activity
19982026
most citedGlobal entrainment of transcriptional systems to periodic inputs

216 citations · 689 across the 90 of their papers we have counts for

collaborators

95 papers

eess.SY2026

Discrete-time polynomial systems with inputs and outputs: structure, reachability, observability, and minimal realizations

Eduardo D. Sontag

We study the realization problem for discrete-time input/output polynomial systems. These are formalized using tools from commutative algebra and algebraic geometry as systems whos…

cs.LG2023★ 2 cited

On the ISS Property of the Gradient Flow for Single Hidden-Layer Neural Networks with Linear Activations

Arthur Castello B. de Oliveira, Milad Siami, Eduardo D. Sontag

Recent research in neural networks and machine learning suggests that using many more parameters than strictly required by the initial complexity of a regression problem can result…

math.DS2022★ 5 cited

An observability result related to active sensing

Eduardo D. Sontag, Debojyoti Biswas, Noah J. Cowan

For a general class of translationally invariant systems with a specific category of nonlinearity in the output, this paper presents necessary and sufficient conditions for global…

math.OC2021

Remarks on input to state stability of perturbed gradient flows, motivated by model-free feedback control learning

Eduardo D. Sontag

Recent work on data-driven control and reinforcement learning has renewed interest in a relative old field in control theory: model-free optimal control approaches which work direc…

math.OC2021

Long-term regulation of prolonged epidemic outbreaks in large populations via adaptive control: a singular perturbation approach

M. Ali Al-Radhawi, Mahdiar Sadeghi, Eduardo D. Sontag

Initial hopes of quickly eradicating the COVID-19 pandemic proved futile, and the goal shifted to controlling the peak of the infection, so as to minimize the load on healthcare sy…

cs.LG2020★ 4 cited

Learning Recurrent Neural Net Models of Nonlinear Systems

Joshua Hanson, Maxim Raginsky, Eduardo Sontag

We consider the following learning problem: Given sample pairs of input and output signals generated by an unknown nonlinear system (which is not assumed to be causal or time-invar…