Hasnain, A., Boddupalli, N., Yeung, E. A data-driven Koopman framework for programming the steady state of biological systems with parametric uncertainty, submitted to the 2020 Proceedings of the IEEE American Control Conference. [preprint]
Liu, Z., Chen, L., Ding, G., and Yeung E. 2019 Towards scalable koopman operator learning: convergence rates and distributed implementation, submitted to the 2020 Proceedings of the IEEE American Control Conference. [preprint]
Sinha, S., Nandanoori, S., and Yeung, E. 2019. Online Learning of Dynamical Systems: An Operator Theoretic Approach, submitted to the 2020 Proceedings of the IEEE American Control Conference. [preprint]
Nandanoori S., Sinha, S., and Yeung E. 2019. Data-Driven Operator Theoretic Methods for Global Phase Space Learning, submitted to the 2020 Proceedings of the IEEE American Control Conference. [preprint]
Balakrishnan, S., Hasnain, A., Boddupalli, N., Joshy, D., and Yeung E. 2019. Prediction of the Growth Rate Population Dynamics of Bacteria by Causal Jump Dynamic Mode Decomposition, submitted to the 2020 Proceedings of the IEEE American Control Conference [preprint].
Khan, N., Yeung E., Farris, Y., Fansler, S., Bernstein, H. 2019. A broad-host event detector: expanding and quantifying performance across bacterial species. to appear in Synthetic Biology [preprint]
Stinis, P., Hagge, T., Tartakovsky, A.M. and Yeung, E., 2019. Enforcing constraints for interpolation and extrapolation in generative adversarial networks. Journal of Computational Physics, 397, p.108844.
Tschirhart, T., Shukla, V., Kelly, E.E., Schultzhaus, Z., NewRingeisen, E., Erickson, J.S., Wang, Z., garcia, W., Curl, E., Egbert, R.G. and Yeung, E., 2019. Synthetic Biology Tools for the Fast-Growing Marine Bacterium Vibrio natriegens. ACS synthetic biology.
Boddupalli, N., Hasnain, A. and Yeung, E., 2019. Persistence of Excitation for Koopman Operator Represented Dynamical Systems. to appear in the Proceedings of the 2019 IEEE Conference on Decision and Control.
Hasnain, A., Boddupalli, N. and Yeung, E., 2019. Optimal reporter placement in sparsely measured genetic networks using the Koopman operator. to appear in the Proceedings of the 2019 IEEE Conference on Decision and Control
Hasnain A., Sinha S., Dorfan Y., Borujeni A. E., Park Y., Maschhoff P., Saxena U., Urrutia J., Gaffney N., Becker D., Maheshri N., Gordon B., Voigt C., and Yeung E.. A Data-Driven Method for Quantifying the Impact of a Genetic Circuit on its Host. to appear in the Proceedings of the 2019 IEEE Conference on Biomedical Circuits and Systems Conference
Sinha S., Vaidya U., and Yeung E.. On Computation of the Koopman Operator from Sparse Data. Proceedings of the 2019 IEEE American Control Conference
Yeung E., Kundu S., and Hodas N.. Using Deep Neural Networks to Learn Koopman Operators for Nonlinear Dynamical Systems Proceedings of the 2019 IEEE American Control Conference
2018
Johnson, C. and Yeung E. A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators. Proceedings of the 2018 IEEE American Control Conference
Liu Z., Kundu S., Chen L. and Yeung E. Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians. Proceedings of the 2018 IEEE American Control Conference
Yeung E., Liu Z, and Hodas, N. A Koopman Operator Approach for Computing and Balancing Gramians For Discrete-Time Nonlinear Systems. Proceedings of the 2018 IEEE American Control Conference
You P., Pang J.Z. and Yeung E. Stabilization of Power Networks via Market Dynamics. Proceedings of the Ninth International Conference on Future Energy Systems
Maruf A., Kundu S., Yeung E., and Anghel M. Decomposition of Nonlinear Dynamical Networks via Comparison Systems. Proceedings of the 2018 European Control Conference, pp. 190-196.
You, P., Pang, J. and Yeung, E., 2018. Deep Koopman Controller Synthesis for Cyber-Resilient Market-Based Frequency Regulation. IFAC-PapersOnLine, 51(28), pp.720-725.
The Biological Control Lab 