Stef Garasto

Dr Stef Garasto BSc, MSc, PhD

Associate Professor in Data Science (AI and Ethics)

Stef Garasto is an Associate Professor in Data Science at the University of Greenwich, with a focus on machine learning, participatory approaches to data science, and issues at the intersection of social justice and data-driven systems.

Prior to joining Greenwich, Stef worked a researcher at Imperial College London and at Nesta. As a Principal Researcher in Data Science at Nesta, they used novel sources of data and machine learning algorithms with a view to building a more resilient and inclusive labour market. This included developing measures of jobs accessibility and skill demand using a variety of datasets.

Stef obtained a PhD in Computational Neuroscience from Imperial College London. Their PhD research focused on stimulus reconstruction methods to investigate how the brain processes sensory information (specifically vision) and population coding principles.

Computer science and computing Computer science Computing

Responsibilities within the university

Associate Professor in Data Science (AI and Ethics)

Programme Leader for MSc Data Science and its Applications; MSc Data Science; MSc Data Science with Placement Year; MSc Management of Business Information Technology; MSc Computing and Information Systems.

Research / Scholarly interests

My research falls under the umbrella of applied data science, using computational and participatory approaches to derive insights about systems - whether societal, biological or algorithmic. My research also seeks to understand if and how data science and machine learning can be used to investigate and challenge social inequities and power hierarchies. I am particularly interested about what happens when data science and queerness collide.

Recent publications
  • Annand, P., Garasto, S., & Groves, L., 2026. Public say versus public sway: ‘Lived experience literacy’ in the pursuit of participatory AI. Big Data & Society, 13(2).
  • Klyshbekova, M., Cruz, G.R., Bentley, C., Garasto, S., Brown, A.A., Aicardi, C., Ball, B., Naiseh, M. and Andrei, O., 2025. A UK perspective on responsible education for responsible AI: a multidisciplinary review and evaluation framework. Journal of Responsible Technology, p.100147.
  • Garasto, S. and Szabó, M., 2025. Teaching AI and data ethics in an ‘Ethics and Governance’ Master’s course. Teaching Ethics.
  • Fotiadis, S., Lino Valencia, M., Hu, S., Garasto, S., Cantwell, C. D., & Bharath, A. A., 2023. Disentangled generative models for robust dynamical system prediction. Proceedings of the 40th International Conference on Machine Learning in Proceedings of Machine Learning Research 202:10222-10248.
  • Garasto, S., Djumalieva, J., Kanders, K., Wilcock, R. and Sleeman, C., 2021. Developing experimental estimates of regional skill demand. (No.  ESCoE DP-2020-19). Economic Statistics Centre of Excellence (ESCoE) Discussion Papers.
  • Djumalieva,  J., Garasto, S.,  and Sleeman, C., 2020.  Evaluating a new earnings indicator:  Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?  (No.  ESCoE DP-2020-19). Economic Statistics Centre of Excellence (ESCoE) Discussion Papers.
  • Garasto, S., Nicola, W., Bharath, A.A. and Schultz, S.R., 2019, March. Neural sampling strategies for visual stimulus reconstruction from two-photon imaging of mouse primary visual cortex. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 566-570). IEEE.
  • Sorteberg, W.E., Garasto, S., Cantwell, C.C. and Bharath, A.A., 2019, April. Approximating the solution of surface wave propagation using deep neural networks. In INNS Big Data and Deep Learning conference (pp. 246-256). Springer, Cham.
  • Cantwell, C.D., Mohamied, Y., Tzortzis, K.N., Garasto, S., Houston, C., Chowdhury, R.A., Ng, F.S., Bharath, A.A. and Peters, N.S., 2019. Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling. Computers in biology and medicine, 104, pp.339-351.
  • Hakkinen,  A.,  Kandhavelu,  M., Garasto, S.,  and  Ribeiro,  A.  S.,  2014. Estimation  of fluorescence-tagged RNA numbers from spot intensities. Bioinformatics, btt766.