Closed-Loop Data Science

Progress in sensing, computational power, storage and analytic tools has given us access to enormous amounts of complex data, which can inform us of better ways to manage our cities, run our companies or develop new medicines. However, the 'elephant in the room' is that when we act on that data we change the world, potentially invalidating the older data. Similarly, when monitoring living cities or companies, we are not able to run clean experiments on them - we get data which is affected by the way they are run today, which limits our ability to model these complex systems. We need ways to run ongoing experiments on such complex systems. We also need to support human interactions with large and complex data sets. In this project we will look at the overlap between the challenge someone faces when coping with all the choices associated with booking a flight for a weekend away, and an expert running complex experiments in a laboratory.
 
The project will test the core ideas in a number of areas, including personalisation of hearing aids, support for travel planning, analysis of cancer data, and media recommendation systems.
 

Partners

This project involves collaborative research with academic partners:  Glasgow Polyomics, the Urban Big Data Centre,  the University of Warwick, and industrial partners: Moodagent,   Widex A/S, Aegean Airlines and is further supported by DataLab Scotland.
 

Publications

Yilmaz, S., Dudkina, E., Bin, M., Crisostomi, E., Ferraro, P., Murray-Smith, R. , Parisini, T., Stone, L. and Shorten, R. (2020) Kemeny-based testing for COVID-19. PLoS ONE, 15(11), e0242401. (doi: 10.1371/journal.pone.0242401)

Borowska, A. , Giurghita, D. and Husmeier, D. (2020) Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis. Journal of Computational Physics, (doi: 10.1016/j.jcp.2020.109999) (In Press)

Taka, E., Stein, S. and Williamson, J. H. (2020) Increasing interpretability of Bayesian probabilistic programming models through interactive visualizations. Frontiers in Computer Science, (doi: 10.3389/fcomp.2020.567344) (Accepted for Publication)

Mohammadi, S., Uhrenholt, A. K. and Jensen, B. S. (2020) Odd-One-Out Representation Learning. Object Representations for Learning and Reasoning, 11 Dec 2020. (Accepted for Publication)

Anagnostopoulos, C. and Kolomvatsos, K. (2020) Predictive intelligence of reliable analytics in distributed computing environments. Applied Intelligence, 50, pp. 3219-3238. (doi: 10.1007/s10489-020-01712-5)

Wu, Y., Macdonald, C. and Ounis, I. (2020) A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation. In: ICTIR 2020: The 6th ACM International Conference on the Theory of Information Retrieval, Stavanger, Norway, 14-18 Sep 2020, pp. 89-96. ISBN 9781450380676 (doi:10.1145/3409256.3409835)

Quiros, A. C., Murray-Smith, R. and Yuan, K. (2020) PathologyGAN: Learning Deep Representations of Cancer Tissue. Proceedings of Machine Learning Research, 124, pp. 669-695.

Laux, L., Cutiongco, M. F.A. , Gadegaard, N. and Jensen, B. S. (2020) Interactive machine learning for fast and robust cell profiling. PLoS ONE, 15(9), e0237972. (doi: 10.1371/journal.pone.0237972) (PMID:32915784)

Kolomvatsos, K., Anagnostopoulos, C. , Koziri, M. and Loukopoulos, T. (2020) Proactive & time-optimized data synopsis management at the edge. IEEE Transactions on Knowledge and Data Engineering, (doi: 10.1109/TKDE.2020.3021377) (Early Online Publication)

Savva, F. , Anagnostopoulos, C. , Triantafillou, P. and Kolomvatsos, K. (2020) Large-scale data exploration using explanatory regression functions. ACM Transactions on Knowledge Discovery from Data, 14(6), 76. (doi: 10.1145/3410448)

Tonolini, F., Radford, J., Turpin, A. , Faccio, D. and Murray-Smith, R. (2020) Variational inference for computational imaging inverse problems. Journal of Machine Learning Research, 21(179), pp. 1-46.

Husmeier, D. and Paun, L. M. (2020) Closed-loop effects in cardiovascular clinical decision support. In: Ladde, G. and Samia, N. (eds.) Proceedings of the 2nd International Conference on Statistics: Theory and Applications (ICSTA'20). Avestia Publishing: Ottawa, Canada, p. 128. ISBN 9781927877685 (doi:10.11159/icsta20.128)

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. (2020) Adaptive learning of aggregate analytics under dynamic workloads. Future Generation Computer Systems, 109, pp. 317-330. (doi: 10.1016/j.future.2020.03.063)

Anagnostopoulos, C. (2020) Edge-centric inferential modeling & analytics. Journal of Network and Computer Applications, 164, 102696. (doi: 10.1016/j.jnca.2020.102696)

Husmeier, D. and Paun, L. M. (2020) Closed-loop effects in coupling cardiac physiological models to clinical interventions. In: Irigoien, I., Lee, D.-J., Martínez-Minaya, J. and Rodríguez-Álvarez, M. X. (eds.) Proceedings of the 35th International Workshop on Statistical Modelling. Servicio Editorial de la Universidad del País Vasco: Bilbao, Spain, pp. 120-125. ISBN 9788413192673

Jadidinejad, A. H. , Macdonald, C. and Ounis, I. (2020) Using Exploration to Alleviate Closed-Loop Effects in Recommender Systems. In: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Xi'an, China, 25-30 Jul 2020, pp. 2025-2028. ISBN 9781450380164 (doi:10.1145/3397271.3401230)

Williamson, J. H. , Quek, M., Popescu, I., Ramsay, A. and Murray-Smith, R. (2020) Efficient human-machine control with asymmetric marginal reliability input devices. PLoS ONE, 15(6), e0233603. (doi: 10.1371/journal.pone.0233603)

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. (2020) SuRF: Identification of Interesting Data Regions with Surrogate Models. In: 36th IEEE International Conference on Data Engineering (IEEE ICDE), Dallas, TX, USA, 20-24 April 2020, pp. 1321-1332. ISBN 9781728129037 (doi:10.1109/ICDE48307.2020.00118)

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. (2020) Aggregate Query Prediction under Dynamic Workloads. In: 2019 IEEE International Conference on Big Data (IEEE BigData 2019), Los Angeles, CA, USA, 09-12 Dec 2019, pp. 671-676. ISBN 9781728108582 (doi:10.1109/BigData47090.2019.9006267)

Anagnostopoulos, C. and Triantafillou, P. (2020) Large-scale predictive modeling and analytics through regression queries in data management systems. International Journal of Data Science and Analytics, 9(1), pp. 17-55. (doi: 10.1007/s41060-018-0163-5)

Ireland, D.G. , Doring, M., Glazier, D.I., Haidenbauer, J., Mai, M., Murray-Smith, R. and Ronchen, D. (2019) Kaon photoproduction and the Lambda decay parameter alpha. Physical Review Letters, 123, 182301. (doi: 10.1103/PhysRevLett.123.182301)

Wandy, J., Davies, V., van der Hooft, J. J.J. , Weidt, S., Daly, R. and Rogers, S. (2019) In silico optimization of mass spectrometry fragmentation strategies in metabolomics. Metabolites, 9(10), 219. (doi: 10.3390/metabo9100219) (PMID:31600991)

Jadidinejad, A. , Macdonald, C. and Ounis, I. (2019) How Sensitive is Recommendation Systems' Offline Evaluation to Popularity? In: REVEAL 2019 Workshop at RecSys, Copenhagen, Denmark, 20 Sep 2019,

Davies, V. , Harvey, W. T., Reeve, R. and Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(4), pp. 859-885. (doi: 10.1111/rssc.12338) (PMID:31598013) (PMCID:PMC6774336)

Tonolini, F., Jensen, B. S. and Murray-Smith, R. (2019) Variational Sparse Coding. In: Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, 22-25 July 2019,

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. (2019) Explaining Aggregates for Exploratory Analytics. In: IEEE Big Data 2018, Seattle, WA, USA, 10-13 Dec 2018, pp. 478-487. ISBN 9781538650356 (doi:10.1109/BigData.2018.8621953)

Jadidinejad, A. H. , Macdonald, C. and Ounis, I. (2019) Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks. In: 5th ACM SIGIR International Conference on the Theory of Information Retrieval, Santa Clara, CA, USA, 02-05 Oct 2019, pp. 149-151. ISBN 9781450368810 (doi:10.1145/3341981.3344225)

Moran, O., Caramazza, P., Faccio, D. and Murray-Smith, R. (2018) Deep, Complex, Invertible Networks for Inversion of Transmission Effects in Multimode Optical Fibres. In: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 02-08 Dec 2018,

Funded Projects

EPSRC funded project: £3M, 2018-2022:  Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics
 
EPSRC funded project: £113k, July-Nov 2020: Fast multi-shot epidemic interventions for post lockdown Covid-19 mitigation: Open-loop mitigation strategies, EP/V018450/1

Vacancies

Currently no vacancies.