# Networks

Wednesday, May 11, 2016 - 11:15am - 12:00pm

Victor Zavala (University of Wisconsin, Madison)

Energy networks are becoming increasingly decentralized and exhibit new forms of coupling. For instance, during the polar vortex of 2014, sustained low temperatures in the Midwest region of the U.S. resulted in unusually high gas demands from buildings in urban areas. This led to shortages of natural gas that propagated to California, Massachusetts, and Texas. The gas shortages forced power plant shutdowns totaling 35 GW. At a value of lost load of 5,000 USD/MWh, such shortages represent economic losses of 175 million USD per hour.

Thursday, May 1, 2014 - 10:15am - 11:05am

Ginestra Bianconi (Queen Mary and Westfield College)

A large variety of complex systems, from the brain to the weather networks and complex infrastructures, are formed by several networks that coexist, interact and coevolve forming a network of networks. Modeling such multilayer structures and characterizing the rich interplay between their structure and their dynamical behavior is crucial in order to understand and predict complex phenomena. In this talk I will present recent works on statistical mechanics of multiplex networks. Multiplex networks are formed by N nodes linked in different layers by different networks.

Tuesday, April 28, 2009 - 7:00pm - 8:00pm

Albert-László Barabási (Northeastern University)

Systems as diverse as the world wide web, Internet or the cell are described by highly interconnected networks with amazingly complex structure. Recent studies indicate that the evolution of these complex networks is governed by simple but generic laws, resulting in apparently universal architectural features. I will discuss this amazing order characterizing our interconnected world, and its implications to how we perceive the impact on communications and medicine.

Thursday, January 22, 2009 - 7:00pm - 8:30pm

Robert Ghrist (University of Pennsylvania)

Sensor networks are poised to impact society in fundamental ways analogous to the impact of the networked personal computers. The rapid development of small-scale sensors coupled with wireless ad hoc networking capability foreshadows a day when our physical surroundings will wake up with sensory data, assuming it does not drown in the data first. In this lecture, Professor Ghrist will describe a recent calculus for sensor network data, whose origins lie in the century-old theory of algebraic topology.

Tuesday, September 19, 2006 - 3:00pm - 3:50pm

Michael Stillman (Cornell University)

he reverse engineering of biological networks is an important and

interesting problem. Two examples of such networks are gene

regulatory networks, and the relationship of voxels in the brain. We

describe a method for determining possible wiring diagrams for such

networks. The method is based on computational algebra, and a key

part of the method uses computations involving monomial ideals in a

polynomial ring. To illustrate the algorithms, we apply the method

to data coming from fMRI scans of the brain.

interesting problem. Two examples of such networks are gene

regulatory networks, and the relationship of voxels in the brain. We

describe a method for determining possible wiring diagrams for such

networks. The method is based on computational algebra, and a key

part of the method uses computations involving monomial ideals in a

polynomial ring. To illustrate the algorithms, we apply the method

to data coming from fMRI scans of the brain.

Monday, March 3, 2014 - 9:00am - 9:50am

Sanjeevi Krishnan (University of Pennsylvania)

Homology on semimodule-valued sheaves naturally generalizes network flows from the setting of numerical capacity constraints to other sorts of constraints (e.g. stochastic, multicommodity). In this talk, we present new work relating the algebraic structure of flows with local network properties and algebraic properties of the ground semiring.

Wednesday, September 5, 2012 - 2:00pm - 3:00pm

Edward Ott (University of Maryland)

We consider Boolean models of the dynamics of interacting genes. Stability is defined for a large Boolean network by imagining two system states that are initially close in the sense of Hamming distance and asking whether or not their evolutions lead to subsequent divergence or convergence.

Tuesday, February 28, 2012 - 11:15am - 12:00pm

Natasha Przulj (Imperial College London)

Sequence-based computational approaches have revolutionized biological understanding. However, they can fail to explain some biological phenomena. Since proteins aggregate to perform a function instead of acting in isolation, the connectivity of a protein interaction network (PIN) will provide additional insight into the inner working on the cell, over and above sequences of individual proteins. We argue that sequence and network topology give insights into complementary slices of biological information, which sometimes corroborate each other, but sometimes do not.