Research & Teaching

Publications

Ming, D. and Williamson, D. (2023) Linked deep Gaussian process emulation for model networks. arXiv:2306.01212. Under Review.

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

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

• Data Analytics II (BSc Management Science)
• Mathematics III (Probability Theory)

ESCP Business School

• Statistics I: Statistics and Probability