Francesco Bullo
University of California, Santa Barbara
520 Papers
6K Citations
Francesco Bullo is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Computer science & Distributed algorithm. The author has an hindex of 81, co-authored 484 publications. Previous affiliations of Francesco Bullo include California Institute of Technology & University of California.
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Papers
Distributed Control of Robotic Networks
Francesco Bullo
- 06 Jul 2009
TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
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Expertise and confidence explain how social influence evolves along intellective tasks.
Omid Askarisichani,Elizabeth Y. Huang,Kekoa S. Sato,Noah E. Friedkin,Francesco Bullo,Ambuj K. Singh +5 more
TL;DR: A cognitive dynamical model inspired by transactive memory systems, social comparison, and confidence heuristics is proposed to describe the process by which individuals adjust interpersonal influences over time and is able to accurately predict individuals' self-reported influence over time.
Novel results on slow coherency in consensus and power networks
Diego Romeres,Florian Dörfler,Francesco Bullo +2 more
- 17 Jul 2013
TL;DR: This work provides a complete analysis of the second-order swing dynamics - without restrictive assumptions on the system damping - of first-order consensus systems and second- order power system swing dynamics.
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Dynamic Models of Appraisal Networks Explaining Collective Learning
TL;DR: The authors proposed models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence.
Coordinated deployment of mobile sensing networks with limited-range interactions
Jorge E. Cortes,Sonia Martinez,Francesco Bullo +2 more
- 17 Dec 2004
TL;DR: A comprehensive smoothness analysis of a class of locational optimization functions and a discrete-time convergence result based on a recently-developed generalized statement of LaSalle invariance principle for coordination algorithms for groups of mobile agents performing deployment and coverage tasks.