2022-2025 - Kavli Foundation (personal posdoctoral research grant) - Data-driven Control of Neural Dynamics as a Tool for Understanding Resilience to Environmental Temperature Change

T. B. Burghi, M. Ivanova, H. Wang, E. Marder, and T. O’Leary. “Rapid, interpretable, data-driven models of neural dynamics using Recurrent Mechanistic Models”. Proceedings of the National Academy of Sciences (PNAS). In print, accepted June 2025. Preprint available on
https://www.biorxiv.org/content/10.1101/2024.10.10.617633v2.

T. B. Burghi, K. Schapiro, M. Ivanova, H. Wang, E. Marder, and T. O’Leary. “Recurrent data-driven models quantitatively predict intracellular dynamics in a neural circuit”. Frontiers in Computational Neuroscience. In print, accepted July 2025.

T. B. Burghi and R. Sepulchre. “Adaptive Observers for Biophysical Neuronal Circuits”. IEEE Transactions on Automatic Control 69.8 (2024), pp. 5020–5033.

T. B. Burghi, J. G. Iossaqui, and J. F. Camino. “A General Update Rule for Lyapunov-Based Adaptive Control of Mobile Robots with Wheel Slip”. International Journal of Adaptive Control and Signal Processing 38.4 (2024), pp. 1172–1198.

T. Anttonen, T. B. Burghi, L. Duvall, M. P. Fernandez, G. Gutierrez, C. M. F. Kermen, and A. Michaiel. “Neurobiology and Changing Ecosystems: Mechanisms Underlying Responses to Human-Generated Environmental Impacts”. Journal of Neuroscience 43 (45 Nov. 2023).

C. Grussler, T. B. Burghi, and S. Sojoudi. “Internally Hankel k-Positive Systems”. SIAM Journal on Control and Optimization 60.4 (2022), pp. 2373–2392. eprint: https://doi.org/10.1137/21M1404685.

R. Schmetterling, T. B. Burghi, and R. Sepulchre. “Adaptive conductance control”. Annual Reviews in Control 54 (2022), pp. 352–362. issn: 1367-5788. doi: https://doi.org/10.1016/j.arcontrol.2022.07.005.

C. Toschi, M. H. Sayed, T. B. Burghi, T. Sell, P. Moazen, L. Huang, U. Gether, T. W. Robbins, and J. W. Dalley. “Dissociating reward sensitivity and negative urgency effects on impulsivity in the five-choice serial reaction time task”. Brain and Neuroscience Advances 6 (2022), p. 23982128221102256.

T. B. Burghi, M. Schoukens, and R. Sepulchre. “Feedback identification of conductance-based models”. Automatica 123 (Jan. 2021), p. 109297. url: https://doi.org/10.1016/j.automatica.2020.109297.

Peer-reviewed conference papers

C. Grussler and T. B. Burghi. “On the monotonicity of frequency response gains”. In: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC). Singapore, Singapore, Dec. 2023, pp. 1686–1691.

R. Schmetterling, T. B. Burghi, and R. Sepulchre. “Robust online estimation of biophysical neural circuits”. In: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC). Singapore, Singapore, Dec. 2023, pp. 703–708.

T. B. Burghi, T. O’Leary, and R. Sepulchre. “Distributed online estimation of biophysical neural networks”. In: Proceedings of the 61st IEEE Conference on Decision and Control (CDC). Cancun, Mexico, Dec. 2022, pp. 628–634.

J. G. Lee, T. B. Burghi, and R. Sepulchre. “Open-loop contraction design”. In: Proceedings of the 61st IEEE Conference on Decision and Control (CDC). Cancun, Mexico, Dec. 2022, pp. 7339–7345.

J. G. Lee and T. B. Burghi. “Funnel control by induced contraction”. In: 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS). Bayreuth, Germany, Sept. 2022, pp. 248–253.

T. B. Burghi, M. Schoukens, and R. Sepulchre. “System identification of biophysical neuronal models”. In: 59th IEEE Conference on Decision and Control. Jeju Island, South Korea, Dec. 2020, pp. 6180–6185.

T. B. Burghi, M. Schoukens, and R. Sepulchre. “ Feedback identification of conductance-based models”. Automatica 123, Article 109297, January 2021.

J. G. Lee, T. B. Burghi, and R. Sepulchre. “Induced contraction”. Under review, 61st IEEE Conference on Decision and Control (CDC). Mar. 2022.

J. G. Lee and T. B. Burghi. “Funnel control by induced contraction”. Under review, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS). Feb. 2022.

T. B. Burghi, M. Schoukens, and R. Sepulchre. “ Feedback for Nonlinear System Identification”. 18th European Control Conference (ECC 2019) – June 2019, Naples, Italy.

T. B. Burghi and Rodolphe Sepulchre. “Identification of Excitable Neuronal Systems”. 27th Annual Computational Neuroscience Meeting (CNS*2018) – July 2018, Seattle, WA, United States.

Thiago Burghi

Thiago Burghi

T. Burghi, M. Schoukens and R. Sepulchre. “Feedback for Nonlinear System Identification”. 18th European Control Conference (ECC 2019) – June 2019, Naples, Italy.

T. Burghi, M. Schoukens and R. Sepulchre. “Feedback identification of conductance-based models”. Provisionally accepted for publication at Automatica, April 2020. Available at https://arxiv.org/abs/2002.09626

Conference papers

T. Burghi, M. Schoukens and R. Sepulchre. “Feedback for Nonlinear System Identification”. Proceedings of the European Control Conference (ECC 2019) – June 2019, Naples, Italy.

T. Burghi, J. Iossaqui, J. Camino, “Stability analysis of perturbed nonlinear systems applied to the trajectory control of a wheeled mobile robot subject to lateral slip ”. Proceedings of the 17^th International Symposium on Dynamic Problems of Mechanics, Natal, Brazil, 2015.

Conference posters

“Identification of Excitable Neuronal Systems”. 27th Annual Computational Neuroscience Meeting (CNS*2018) – July 2018, Seattle, WA, United States.

Talks

“Identification of Excitable Neuronal Systems”. 27th European Research Network on System Identification (ERNSI) Workshop – September 2018, Cambridge, United Kingdom.

Thiago Burghi

2012: Diplôme d'Ingénieur (École Nationale Supérieure de Techniques Avancées, France)

2014: Msc Mechanical Engineering (University of Campinas, Brazil)