Dr. Mimi Zhang

• Mimi Zhang and Tim Bedford, Vine Copula Approximation: A Generic Method for Coping with Conditional Dependence, Statistics and Computing, 28, (1), 2018, p219 - 237Journal Article, 2018, TARA - Full Text
• Mimi Zhang and Min Xie, An Ameliorated Improvement Factor Model for Imperfect Maintenance and Its Goodness of Fit, Technometrics, 59 (2), 2017, p237 - 246Journal Article, 2017, TARA - Full Text
• Mimi Zhang and Matthew Revie, Continuous-Observation Partially Observable Semi-Markov Decision Processes for Machine Maintenance, IEEE Transactions on Reliability, 66 (1), 2017, p202 - 218Journal Article, 2017, TARA - Full Text
• Mimi Zhang, Olivier Gaudoin and Min Xie, Degradation-Based Maintenance Using Stochastic Filtering for Systems under Imperfect Maintenance, European Journal of Operational Research, 245 (2), 2015, p531 - 541Journal Article, 2015, TARA - Full Text
• Mimi Zhang, Qingpei Hu, Min Xie and Dan Yu, Lower Confidence Limit for Reliability Based on Grouped Data with a Quantile Filling Algorithm, Computational Statistics & Data Analysis, 75, 2014, p96 - 111Journal Article, 2014, TARA - Full Text
• Mimi Zhang, Zhisheng Ye and Min Xie, A Condition-Based Maintenance Strategy for Heterogeneous Populations, Computers & Industrial Engineering, 77, 2014, p103 - 114Journal Article, 2014, TARA - Full Text
• Mimi Zhang, Min Xie and Olivier Gaudoin, A Bivariate Maintenance Policy for Multi-State Repairable Systems with Monotone Process, IEEE Transactions on Reliability, 62 (4), 2013, p876 - 886Journal Article, 2013, TARA - Full Text
• Mimi Zhang, Zhisheng Ye and Min Xie, A Stochastic EM Algorithm for Progressively Censored Data Analysis, Quality and Reliability Engineering International, 30 (5), 2014, p711 - 722Journal Article, 2014, TARA - Full Text
• Mimi Zhang and Matthew Revie, Model selection with application to gamma process and inverse Gaussian process, CRC/Taylor & Francis Group, European Safety and Reliability Conference 2016, Glasgow, Sep, 2016Conference Paper, 2016, TARA - Full Text
• Mimi Zhang, Weighted Clustering Ensemble: A Review, Pattern Recognition, 124, 2022, p108428Journal Article, 2022, TARA - Full Text
• Mimi Zhang, Forward-Stagewise Clustering: An Algorithm for Convex Clustering, Pattern Recognition Letters, 128, 2019, p283 - 289Journal Article, 2019, TARA - Full Text
• Min Xie and Mimi Zhang, Discussion of "Virtual age, is it real?", Applied Stochastic Models in Business and Industry, 37, (1), 2021, p30 - 31Journal Article, 2021
• Mimi Zhang, A Heuristic Policy for Maintaining Multiple Multi-State Systems, Reliability Engineering and System Safety, 203, 2020, p107081Journal Article, 2020, TARA - Full Text
• 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, p2554-2570Journal Article, 2021, DOI , URL
• Joshua Tobin and Mimi Zhang, DCF: An Efficient and Robust Density-Based Clustering Method, 2021 IEEE International Conference on Data Mining (ICDM), 2021, p629 - 638Journal Article, 2021, 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, p114275Journal Article, 2022, 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 - 56Journal Article, 2022
• 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, pe77373Journal Article, 2022
• Mimi Zhang and Andrew Parnell, Review of Clustering Methods for Functional Data, ACM Transactions on Knowledge Discovery from Data, 17, (7), 2023, p1 - 34Journal Article, 2023
• Bernard Fares and Mimi Zhang, Incorporating Ignorance within Game Theory: An Imprecise Probability Approach, International Journal of Approximate Reasoning, 154, (March), 2023, p133 - 148Journal Article, 2023, 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 - 126Journal Article, 2023, TARA - Full Text
• 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 - 1120Journal Article, 2024, TARA - Full Text

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

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.

• Title

  FLImagin3D: Fluorescent Lifetime Imaging Microscopy in Biomedical Applications
  Summary

  beneficiary of the 2021 MSCA Doctoral Networks FLImagin3D, working on fluorescence microscopy data analysis
  Funding Agency

  European Union
  Date From

  Jan/2023
  Date To

  Dec/2026
• Title

  I-Form, the SFI Research Centre for Advanced Manufacturing
  Summary

  funded investigator for I-Form Phase 1, working on AM process feedback and control
  Funding Agency

  Science Foundation Ireland
  Date From

  Nov/2017
  Date To

  Oct/2023
• Title

  AIM4HEALTH
  Summary

  artificial intelligence approaches to addressing mental health inequalities in Ireland through improved diet and lifestyle
  Funding Agency

  Higher Education Authority
  Date From

  Sep/2022
  Date To

  Feb/2024
• Title

  I-Form, the SFI Research Centre for Advanced Manufacturing
  Summary

  funded investigator for I-Form Phase 2, working on AM process feedback and control
  Funding Agency

  Science Foundation Ireland
  Date From

  Nov/2023
  Date To

  Oct/2029

Computer science - Theory & Methods, Algorithms, Mathematics, Industrial and mechanical Engineering, Computer science - Artificial Intelligence,