Dr Mingjun Zhong

Dr Mingjun Zhong
Dr Mingjun Zhong

Dr Mingjun Zhong

Lecturer

About

Memberships and Affiliations

Internal Memberships
  • Currently I am serving as an Exam Officer and a Tutor.
External Memberships
  • I serve as an Associate Editor for the journal Neural Processing Letters.
  • I serve as a Review Editor for the journal Frontiers in Applied Mathematics and Statistics (Statistics section).

     

  • I am a FHEA (Fellow of the Higher Education Academy)
  • I review grants for multiple funding bodies, a variety of international journals and several prestigious machine learning and artifial intellegence conferences. 
Research

Research Overview

My research interests are mainly (application-driven) machine learning and computational statistics. My research mission is to understand patterns and phenomena observed in real-world data by devising probabilistic and statistical machine learning methodologies.

Current Research

Currently I am working on the following application domains and inferential machine learning algorithms:
  1. Health care data.
  2. Non-intrusive load monitoring (energy disaggregation).
  3. Spectroscopy data.
  4. EEG and fMRI.
  5. Variational inference
  6. Markov chain Monte Carlo
  7. High-dimentional inference
  8. Variance reduction
  9. Intractable likelihood models
  10. Bayesian matrix factorization
Teaching

Teaching Responsibilities

I am teaching the following courses (20/21):

  • CS4048/5059 Robotics
  • CS5062 Machine Learning
  • CS551J Knowlege Representation and Reasoning
Publications

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  • Interpretation and Reporting of Predictive or Diagnostic Machine Learning Research in Trauma & Orthopaedics

    Farrow, L., Zhong, M., Anderson, L., Ashcroft, G., Meek, R. M. D.
    Bone and Joint Journal, vol. 103B, no. 12, pp. 1754-1758
    Contributions to Journals: Articles
  • The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

    Pullinger, M., Kilgour, J., Goddard, N., Berliner, N., Webb, L., Dzikovska, M., Lovell, H., Mann, J., Sutton, C., Webb, J., Zhong, M.
    Scientific Data, vol. 8, no. 1, 146
    Contributions to Journals: Articles
  • Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning

    Barber, J., CuayƔhuitl, H., Zhong, M., Luan, W.
    NILM 2020 - Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring. Association for Computing Machinery, Inc pp. 11-15, 5 pages.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • AREA: An adaptive reference-set based evolutionary algorithm for multiobjective optimisation

    Jiang, S., Li, H., Guo, J., Zhong, M., Yang, S., Kaiser, M., Krasnogor, N.
    Information Sciences, vol. 515, pp. 365-387
    Contributions to Journals: Articles
  • Transfer Learning for Non-Intrusive Load Monitoring

    D'Incecco, M., Squartini, S., Zhong, M.
    IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1419-1429
    Contributions to Journals: Articles
  • Neural Control Variates for Monte Carlo Variance Reduction

    Wan, R., Zhong, M., Xiong, H., Zhu, Z.
    Machine Learning and Knowledge Discovery in Databases. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.). Springer pp. 533-547, 15 pages.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK

    Batra, N., Kukunuri, R., Pandey, A., Malakar, R., Kumar, R., Krystalakos, O., Zhong, M., Meira, P., Parson, O.
    BuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Association for Computing Machinery, Inc pp. 358-359, 2 pages.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Efficient Gradient-Free Variational Inference using Policy Search

    Arenz, O., Neumann, G., Zhong, M.
    Proceedings of the 35th International Conference on Machine Learning. Dy, J., Krause, A. (eds.). MLR Press pp. 234-243, 10 pages.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Sequence-to-Point Learning with Neural Networks for Non-Intrusive Load Monitoring

    Zhang, C., Zhong, M., Wang, Z., Goddard, N., Sutton, C.
    Thirty-second AAAI conference on artificial intelligence. Palo Alto, California USA: AIII Press pp. 2604-2611, 8 pages.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Latent Bayesian melding for integrating individual and population models

    Zhong, M., Goddard, N., Sutton, C.
    Contributions to Journals: Conference Articles
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