA (2023) . Machine Learning, 112(3), 971-1009.
  • Smith MT, Livingstone M & Comont R (2021) . Methods in Ecology and Evolution, 12(11), 2184-2195.
  • Smith MT, Alvarez MA & Lawrence ND (2021) Differentially private regression and classification with sparse Gaussian processes. Journal of Machine Learning Research, 22.
  • Fotheringham J, Smith MT, Froissart M, Kronenberg F, Stenvinkel P, Floege J, Eckardt K-U & Wheeler DC (2020) . BMC Nephrology, 21(1).
  • Smith MT, Zwiessele M & Lawrence ND (2016) Differentially Private Gaussian Processes.. CoRR, abs/1606.00720.
  • Bett D, Stevenson CH, Shires KL, Smith MT, Martin SJ, Dudchenko PA & Wood ER (2013) . The Journal of Neuroscience, 33(16), 6928-6943.
  • Feldwisch-Drentrup H, Barrett AB, Smith MT & van Rossum MCW (2012) . Journal of Neuroscience Methods, 210(1), 15-21.
  • Wutte M () . Frontiers in Psychology, 2.

Book chapters

  • Yao M, Smith M & Peng C (2025) , Advances in Science, Technology & Innovation (pp. 55-67). Springer Nature Switzerland

Conference proceedings

  • McDonald TM, Ross M, Smith MT & 脕lvarez MA (2023) Nonparametric gaussian process covariances via multidimensional convolutions. Proceedings of Machine Learning Research, Vol. 206 (pp 8279-8293). Palau de Congressos, Valencia, Spain, 25 April 2023 - 25 April 2023.
  • Gahungu P, Lanyon CW, 脕lvarez MA, Bainomugisha E, Smith MT & Wilkinson RD (2022) Adjoint-aided inference of Gaussian process driven differential equations. Advances in Neural Information Processing Systems (NeurIPS 2022), Vol. 35. New Orleans, LA, USA, 28 November 2022 - 28 November 2022.
  • Grosse K, Smith MT & Backes M (2021) . 2020 25th International Conference on Pattern Recognition (ICPR) Proceedings (pp 4696-4703). MIlan, Italy, 10 January 2021 - 10 January 2021.
  • Yousefi F, Smith MT & Alvarez MA (2019) Multi-task Learning for aggregated data using Gaussian processes. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vol. 32 (pp 15050-15060). Vancouver, Canada, 8 December 2019 - 8 December 2019.
  • Yousefi F, Smith MT & Alvarez Lopez M (2019) Multi-task Learning for aggregated data using Gaussian processes. Proceedings of the conference on Advances in Neural Information Processing Systems (NIPS 2019), Vol. 32. Vancouver, Canada, 8 December 2019 - 8 December 2019.
  • Smith MT, Alvarez MA, Zwiessele M & Lawrence ND () Differentially private regression with Gaussian processes. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics(84) (pp 1195-1203). Lanzarote, Canary Islands, 9 April 2018 - 9 April 2018.

Preprints

  • Ross M, Smith MT & 脕lvarez MA (2021) , arXiv.
  • Smith M (2019) .
  • Smith MT, Alvarez MA & Lawrence ND (2019) , arXiv.
  • Yousefi F, Smith MT & 脕lvarez MA (2019) , arXiv.
  • Smith MT, Alvarez MA & Lawrence ND (2018) , arXiv.
Grants
  • Tracking flying insects: Deploying novel technology to explore the lives of bees, Leverhulme Trust, 02/2026 - 08/2029, 拢263,018, as PI
  • BLE Bee Tracking System, Eva Crane Trust, 05/2025 - 05/2027, 拢19,902, as Co-I
  • Using Data Driven Artificial Intelligence to Reveal Subtle Pesticide Induced Changes in Pollinator Behaviour, Biotechnology and Biological Sciences Research Council, 02/2024 - 10/2025, 拢249,159, as PI
  • AirQo, Industrial, 08/2019 - 07/2023, 拢197,726, as PI
  • Improved Retroreflector Based Tracking for Bees, Eva Crane Trust, 03/2021 - 03/2023, 拢13,909, as PI
  • Foraging distances and nest locations of bumblebees Bombus, Eva Crane Trust, 04/2019 - 12/2020, 拢5,341, as PI

Links

Machine Learning research group