2011-12 Calendar of Events

Michael Macy

Guest Lecturer: Michael Macy

Date: Thursday, September 26

Time: 7:00 pm

Where: ARTS 150

The Relational Revolution: How Digital Records of Human Interactions are Transforming Social Science


 Guest Lecture and Book Signing by Noted Author: Melanie Mitchell

Melanie Mitchell  Complexity A Guided Tour
Complexity: A Guided Tour 

Guest Lecturer: Melanie Mitchell

Date: Thursday, October 20

Time: 7:00 pm

Where: ARTS 150
How to Understand Pictures (If You are a Computer)

Guest Lecturer: Melanie Mitchell

Date: Friday, October 21

Time: 12-1 pm 

Where: CPISB 120

Tea and Conversation with Melanie Mitchell: A woman with Complexity

Date: Friday, October 21

Time: 3:00 to 4:30 pm

Where: UAA Campus Bookstore


Melanie Big Sur 2010

Guest Lecturer: Melanie Moses

Date: Thursday, October 13th

Time: 7:00 p.m.

Where: ARTS 150

“Network Scaling:How size determines the growth and behavior of organisms and societies”

Scaling properties of networks that deliver energy and information within industrial societies can affect the behavior of people living in those societies. Scaling theory offers the perspective that human life spans, reproductive choices, and economic structures may be constrained by the way that energy flows through networks in modern societies.

Dr. Moses is an Assistant Professor in the Department of Computer Science at the University of New Mexico, with a joint appointment to the Department of Biology.  She concentrates on scaling properties of biological, social, and information networks, and the general rules governing the acquisition and efficiency of energy and information exchanges in complex adaptive systems. 

 


“Search Algorithms from Ant Colonies to Robotic Swarms”

Guest Lecturer: Melanie Moses

Date: Friday, October 14th

Time: Noon

Where: CPISB 120

Successful search by a colony is an emergent property of the behaviors and interactions of individual ants. Computer simulations demonstrate how pheromone communication and memory of individual ants contribute to successful search by a colony.  Genetic algorithms ‘evolve’ successful search strategies in simulated ants and these successful strategies are implemented as algorithms to develop intelligent robot swarms capable of effectively searching for resources in a variety of environments.