1. F. Bullo. Lectures on Network Systems. Kindle Direct Publishing, 1.7 edition, April 2024. ISBN: 978-1986425643. [bibtex-entry]

1. V. Centorrino, F. Bullo, and G. Russo. Modelling and Contractivity of Neural-Synaptic Networks with Hebbian Learning. Automatica, 164:111636, 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



2. V. Centorrino, A. Davydov, A. Gokhale, G. Russo, and F. Bullo. On Weakly Contracting Dynamics for Convex Optimization. IEEE Control Systems Letters, March 2024. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



3. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Positive Competitive Networks for Sparse Reconstruction. Neural Computation, January 2024. Note: To appear. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



4. O. Dalin, R. Ofir, E. Bar Shalom, A. Ovseevich, F. Bullo, and M. Margaliot. Verifying $k$-Contraction without Computing $k$-Compounds. IEEE Transactions on Automatic Control, 69(3):1492-1506, 2024. Keyword(s): Contraction Theory. [bibtex-entry]



5. A. Davydov and F. Bullo. Exponential Stability of Parametric Optimization-Based Controllers via Lur'e contractivity. IEEE Control Systems Letters, 2024. Note: Submitted. Keyword(s): Contraction Theory. [bibtex-entry]



6. A. Davydov and F. Bullo. Perspectives on Contractivity in Control, Optimization and Learning. IEEE Control Systems Letters, April 2024. Note: Submitted. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



7. G. De Pasquale, K. D. Smith, F. Bullo, and M. E. Valcher. Dual Seminorms, Ergodic Coefficients, and Semicontraction Theory. IEEE Transactions on Automatic Control, 69(5):3040-3053, 2024. Note: To appear. Keyword(s): Contraction Theory. [bibtex-entry]



8. Z. Marvi, F. Bullo, and A. G. Alleyne. Control Barrier Proximal Dynamics: A Contraction Theoretic Approach for Safety Verification. IEEE Control Systems Letters, 2024. [bibtex-entry]



9. W. Ye, F. Bullo, N. E. Friedkin, and A. K. Singh. Computational Models for Human-AI Team Decision Making. ACM Transactions on Computer-Human Interaction, 2024. Note: Available at http://arxiv.org/abs/2201.02759. [bibtex-entry]

1. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Biologically Plausible Neural Networks for Sparse Reconstruction: A Normative Framework. In Workshop “Mathematics for Artificial Intelligence and Machine Learning�, Milan, Italy, january 2024. Note: Oral Presentation. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



2. V. Centorrino, A. Gokhale, A. Davydov, G. Russo, and F. Bullo. Towards a Top/Down Normative Framework for a Biologically Plausible Explanation of Neural Circuits: Application to Sparse Reconstruction Problems. In 5th International Convention on the Mathematics of Neuroscience and AI, Rome, Italy, May 2024. Keyword(s): Contraction Theory, Neural Networks. [bibtex-entry]



3. Y. John, C. Hughes, G. Diaz-Garcia, J. Marden, and F. Bullo. RoSSO: A High-Performance Python Package for Robotic Surveillance Strategy Optimization Using JAX. In IEEE Int. Conf. on Robotics and Automation, Yokohama, Japan, May 2024. Note: To appear. Keyword(s): Robotic Surveillance. [bibtex-entry]

1. S. Jaffe, A. Davydov, D. Lapsekili, A. K. Singh, and F. Bullo. Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees, 2024. [bibtex-entry]

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