Projects
Bayesian analysis of single cell sequencing data
Developing statistical tools for biological data, to understand complex biological processes, such as cancer
Artificial intelligence for data analytics (AIDA)
Drawing on new advances in AI and machine learning to address data wrangling issues, and help to automate the data analytics process
People
Ioana Bica
Doctoral Student
Ilan Price
Doctoral Student
Edward Bartrum
Doctoral Student
Edwin Fong
Doctoral Student
Dr Pranay Seshadri
Data-Centric Engineering Group Leader
Dr Chris Russell
Reader in Computer Vision and Machine Learning, University of Surrey
Dr Gerrit van den Burg
Research Associate
Dr Giovanni Colavizza
Visiting Researcher
Dr Evelina Gabasova
Principal Research Data ScientistPublications and software
Handling Incomplete Heterogeneous Data using VAEs
Variational autoencoders (VAEs), as well as other generative models, have been shown to be...
A. Nazábal, P. M. Olmos, Z. Ghahramani, I. Valera "Handling Incomplete Heterogeneous Data using VAEs", Arxiv preprint, July 2018.
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on...
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, Muhammad Bilal Zafar KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018, London, United Kingdom, August 2018
Labelling as an unsupervised learning problem
Unravelling hidden patterns in datasets is a classical problem with many potential applications. In...
Lyons, Terry, and Imanol Perez Arribas. "Labelling as an unsupervised learning problem." arXiv preprint arXiv:1805.03911 (2018).
Sparse communication for distributed gradient descent
We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense...
Aji, A & Heafield, K 2017, Sparse Communication for Distributed Gradient Descent. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). Association for Computational Linguistics (ACL), pp. 440-445, EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 7-11 September.
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning
With widespread use of machine learning methods in numerous domains involving humans, several studies...
Grgic-Hlaca, N., Zafar, M.B., Gummadi, K.P., & Weller, A. (2018). Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. AAAI.