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Pequito's research consists of understanding the global qualitative behavior of large-scale systems from their structural or parametric descriptions and provides a rigorous framework for the design, analysis, optimization, and control of large scale systems. Currently, his interests span to neuroscience and biomedicine, where dynamical systems and control theoretic tools can be leveraged to develop new analysis tools for brain dynamics towards effective personalized medicine and improve brain-computer and brain-machine-brain interfaces. Pequito was awarded the best student paper finalist in the 48th IEEE Conference on Decision and Control (2009) and the 2016 O. Hugo Schuck Award in the Theory Category by the American Automatic Control Council.
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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGYno. 99 (2024): 1-8
Automatica (2024): 111668
2023 AMERICAN CONTROL CONFERENCE, ACCpp.2228-2233, (2023)
M. Sibeijn,S. Pequito
IFAC PAPERSONLINEno. 2 (2023): 3856-3861
SYSTEMS & CONTROL LETTERS (2023): 105608-105608
IEEE Transactions on Automatic Controlno. 5 (2023): 3099-3106
Scientific reportsno. 1 (2023): 19502-13
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDCpp.8026-8031, (2023)
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