Dr Ida Pu

Ida specialises in probabilistic and average case algorithms, data structures and communications and complexity theory.

Staff details

Dr Ida Pu






i.pu (@gold.ac.uk)



Lecturer, deputy Senior Tutor, researcher, PhD supervisor and examiner, project manager, member of academic committee, equal opportunity representative, special needs coordinator.

Areas of supervision

Probabilistic and average case algorithms, data structures, networks and communications and complexity theory.

Publications and research outputs


Pu, Ida. 2006. Fundamental Data Compression. 0-7506-6310-3: Elsevier.

Book Section

Pu, Ida and Shen, Y.. 2009. Efficient algorithms for noise propagation in diffusion tensor imaging. In: Joseph Chan; Jacqueline Daykin and M. Sohel Rahman, eds. London Algorithmics 2008: Theory and Practice: (Texts in Algorithmics). London: College Publications, pp. 1-21. ISBN 978-1-904-98797-0


Pu, Ida; Stamate, Daniel and Shen, Yuji. 2014. Improving time-efficiency in blocking expanding ring search for mobile ad hoc networks. Journal of Discrete Algorithms, 24, pp. 59-67. ISSN 1570-8667

Shen, Yuji and Pu, Ida. 2013. Quantification of venous vessel size in human brain in response to hypercapnia and hyperoxia using magnetic resonance imaging. Magnetic Resonance in Medicine, 69(6), pp. 1541-1552. ISSN 0740-3194

Shen, Yuji; Ho, Yi-Ching L.; Vidyasagar, Rishma; Balanos, George; Golay, Xavier; Pu, Ida and Kauppinen, Risto A.. 2012. Gray matter nulled and vascular space occupancy dependent fMRI response to visual stimulation during hypoxic hypoxia. NeuroImage, 59(4), pp. 3450-3456. ISSN 1053-8119

Conference or Workshop Item

Musto, Henry; Stamate, Daniel; Pu, Ida and Stahl, Daniel. 2023. 'Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort'. In: Computational Collective Intelligence. ICCCI 2023.. Budapest, Hungary 27–29 September 2023.

Musto, Henry; Stamate, Daniel; Pu, Ida and Stahl, Daniel. 2022. 'A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease'. In: 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Pasadena, CA, United States 13-16 December 2021.

Ermaliuc, Miha; Stamate, Daniel; Magoulas, George D. and Pu, Ida. 2021. 'Creating Ensembles of Generative Adversarial Network Discriminators for One-Class Classification'. In: International Conference on Engineering Applications of Neural Networks. Halkidiki, Greece 25–27 June 2021.

Research Interests

Centre on Randomised, Parallel, Probabilistic and Average Case Algorithmics and cover various application areas: Design and Analysis of Algorithms, Data Communications and Networking, Data Compression, Diffusion-weighted Image Processing, Music Analysis

Current research

Parallel generation of combinatorial structures (e.g. paths, spanning trees, random graphs and strings) uniformly at random, matricial space economy with constant worst case access time, geometric distortions in diffusion-weighted images, and problems in various application areas such as data communications and mobile ad doc networking, data compression, image processing and music analysis.