Dr. Mimi Zhang
Assistant Professor, Statistics
Biography
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. Tríona 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
Software
- CPFcluster: https://pypi.org/project/CPFcluster/
- REMclust: https://pypi.org/project/REMclust/
Publications and Further Research Outputs
Peer-Reviewed Publications
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
Mimi Zhang and Andrew Parnell, Review of Clustering Methods for Functional Data, ACM Transactions on Knowledge Discovery from Data, 17, (7), 2023, p1 - 34
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
Bernard Fares and Mimi Zhang, Incorporating Ignorance within Game Theory: An Imprecise Probability Approach, International Journal of Approximate Reasoning, 154, (March), 2023, p133 - 148
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
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
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
Mimi Zhang, Weighted Clustering Ensemble: A Review, Pattern Recognition, 124, 2022, p108428
Min Xie and Mimi Zhang, Discussion of "Virtual age, is it real?", Applied Stochastic Models in Business and Industry, 37, (1), 2021, p30 - 31
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
Joshua Tobin and Mimi Zhang, DCF: An Efficient and Robust Density-Based Clustering Method, 2021 IEEE International Conference on Data Mining (ICDM), 2021, p629 - 638
Mimi Zhang, A Heuristic Policy for Maintaining Multiple Multi-State Systems, Reliability Engineering and System Safety, 203, 2020, p107081
Mimi Zhang, Forward-Stagewise Clustering: An Algorithm for Convex Clustering, Pattern Recognition Letters, 128, 2019, p283 - 289
Mimi Zhang and Tim Bedford, Vine Copula Approximation: A Generic Method for Coping with Conditional Dependence, Statistics and Computing, 28, (1), 2018, p219 - 237
Mimi Zhang and Matthew Revie, Continuous-Observation Partially Observable Semi-Markov Decision Processes for Machine Maintenance, IEEE Transactions on Reliability, 66 (1), 2017, p202 - 218
Mimi Zhang and Min Xie, An Ameliorated Improvement Factor Model for Imperfect Maintenance and Its Goodness of Fit, Technometrics, 59 (2), 2017, p237 - 246
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, 2016
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 - 541
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 - 722
Mimi Zhang, Zhisheng Ye and Min Xie, A Condition-Based Maintenance Strategy for Heterogeneous Populations, Computers & Industrial Engineering, 77, 2014, p103 - 114
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 - 111
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 - 886
Non-Peer-Reviewed Publications
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
Research Expertise
Description
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.
Projects
- 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
- 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 1, working on AM process feedback and control
- Funding Agency
- Science Foundation Ireland
- Date From
- Nov/2017
- Date To
- Oct/2023
- 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