Dr. James Ng

Dr. James Ng

Assistant Professor, Statistics

Publications and Further Research Outputs

  • Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu, Maurizio Filippone, Deep compositional spatial models, Journal of the American Statistical Association, 2021Journal Article, 2021
  • Tin Lok James Ng, Thomas Brendan Murphy, Ted Westling, Tyler H. McCormick, Bailey K. Fosdick, Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmode, Annals of Applied Statistics, 2021Journal Article, 2021
  • Bailey K. Fosdick; Tyler H. McCormick; Thomas Brendan Murphy; Tin Lok James Ng; Ted Westling, Multiresolution Network Models, Journal of Computational and Graphical Statistics, 28, (1), 2019, p185 - 196Journal Article, 2019
  • Tin Lok James Ng; Thomas Brendan Murphy, Model-based clustering for random hypergraphs, Advances in Data Analysis and Classification, 2021Journal Article, 2021
  • Tin Lok James Ng, Thomas Brendan Murphy, Generalized Random Dot Product graph, Statistics & Probability Letters, 148, 2019, p143 - 149Journal Article, 2019
  • Tin Lok James Ng; Kwok-Kun Kwong , Universal approximation on the hypersphere, Communications in Statistics - Theory and Methods, 2021Journal Article, 2021
  • Tin Lok James Ng; Thomas Brendan Murphy , Model-based Clustering of Count Processes, Journal of Classification volume, 38, 2021, p188 - 211Journal Article, 2021
  • Tin Lok J. Ng; Thomas B. Murphy, Estimation of the intensity function of an inhomogeneous Poisson process with a change-point, The Canadian Journal of Statistics, 47, 2019, p604 - 618Journal Article, 2019
  • Tin Lok James Ng; Thomas Brendan Murphy , Weighted stochastic block model, Statistical Methods & Applications, 2021Journal Article, 2021
  • Jake Thompson; James Ng; Bruce Armstrong; Eleonora Feletto; Tam Ha, Differences in colorectal cancer (CRC) patients who did and did not undergo screening: Results from the 45 and Up Study cohort, Cancer Epidemiology, 72, 2021Journal Article, 2021
  • Linyi Yang, Tin Lok James Ng, Barry Smyth, Riuhai Dong, Html: Hierarchical transformer-based multi-task learning for volatility prediction, Proceedings of The Web Conference, Taiwan (Virtual), April 20 - 24, 2020, 2020Conference Paper, 2020
  • Linyi Yang, Tin Lok James Ng, Catherine Mooney, Ruihai Dong, Multi-level attention-based neural networks for distant supervised relation extraction, 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, 7-8 December 2017, 2017Conference Paper, 2017
  • Linyi Yang, Eoin M. Kenny, Tin Lok Ng, Yi Yang, Barry Smyth, and Ruihai Dong, Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification, THE 28TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, Virtual , 8-13 December, 2020, 2020Conference Paper, 2020
  • Jennifer Scott, Enock Havyarimana, Albert Navarro-Gallinad, Arthur White, Jason Wyse, Jos van Geffen, Michiel van Weele, Antonia Buettner, Tamara Wanigasekera, Cathal Walsh, Louis Aslett, John D Kelleher, Julie Power, James Ng, Declan O'Sullivan, Lucy Hederman, Neil Basu, Mark A Little, Lina Zgaga, The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset, Arthritis Research & Therapy, 24, (1), 2022, p1 - 14Journal Article, 2022, DOI
  • Tin Lok James Ng, Andrew Zammit-Mangion, Tin Lok James Ng [PDF] from projecteuclid.org Non-homogeneous Poisson process intensity modeling and estimation using measure transport, Bernoulli, 2023Journal Article, 2023, DOI
  • Tin Lok James Ng, Andrew Zammit-Mangion, Spherical Poisson point process intensity function modeling and estimation with measure transport, Spatial Statistics, 2022Journal Article, 2022, DOI
  • Tin Lok James Ng, Penalized maximum likelihood estimator for mixture of von Mises"Fisher distributions, Metrika, 2023Journal Article, 2023, DOI
  • Daniel E Zoughbie, Tin Lok James Ng, Jacqueline Y Thompson, Kathleen T Watson, Rami Farraj, Eric L Ding, Ramadan fasting and weight change trajectories: Time-varying association of weight during and after Ramadan in low-income and refugee populations, 2022Journal Article, 2022, DOI
  • Z Zhao, TLJ Ng, Fairness-Aware Processing Techniques in Survival Analysis: Promoting Equitable Predictions, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Turin, Italy, 14174, Springer Nature Switzerland, 2023, pp460 - 476Conference Paper, 2023
  • Jennifer Scott, Arthur White, Cathal Walsh, Louis Aslett, Matthew A Rutherford, James Ng, Conor Judge, Kuruvilla Sebastian, Sorcha O'Brien, John Kelleher, Julie Power, Niall Conlon, Sarah M Moran, Raashid Ahmed Luqmani, Peter A Merkel, Vladimir Tesar, Zdenka Hruskova Mark A Little, Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis, RMD Open, 10, (2), 2024, p1-11Journal Article, 2024, DOI

Research Expertise

  • Title
    The project involves applying state-of-the-art approaches, including statistical modelling and Artificial Intelligence techniques to analyse data, to investigate risk factors contributing to depression and anxiety, and the dietary and lifestyle changes that should be implemented to help to improve mental health in older people.
    Funding Agency
    Higher Education Authority
  • Title
    Personalisation of Relapse Risk in Autoimmune Disease: PARADISE Study
    This project aims to develop a robust, evidence-based standardised predictive model to predict ANCA-Associated Vasculitis (AAV) flare risk. Our approach uses semantic web technology to integrate standardised clinical data derived from a longitudinal inception cohort, physician clinical assessment, focused biomarker analysis and app-based patient feedback. The focus of this project is the development of novel Association Rule Mining based predictive models suitable for the complex longitudinal nature of the data and to embed the resulting algorithms alongside international standards development such as ISO/IEC JTC 1/SC 42 for Artificial Intelligence. Unlike many black box data mining and machine learning methods, humans can easily understand the decision-making process of ARM models. The interpretability of ARM based tools makes them particularly suitable to be deployed in a wide range of clinical environments by aligning them with international standards for health applications. The benefit of the project is potentially significant healthcare costs in chronic inflammatory and autoimmune diseases can be expected to drop, while patient/carer burden, clinical time and resources will all be reduced.
    Funding Agency
    Horizon 2020 Marie Sklodowska-Curie COFUND

Mental Health (including Psychiatry and Clinical Psychology), Medical, Health and Life Sciences & Technologies,


  • Irish Statistical Association
  • CMStatistics