Fabio Pasqualetti
@Fabiopas82
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Professor, Department of Mechanical Engineering, University of California, Riverside. Associate Director CRIS research center.
Orange County, California
Joined June 2014
RT @CSSIEEE: IEEE CSS Day 2024 is starting soon! With presentations and events from Monday Oct. 21st through Friday Oct. 25th. Register for…
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RT @FrancoLabUCLA: @UCLAMechAeroEng is hiring! We have 4 openings in the areas of 1) thermal science, 2) #mechanobiology, 3) space engineer…
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RT @ShiqiZhangPKU: Excited to announce our latest paper on PISA: a cutting-edge data-driven algorithm for predicting autonomous AI agents’…
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RT @ShiNungChing: Our piece on modeling astrocytic modulation of neural dynamics is online at @PLOSCompBiol! @GongLuke
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RT @YuzhenQin: Check our paper to see how a simple bistable model can capture the essence of epileptic dynamics. Networks of such bistable…
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Sometimes, good news comes on a Friday :) with @FrancescoBullo @KeremCamsari @DaniSBassett @adilson_motter @MillerLabMIT and other great colleagues!
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RT @IEEE_OJCSYS: Title: Vibrational Stabilization of Cluster Synchronization in Oscillator Networks Authors: Yuzhen Qin; Alberto Maria Nobi…
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Honored, humbled, and still in deep disbelief, to receive the 2023 IEEE CSS Antonio Ruberti Young Researcher Prize 😳 A big, BIG, shout out to my nominator and supporters, and to all my colleagues and friends! @CSSIEEE @IEEECDC2023
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A survey of our data-driven methods for LQ control is finally online 👇🏼with @FedericoCeli3 and Giacomo Baggio
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RT @ShiNungChing: We also have a new preprint! Bringing together dynamics, astrocytes, and RL. How might astrocytic slow time-scales and n…
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RT @IEEE_OJCSYS: ‼️👀There's still time to submit your paper! These OJ-CSYS Special Sections have been extended through May 15, 2023: Synchr…
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The LQG controller can be learned from offline open-loop data without knowing the system model and noise statistics. Here's how, together with error bounds and explicit data-driven formulas for the LQR/LQG gains and Kalman filter: @MakdahAbed
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Data-driven control for network and multi-agent systems, via finite-time distributed computation and with robustness guarantees 👇🏽 @FedericoCeli3
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Learning LQG controllers is easy when you have access to an optimal control sequence, and easier when you have seen a number of tasks. No need to know the system, the cost function, and the noise statistics! @GuoGuoGuo8600 @MakdahAbed @VishaalKrishna1
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RT @YuzhenQin: The outcome we experience now may depend on our choices a long time ago. How do we find out such long-horizon dependence eff…
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RT @FedericoCeli3: If you missed (🩳) my first #cdc2022 presentation today about data-driven optimal control design you can catch it here 📣h…
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RT @dgadginmath: Is the Koopman operator related to geometric control? We leverage the structure of the Koopman generator when a system is…
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RT @NatComputSci: We highlight a @NatureComms paper by @DaniSBassett, @Fabiopas82, and colleagues on controlling functional relations in os…
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RT @CSSIEEE: The CSS-Day platform is ready and open for registration: Please register (it's free) and share with y…
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