Research & Teaching
Publications
Septiandri, A., Ming, D., DiazDelaO, A., Jendoubi, T., and Ray S. (2025) Integrative analysis and imputation of multiple data streams via deep Gaussian processes. Bioinformatics Advances, in press.
Yang, Y., Ming, D., and Guillas, S. (2025) Distribution of deep Gaussian process gradients and sequential design for simulators with sharp variations. arXiv:2503.16027. Under Review.
Zhuang, C., Ming, D., Yuan, M., Makasis, N., Kreitmair J., M., & Choudhary, R. (2024). Linked Deep Gaussian Process for Digital Twins in Building Energy Systems. In Proceedings of ASim Conference 2024: 5th Asia Conference of IBPSA. Osaka, Japan.
Ming, D. and Williamson, D. (2023) Linked deep Gaussian process emulation for model networks. arXiv:2306.01212.
Ming, D., Williamson, D., and Guillas, S. (2023) Deep Gaussian process emulation using stochastic imputation. Technometrics. 65(2), 150-161.
Ming, D. and Guillas, S. (2021) Linked Gaussian process emulation for systems of computer models using Matérn kernels and adaptive design. SIAM/ASA Journal on Uncertainty Quantification. 9(4), 1615-1642.
Research Grants
Knowledge Transfer Partnership (£220,000), Innovate UK & Howden Re, PI (Academic Supervisor), 2025-2027
Development and Calibration of a Mixed-Species Model for Enhanced Woodland Creation Support (£160,000), BBSRC, Co-I on Model Calibration, 2024-2025
ADD-TREES: AI-elevated Decision-support via Digital Twins for Restoring and Enhancing Ecosystem Services (£2M), EPSRC, Co-I on AI development, 2023-2025
NetZeroPlus: Sustainable Treescapes Demonstrator and Decision Tools (£4M), BBSRC, Co-I on emulator development, 2022-2023
Non-Peer-Reviewed Manuscripts
Ming, D. (2020) Notes on Seismic Source Models in Elastostatic. EarthArXiv.
Teaching
University College London
- International Business Analytics (BSc International Management)
- Data Analytics II (BSc Management Science)
- Mathematics III (Probability Theory)
ESCP Business School
- Statistics I: Statistics and Probability