Dr Michael Smith

MScs, PhD

Department of Computer Science

Academic Fellow

Member of the Machine Learning research group

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

Full contact details

Dr Michael Smith
Department of Computer Science
Regent Court
211 Portobello
Sheffield
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 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
Publications

Journal articles

  • Smith MT, Álvarez MA & Lawrence ND (2019) Differentially Private Regression and Classification with Sparse Gaussian Processes.. CoRR, abs/1909.09147. RIS download Bibtex download
  • Smith MT, Grosse K, Backes M & Álvarez MA (2019) Adversarial Vulnerability Bounds for Gaussian Process Classification.. CoRR, abs/1909.08864. RIS download Bibtex download
  • Smith MT, Ssematimba J, Álvarez MA & Bainomugisha E (2019) Machine Learning for a Low-cost Air Pollution Network.. CoRR, abs/1911.12868. RIS download Bibtex download
  • Smith MT, Álvarez MA & Lawrence ND (2018) Gaussian Process Regression for Binned Data.. CoRR, abs/1809.02010. RIS download Bibtex download

Conference proceedings papers

  • Yousefi F, Smith MT & Álvarez MA (2019) Multi-task Learning for Aggregated Data using Gaussian Processes.. NeurIPS (pp 15050-15060) RIS download Bibtex download
  • Smith MT, Álvarez MA, Zwiessele M & Lawrence ND (2018) Differentially private regression with gaussian processes. International Conference on Artificial Intelligence and Statistics, AISTATS 2018 (pp 1195-1203) View this article in WRRO 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

Working papers