Ruda Zhang, Phillip Griffiths Assistant Research Professor

My current research is on manifold-based methods for dimension reduction of computational models, which includes learning manifold-valued mappings and probabilistic learning on manifolds.

Please note: Ruda has left the Mathematics department at Duke University; some info here might not be up to date.

Contact Info:

Office Location:
Email Address:
Web Page:	https://ruda.city/

Education:

Ph.D.

University of Southern California

2018

Keywords:

Computational methods • Machine learning • Manifolds • Uncertainty Quantification

Recent Publications   (More Publications)

1. Zhang, R; Ghanem, R, Drivers Learn City-Scale Intra-Daily Dynamic Equilibrium, Ieee Transactions on Intelligent Transportation Systems (January, 2022), pp. 1-10, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
2. Zhang, R; Mak, S; Dunson, D, Gaussian Process Subspace Regression for Model Reduction (July, 2021)  [abs]
3. Zhang, R; Ghanem, R, Normal-Bundle Bootstrap, Siam Journal on Mathematics of Data Science, vol. 3 no. 2 (January, 2021), pp. 573-592, Society for Industrial & Applied Mathematics (SIAM) [doi]
4. Zhang, R; Ghanem, R, Multi-market Oligopoly of Equal Capacity (December, 2020)  [abs]
5. Zhang, R; Ghanem, R, Demand, Supply, and Performance of Street-Hail Taxi, Ieee Transactions on Intelligent Transportation Systems, vol. 21 no. 10 (October, 2020), pp. 4123-4132, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]