A current list of publications can be found on Enoch Yeung’s Google Scholar page, here.

### 2022

- Johnson, Charles A., Shara Balakrishnan, and Enoch Yeung. “Learning Invariant Subspaces of Koopman Operators–Part 1: A Methodology for Demonstrating a Dictionary’s Approximate Subspace Invariance.”
*arXiv preprint arXiv:2212.07358*(2022). - Johnson, Charles A., Shara Balakrishnan, and Enoch Yeung. “Learning Invariant Subspaces of Koopman Operators–Part 2: Heterogeneous Dictionary Mixing to Approximate Subspace Invariance.”
*arXiv preprint arXiv:2212.07365*(2022). - Nandanoori, Sai Pushpak, Subhrajit Sinha, and Enoch Yeung. “Data-driven operator theoretic methods for phase space learning and analysis.”
*Journal of Nonlinear Science*32.6 (2022): 1-35. - Balakrishnan, Shara, et al. “Data-driven observability decomposition with koopman operators for optimization of output functions of nonlinear systems.”
*arXiv preprint arXiv:2210.09343*(2022). - Kim, Jongmin, Juan F. Quijano, Jeongwon Kim, Enoch Yeung, and Richard M. Murray. “Synthetic logic circuits using RNA aptamer against T7 RNA polymerase.”
*Biotechnology Journal*17, no. 3 (2022): 2000449. - Eslami, Mohammed, Amin Espah Borujeni, Hamed Eramian, Mark Weston, George Zheng, Joshua Urrutia, Carolyn Corbet, Enoch Yeung, et al. “Prediction of whole-cell transcriptional response with machine learning.”
*Bioinformatics*38, no. 2 (2022): 404-409. - Hasnain, Aqib, Shara Balakrishnan, Dennis M. Joshy, Steven B. Haase, Jen Smith, and Enoch Yeung. “Learning transcriptome dynamics for discovery of optimal genetic reporters of novel compounds.”
*bioRxiv*(2022).

### 2021

- Harrison, Jamiree, and Enoch Yeung. “Stability Analysis of Parameter Varying Genetic Toggle Switches Using Koopman Operators.”
*Mathematics*9, no. 23 (2021): 3133. - Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, and Richard M. Murray. “Data-driven network models for genetic circuits from time-series data with incomplete measurements.”
*Journal of the Royal Society Interface*18, no. 182 (2021): 20210413. - Balakrishnan, Shara, Aqib Hasnain, Rob Egbert, and Enoch Yeung. “The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators.”
*arXiv preprint arXiv:2106.15091*(2021). - Sinha, Subhrajit, Umesh Vaidya, and Enoch Yeung. “On Few Shot Learning of Dynamical Systems: A Koopman Operator Theoretic Approach.”
*arXiv preprint arXiv:2103.04221*(2021). - Sinha, Subhrajit, Sai Pushpak Nandanoori, and Enoch Yeung. “Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach.” In
*2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)*, pp. 1-6. IEEE, 2020.

### 2020

- Sinha, Subhrajit, Sai Pushpak Nandanoori, and Enoch Yeung. “Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach.” In
*2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)*, pp. 1-6. IEEE, 2020. - Sinha, Subhrajit, Sai Pushpak Nandanoori, and Enoch Yeung. “Data driven online learning of power system dynamics.” In
*2020 IEEE Power & Energy Society General Meeting (PESGM)*, pp. 1-5. IEEE, 2020. - Balakrishnan, Shara, Aqib Hasnain, Nibodh Boddupalli, Dennis M. Joshy, Robert G. Egbert, and Enoch Yeung. “Prediction of fitness in bacteria with causal jump dynamic mode decomposition.” In
*2020 American Control Conference (ACC)*, pp. 3749-3756. IEEE, 2020. - Nandanoori, Sai Pushpak, Subhrajit Sinha, and Enoch Yeung. “Data-driven operator theoretic methods for global phase space learning.” In
*2020 American Control Conference (ACC)*, pp. 4551-4557. IEEE, 2020. - Hasnain, Aqib, Nibodh Boddupalli, Shara Balakrishnan, and Enoch Yeung. “Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition.” In
*2020 American Control Conference (ACC)*, pp. 4245-4251. IEEE, 2020. - Liu, Zhiyuan, Guohui Ding, Lijun Chen, and Enoch Yeung. “Towards scalable Koopman operator learning: Convergence rates and a distributed learning algorithm.” In
*2020 American Control Conference (ACC)*, pp. 3983-3990. IEEE, 2020. - Khan, Nymul, Enoch Yeung, Yuliya Farris, Sarah J. Fansler, and Hans C. Bernstein. “A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species.”
*Synthetic Biology*5, no. 1 (2020): ysaa002. - Sinha, Subhrajit, Sai Pushpak Nandanoori, and Enoch Yeung. “Koopman operator methods for global phase space exploration of equivariant dynamical systems.”
*IFAC-PapersOnLine*53, no. 2 (2020): 1150-1155.

### 2019

- 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.