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Dr. Mimi Zhang

Assistant Professor (Statistics)

Mimi Zhang joined TCD as an assistant professor in October 2017. She holds a B.Sc. in statistics from University of Science and Technology of China (Sep. 2007-Jul. 2011), and a Ph.D. in industrial engineering from City University of Hong Kong (Nov. 2011-Dec. 2014). Before joining TCD, she was a research associate at University of Strathclyde and Imperial College London. Her main research areas are machine learning and operations research, including cluster analysis, Bayesian optimization, functional data analysis, reliability & maintenance (engineering), etc. She is the strand leader of the Data Science MSc programme and an AE for Journal of Classification.

Current PhD students:

  • Guangchen Wang, 2023, co-supervise with Prof. Michael Monaghan
  • Samuel Singh, 2023, co-supervise with Dr Shirley Coyle
  • Emmanuel Akeweje, 2023, co-supervise with Prof. Thomas Chadefaux
  • Jessica Bagnall, 2023, co-supervise with Prof. TrĂ­ona Lally
  • Sukriti Dhang, 2022, co-supervise with Dr Soumyabrata Dev

Former PhD students:

  • Joshua Tobin, thesis title "Consistent Mode-Finding for Parametric and Non-Parametric Clustering".
  • Bernard Fares (part time), thesis title "Incorporating Ignorance within Game Theory: An Imprecise Probability Approach".

Teaching Activities

  • 09/21-now: Introduction to Statistical Concepts and Methods (10 ECT), Coordinator
  • 09/21-now: Implementing Statistical Methods in R (5 ECT), Coordinator
  • 09/17-now: Software Application (5 ECT), Coordinator
  • 09/17-08/21: Statistics Base Module (15 ECT), Coordinator


 FLImagin3D: Fluorescent Lifetime Imaging Microscopy in Biomedical Applications
 I-Form, the SFI Research Centre for Advanced Manufacturing
 I-Form, the SFI Research Centre for Advanced Manufacturing

Joshua Tobin and Mimi Zhang, A Theoretical Analysis of Density Peaks Clustering and the Component-wise Peak-Finding Algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, (2), 2024, p1109 - 1120, Journal Article, PUBLISHED  TARA - Full Text
Mimi Zhang and Andrew Parnell, Review of Clustering Methods for Functional Data, ACM Transactions on Knowledge Discovery from Data, 17, (7), 2023, p1 - 34, Journal Article, PUBLISHED
Bernard Fares and Mimi Zhang, Incorporating Ignorance within Game Theory: An Imprecise Probability Approach, International Journal of Approximate Reasoning, 154, (March), 2023, p133 - 148, Journal Article, PUBLISHED  TARA - Full Text
Joshua Tobin, Chin Pang Ho and Mimi Zhang, Reinforced EM Algorithm for Clustering with Gaussian Mixture Models, Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 2023, p118 - 126, Journal Article, PUBLISHED  TARA - Full Text
Mimi Zhang, Weighted Clustering Ensemble: A Review, Pattern Recognition, 124, 2022, p108428 , Journal Article, PUBLISHED  TARA - Full Text
Mimi Zhang, Matthew Revie and John Quigley, Saddlepoint Approximation for the Generalized Inverse Gaussian Levy Process, Journal of Computational and Applied Mathematics, 411, 2022, p114275 , Journal Article, PUBLISHED  TARA - Full Text
Mimi Zhang and Bin Liu, Discussion of signature-based models of preventive maintenance, Applied Stochastic Models in Business and Industry, 39, (1), 2022, p54 - 56, Journal Article, PUBLISHED
Nuno Neto, Sinead O'Rourke, Mimi Zhang, Hannah Fitzgerald, Aisling Dunne and Michael Monaghan, Non-Invasive classification of macrophage polarisation by 2P-FLIM and machine learning, eLife, 11, 2022, pe77373 , Journal Article, PUBLISHED
Min Xie and Mimi Zhang, Discussion of "Virtual age, is it real?", Applied Stochastic Models in Business and Industry, 37, (1), 2021, p30 - 31, Journal Article, PUBLISHED
Muhannad Ahmed Obeidi, Medad Monu, Cian Hughes, Declan Bourke, Merve Nur Dogu, Joshua Francis, Mimi Zhang, Inam Ul Ahad and Dermot Brabazon, Laser beam powder bed fusion of nitinol shape memory alloy (SMA), Journal of Materials Research and Technology, 14, 2021, p2554-2570 , Journal Article, PUBLISHED  DOI

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Mimi Zhang, Andrew Parnell, Dermot Brabazon and Alessio Benavoli, Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing, arXiv:2107.12809, arXiv, 2021, Report, PUBLISHED


My academic journey spans from a foundation in mathematical statistics during my undergraduate studies to a focus on optimization algorithms and their applications in my doctoral and postdoctoral research. This interdisciplinary background integrates mathematics, probability, statistics, and algorithms to address diverse challenges across sectors like manufacturing, materials science, and healthcare.

My primary research focus centers on cluster analysis, where I specialize in advancing methodological, theoretical, and computational approaches tailored to analyze various data types including multivariate, functional, and image data, among others. Functional data clustering is to find patterns in the subjects, where each subject is represented by a continuous function. Functional data clustering has a wide range of applications in many fields: in bioinformatics to group gene expression profiles, in econometrics to group economic time series, and in engineering to group vibrations of mechanical systems.

Complementing my work in cluster analysis, my research portfolio extends to Bayesian Optimization -- a methodology designed to find the maximum (or minimum) of an unknown function, often called the ''objective function'', which is typically expensive to evaluate and may be noisy or exhibit uncertainty. The goal is to iteratively select the next best point to evaluate in order to efficiently search for the optimal solution. My collaborations in Bayesian optimization with academic and industry partners have afforded me the opportunity to address real-world challenges, a pursuit that I find immensely rewarding and fulfilling.