Dr Michael Smith

MScs, PhD

School of Computer Science

Lecturer

Outreach, Open Days and Headstart Officer

Member of the Machine Learning research group

m.t.smith@sheffield.ac.uk
+44 114 222 1800

Full contact details

Dr Michael Smith
School of Computer Science
Regent Court (DCS)
211 Portobello
91直播
S1 4DP
Profile

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 91直播 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
Publications

Journal articles

  • Chapman KE, Smith MT, Gaston KJ & Hempel de Ibarra N (2024) . Biology Letters, 20(4). RIS download Bibtex download
  • Smith MT, Grosse K, Backes M & 脕lvarez MA (2022) . Machine Learning. RIS download Bibtex download
  • Smith MT, Livingstone M & Comont R (2021) . Methods in Ecology and Evolution. RIS download Bibtex download
  • Ross M, Smith MT & 脕lvarez MA (2021) Learning Nonparametric Volterra Kernels with Gaussian Processes. Advances in Neural Information Processing Systems, 29, 24099-24110. RIS download Bibtex download
  • Smith MT, Alvarez MA & Lawrence ND (2021) Differentially Private Regression and Classification with Sparse Gaussian Processes. JOURNAL OF MACHINE LEARNING RESEARCH, 22. RIS download Bibtex download
  • Fotheringham J, Smith MT, Froissart M, Kronenberg F, Stenvinkel P, Floege J, Eckardt K-U & Wheeler DC (2020) . BMC Nephrology, 21(1). RIS download Bibtex download
  • Smith MT, Ssematimba J, 脕lvarez MA & Bainomugisha E (2019) Machine Learning for a Low-cost Air Pollution Network.. CoRR, abs/1911.12868. RIS download Bibtex download
  • Smith MT, 脕lvarez MA & Lawrence ND (2019) Differentially Private Regression and Classification with Sparse Gaussian Processes.. CoRR, abs/1909.09147. RIS download Bibtex download
  • Smith MT, 脕lvarez MA & Lawrence ND (2018) Gaussian Process Regression for Binned Data.. CoRR, abs/1809.02010. RIS download Bibtex download
  • Smith MT, Zwiessele M & Lawrence ND (2016) Differentially Private Gaussian Processes.. CoRR, abs/1606.00720. RIS download Bibtex download
  • Smith M, Ross M, Ssematimba J, 脕lvarez MA, Bainomugisha E & Wilkinson R () Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society Series C: Applied Statistics. RIS download Bibtex download

Conference proceedings papers

  • 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) RIS download Bibtex download
  • Gahungu P, Christopher L, Alvarez M, Bainomugisha E, Smith M & Wilkinson R (2022) . Conference on Neural Information Processing Systems, 29 November 2022 - 1 December 2022. RIS download Bibtex download
  • Grosse K, Smith MT & Backes M (2021) . 2020 25th International Conference on Pattern Recognition (ICPR), 10 January 2021 - 15 January 2021. RIS download Bibtex download
  • Yousefi F, Smith MT & 脕lvarez 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) 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. RIS download Bibtex download

Working papers

  • Smith MT, Zwiessele M & Lawrence ND () Differentially Private Gaussian Processes. RIS download Bibtex download

Preprints

  • Ross M, Smith MT & 脕lvarez MA (2021) , arXiv. RIS download Bibtex download
  • Smith MT, Ssematimba J, Alvarez MA & Bainomugisha E (2019) , arXiv. RIS download Bibtex download
  • Smith MT, Grosse K, Backes M & Alvarez MA (2019) , arXiv. RIS download Bibtex download
  • Smith MT, Alvarez MA & Lawrence ND (2019) , arXiv. RIS download Bibtex download
  • Yousefi F, Smith MT & 脕lvarez MA (2019) , arXiv. RIS download Bibtex download
  • Smith MT, Alvarez MA & Lawrence ND (2018) , arXiv. RIS download Bibtex download
Grants

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