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Robustness and the Internet: Design, Evolution, and Theoretical Foundations
Organizers:
Walter
Willinger
AT&T Labs - Research
walter@research.att.com
http://www.research.att.com/~walter/
and
John Doyle
California Institute of Technology
doyle@cds.caltech.edu
http://www.cds.caltech.edu/~doyle/home.htm
This tutorial uses the Internet as starting point for a scientific exploration of the broader issues of robustness in complex systems throughout technology and biology. In most of these systems, complexity is driven by the need for robustness to uncertainty in their environments and components far more than by basic functionality. At the same time, most of this complexity tends to be hidden, deliberately creating the illusion of superficially simple systems, which has encouraged the development of specious theories.
The objective of this tutorial is to outline an emerging theoretical foundation for the Internet that provides a sound framework for understanding both success and shortcomings of existing Internet technologies, offers alternative protocols for identified problems, guides the rational design for future evolution of ubiquitous networking, and suggests what new mathematics and technology will be needed for developing a useful, general theory of complex systems.
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| SUNDAY,
FEBRUARY 8 All talks are in Lecture Hall EE/CS 3-180 unless otherwise noted. |
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| 9:00-10:00 | John
Doyle California Institute of Technology |
Biology 101 for Networking Researcher: The Biological Internet I |
| 10:30-11:30 | John
Doyle California Institute of Technology |
Biology 101 for Networking Researcher: The Biological Internet II |
| 1:30-2:30 | Stephen
Prajna California Institute of Technology |
Robustness in Complex Systems: Theoretical Foundations I |
| 3:00-4:00 | Antonis
Papachristodoulou California Institute of Technology |
Robustness in Complex Systems: Theoretical Foundations II |
Abstracts
Antonis Papachristodoulou (California Institute of Technology) antonis@its.caltech.edu
Robustness in Complex Systems: Theoretical Foundations II
Ordinary or Functional differential equations with uncertain parameters can be used to model a variety of systems. Analysis usually proceeds by further simplification to the investigation of the linearizations of these models, or a series of assumptions that result in conservativeness or may be misleading. This methodology offers scalability, but the conclusions are only locally correct. Investigating the properties of the system at the nonlinear level with delays is usually cumbersome. Using the Sum of Squares decomposition, we will build a framework for the algorithmic analysis of nonlinear ordinary and functional differential equations, taking examples from network congestion control.
LIST OF CONFIRMED PARTICIPANTS
NAME DEPARTMENT AFFILIATION Scot Adams Institute for Mathematics and its Applications University of Minnesota Soohan Ahn Department of Statistics Seoul National University David Alderson Department of Computer Science California Institute of Technology Greg Anderson School of Mathematics University of Minnesota Douglas Arnold Institute for Mathematics and its Applications University of Minnesota Donald Aronson Institute for Mathematics and its Applications University of Minnesota Gerard Awanou Institute for Mathematics and its Applications University of Minnesota Karen Ball Institute for Mathematics and its Applications University of Minnesota Antar Bandyopadhyay Institute for Mathematics and its Applications University of Minnesota Maury Bramson School of Mathematics University of Minnesota Olga Brezhneva Institute for Mathematics and its Applications University of Minnesota Hi Jun Choe Department of Mathematics Yonsei University Wanyang Dai Department of Mathematics Nanjing University John Doyle Department of Control and Dynamical Systems California Institute of Technology Philip Fleming Network Advanced Technology Motorola Shmuel Friedland Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Tim Garoni Institute for Mathematics and its Applications University of Minnesota Martin Greiner Corporate Technology Department CT IC4 Siemens Tom Haigh Adventium Labs Chuan-Hsiang Han Institute for Mathematics and its Applications University of Minnesota Eric Harder Office of Defense Computing Research Department of Defense Naresh Jain School of Mathematics University of Minnesota Ramesh Johari Laboratory for Information and Decision Systems Massachusetts Institute of Technology Lili Ju Institute for Mathematics and its Applications University of Minnesota Herve Kerivin Institute for Mathematics and its Applications University of Minnesota Mohammad Khan Department of Mathematics Kent State University Dohyun Kim Department of Statistics Seoul National University Hye-Ryoung Kim Seoul National University Devdatta Kulkarni University of Minnesota Thomas Kurtz Department of Mathematics University of Wisconsin-Madison Lun Li Department of Electrical Engineering California Institute of Technology Zhuoqing Mao Department of Electrical Engineering and Computer Science University of Michigan Richard McGehee School of Mathematics University of Minnesota Haewon Nam Institute of Mathematics and Statistics University of Minnesota Amir Niknejad Department of Mathematics University of Illinois at Chicago Antonis Papachristodoulou Department of Control and Dynamical Systems California Institute of Technology Pablo Parrilo Automatic Control Laboratory Eidgenössische TH Zürich-Zentrum Lea Popovic Institute for Mathematics and its Applications University of Minnesota Stephen Prajna Department of Control and Dynamical Systems California Institute of Technology Grzegorz Rempala Department of Mathematics University of Louisville Fadil Santosa Institute for Mathematics and its Applications University of Minnesota Arnd Scheel School of Mathematics University of Minnesota Tamon Stephen Institute of Mathematics and its Application University of Minnesota Hui Wang Division of Applied Mathematics Brown University Jing Wang Institute for Mathematics and its Applications University of Minnesota Walter Willinger Statistics Research AT&T Laboratories - Research Yuhong Yang Department of Statistics Iowa State University Ofer Zeitouni School of Mathematics University of Minnesota Lixia Zhang Department of Computer Science University of California, Los Angeles (UCLA) Jun Zhao Institute of Mathematics and its Application University of Minnesota
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