Kaihua Ding, Ph.D.

Director of Data Science and Artificial Intelligence at AstraZeneca, AZ Brain Group

Research interest: deep learning, machine learning, adjoint method, parallel computing, and numerical optimization.

Selected Publications

Kaihua Ding, Jingsong Cui, Mohammad Soltani and Jing Jin. Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy. PMSA Annual Meeting, General-3 2024.
Kaihua Ding and Krzysztof J. Fidkowski. Acceleration of Adjoint-Based Adaptation through Sub-Iterations for Unsteady Simulations. AIAA Paper, 2021-0155, 2021.
Kaihua Ding and Krzysztof J. Fidkowski. Acceleration of adjoint-based adaptation through sub-iterations. Journal of Computer & Fluids, Volume 202, 104491, 2020.
Kaihua Ding. Efficient output-based adaptation mechanics for high-order computational fluid dynamics methods. Ph.D. Dissertation, University of Michigan-Ann Arbor, 2018
Kaihua Ding and Krzysztof J. Fidkowski. Output error control using r-adaptation. AIAA Paper, 2017-4111, 2017.
Kaihua Ding, Krzysztof J. Fidkowski, and Philip L. Roe. Continuous adjoint based error estimation and r-refinement for the active flux method. AIAA Paper 2016-0832, 2016.
Kaihua Ding, Krzysztof J. Fidkowski, and Philip L. Roe. Acceleration techniques for adjoint-based error estimation and mesh adaptation. Eighth International Conference on Computational Fluid Dynamics, ICCFD8-0249, 2014.
Kaihua Ding, Krzysztof J. Fidkowski, and Philip L. Roe. Adjoint-based error estimation and mesh adaptation for the active flux method. AIAA Paper 2013-2942, 2013.