Mengjia Zhu

Mengjia Zhu

Research Fellow at UoL. My current research interests include black-box optimization and control, with particular emphasis on active, preference, and safe learning.

Publications

JOURNAL PAPERS (SUBMITTED)

  1. L. Cannelli, M. Zhu, M. Schranz, A. Bemporad, and D. Piga, “D-GLISp: A distributed algorithm for preference-based black-box optimization in multi-agent systems,” 2025, submitted for publication.
  2. M. Zhu, O. Pennington, T. Pincam, M. Zarei, S. Kay, Y. Liu, M. Short, and D. Zhang, “A novel bioprocess control strategy under uncertainty via operational space identification,” 2025, submitted for publication.
  3. S. Kay, M. Zhu, A. Lane, J. Shaw, and D. Zhang, “A surrogate-enhanced framework for flexible and optimal operational space identification under uncertainty,” 2025, submitted for publication.
  4. Y. Xiao, M. Zhu, D. Tsaoulidis, and T. Chen, “Adaptive optimisation of experiments with early stopping using gaussian process regression,” 2025, submitted for publication.

JOURNAL PAPERS (IN PRESS)

JOURNAL PAPERS

  1. S. Kay, M. Zhu, A. Lane, J. Shaw, P. Martin, and D. Zhang, “A novel approach to identify optimal and flexible operational spaces for product quality control,” Chemical Engineering Science, 2025, vol. 309, no. 121429. paper-link, pdf
  2. M. Zhu and A. Bemporad, “Global and preference-based optimization with mixed variables using piecewise affine surrogates,” Journal of Optimization Theory and Applications, vol. 204, no. 26, 2025. paper-link, code, arXiv
  3. M. Zhu, A. Mroz, L. Gui, K. Jelfs, A. Bemporad, EA. del Río Chanona, and Y. Lee, “Discrete and mixed-variable experimental design with surrogate-based approach,” Digital Discovery, 2024, vol. 3, pp. 2589-2606, 2024. paper-link, code, ChemRxiv
  4. L. Cannelli, M. Zhu, F. Farina, A. Bemporad, and D. Piga, “Multi-agent active learning for distributed black-box optimization,” IEEE Control Systems Letters, vol. 7, pp. 1488–1493, 2023. paper-link, pdf, code
  5. M. Zhu, D. Piga, and A. Bemporad, “C-GLISp: Preference-based global optimization under unknown constraints with applications to controller calibration,” IEEE Trans. Contr. Systems Technology, vol. 30, no. 3, pp. 2176–2187, 2022. paper-link, pdf, code

CONFERENCE PAPERS (SUBMITTED)

CONFERENCE PAPERS

  1. S. Kay, M. Zhu, O. Pennington, and D. Zhang, “Machine learning-driven optimisation of operational spaces for uncertainty management in process industries,” in 14th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS 2025), IFAC-PapersOnLine, vol. 59, no. 6, pp. 241-246, Bratislava, Slovakia, 2025. paper-link
  2. M. Zhu, A. Bemporad, M. Kneissl, and H. Esen, “Learning critical scenarios in feedback control systems for automated driving,” in IEEE 26th Int. Conf. on Intelligent Transportation Systems (ITSC), Bilbao, Bizkaia, Spain, 2023. paper-link, pdf
  3. M. Zhu, A. Bemporad, and D. Piga, “Preference-based MPC calibration,” in European Control Conference, 2021. paper-link, pdf

BOOK CHAPTERS

  1. M. Zhu, O. Pennington, S. Kay, M. Zarei, M. Short, and D. Zhang, “Optimization-based operational space design for effective bioprocess performance under uncertainty,” Systems and Control Transactions, vol. 4, pp. 2291-2296, 2025. paper-link, pdf
  2. S. Kay, M. Zhu, A. Lane, J. Shaw, P. Martin, and D. Zhang, “Machine learning-aided robust optimisation for identifying optimal operational spaces under uncertainty,” Systems and Control Transactions, vol. 4, pp. 1041-1046, 2025. paper-link, pdf
  3. M. Zarei, M. Dolat, R. Murali, M. Zhu, O. Pennington, D. Zhang, M. Short, “Real-time dynamic optimisation for sustainable biogas production through anaerobic co-digestion with hybrid models,” Systems and Control Transactions, vol. 4, pp. 2423-2428, 2025. paper-link, pdf
  4. A. Molin, E. Aguilar, D. Nickovic, M. Zhu, A. Bemporad, and H. Esen, “Specification-guided critical scenario identification for automated driving,” 25th International Symposium on Formal Methods, 2023. paper-link, pdf
  5. M. Zhu, FL. Santamaria, S. Macchietto, “A general dynamic model of a complete milk pasteuriser unit subject to fouling,” in Computer Aided Chemical Engineering, vol. 48, pp. 247-252, 2020. paper-link, pdf

TECHNICAL REPORTS

  1. M. Zhu, D. Piga, and A. Bemporad, “C-GLIS: Global Optimization under Unknown Constraints,” 2021. pdf

THESES

  1. M. Zhu, “Global and preference-based optimization using surrogate-based methods”, Ph.D. dissertation, Systems Science, Track in Computer Science and Systems Engineering, IMT School for Advanced Studies Lucca (Scuola IMT Alti Studi Lucca), Lucca, Italy, May 2024. pdf
  2. M. Zhu, “A general dynamic model of a complete milk pasteuriser unit subject to fouling,” MSc. thesis, Imperial College London, London, UK, September 2019. pdf