Biological Control Lab

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 Science32.6 (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.

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