Analysis of complex heterogeneous networks: scalability, robustness and fundamental limitations.

Abstract:
Complex networks are receiving an increasing attention by various scientific communities, as a result of their
significance and enormous impact in both natural and man made systems. Such examples could range from
Internet protocols, power networks, vehicle platoons to flocking phenomena and gene regulatory networks. On the
one hand it is important to understand the fundamental principles and theories behind the success of networks
present in nature, but, at the same time, there is an urgent need to develop methodologies that enable the design of
networkswhere robustness and scalability can be guaranteed. This talk is going to address such challenges by
presenting recently developed tools that can lead to scalable network designs, and by deriving fundamental
limitations in biological networks.
By scalability we refer to the requirement that robust stability is guaranteed for an arbitrary interconnection by
conditions on only local interactions, without having to redesign the whole network whenever a new
heterogeneous agent is added or removed. It would be, for example unrealistic to carry out a centralized analysis,
whenever a computer/router enters the Internet or a generator becomes part of a power network. We show in the
talk, how the new notion of an S-hull, a relaxed convexification in the complex plane, can lead to stability
certificates that are both decentralized and scalable. This creates an abstraction that is relevant in diverse
applications such as data network protocols and group coordination problems.
For the case of biological networks we focus on regulatory processes at the molecular level. These are well known
to be inherently stochastic, with a substantial part of the noise being intrinsic, arising from the random births and
deaths of individual molecules. We establish in the talk fundamental limitations for the suppression of intrinsic
noise in gene regulatory networks. These are hard bounds that hold for arbitrary feedback, being a result of simple
generic features of the underlying jump processes. Such features include causality in conjunction with limited
information, arising from the inevitable presence of delays and feedback channels with finite Shannon capacity.
Bio:
Ioannis Lestas graduated with a BA (Starred First) and an MEng (Distinction) in Electrical and Information
Sciences from the University of Cambridge (Trinity College). Prior to that he was chosen to participate in
International Mathematics, Physics, and Chemistry Olympiads (two bronze medals in International Chemistry
Olympiads and first prizes in several national mathematics and physics competitions). From 2002 to 2006 he was a
PhD student in the Control Group at the University of Cambridge as a Gates Scholar and Trinity College Research
Scholar. Since October 2006 he is a class A Fellow of Clare College, Cambridge (and a member of the University
governing body). These are prestigious appointments at Cambridge previously held by a number of Cambridge's
distinguished academics and Nobel laureates. His research interests lie within the area of complex networks,
focusing on the development of theoretical frameworks for addressing issues of scalability, robustness and
fundamental limitations, with applications in data networks, multiagent systems and biological networks.

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Shane Goodoff