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Field Course in Computational
Ecology

Wild animals at Mpala Research Centre.

Welcome to the website for the Field Course in Computational Ecology! On this page you can find the important information about the course. To learn more, use the menu on the left side of the page.

Instructors:

Tanya Berger-Wolf (UIC)
Jason Leigh (UIC)
Daniel Rubenstein (Princeton)
Iain Couzin (Princeton)

Course Description

A unique highly integrated field course is offered in Kenya (at the Mpala Research Centre) where US biology (PhD from Princeton University) and engineering students (PhD from UIC) will work with faculty in both disciplines to learn how to ask questions, frame hypotheses and understand how and why the disciplines and cultures do this differently.

The course will consist of three stages:

  1. Interdisciplinary background preparation during the Fall 2011 semester, where students will learn the key concepts and approaches from biology, computer science, and engineering.
  2. Project design and field work in Kenya, at Mpala Research Centre from January 5, 2012 to January 24, 2012.
  3. Project implementation and integration during the Spring 2012 semester, culminating in a conference of student presentations.
This is a highly integrated interdisciplinary field course that builds on the success of a pilot course conducted in an abbreviated format in Spring 2010.

The course is funded by National Science Foundation Award Number 1152895.

Images from field research in Kenya in Spring 2010.

Syllabus

Level Topic Instructors
I, X Introduction to population biology and ecology Dan Rubenstein (P), Mosheh Wolf (UIC), Joel Brown (UIC)
A Game theory and population dynamics Dan Rubenstein (P), Mosheh Wolf (UIC), Joel Brown (UIC)
A, X Behavioral ecology and social interaction Dan Rubenstein (P), Mosheh Wolf (UIC), Joel Brown (UIC)
I Computational thinking Tanya Berger-Wolf (UIC), Sanjeev Arora (P)
A Collective behavior Iain Couzin (P)
A Agent-based modeling Moira Zellner (UIC)
A, X Network analysis Tanya Berger-Wolf (UIC)
I, X Machine learning, statistical analysis, and data mining Barbara Di Eugenio (UIC), Bryan Pardo (Northwestern)
I, X Visualization Jason Leigh (UIC), Andrew Johnson (UIC)

Legend:

  • I: Introductory version of the topic given to out-of-discipline students.
  • X: Advanced version of the topic given to in-discipline students.
  • A: Version of the topic taught to all students.
Potential instructors are indicated next to each topic.
Topic attachments
I Attachment Action Size Date Who Comment
JPEGjpeg image001.jpeg manage 33.0 K 2011-09-23 - 05:47 UnknownUser  
JPEGjpeg image002.jpeg manage 32.8 K 2011-09-23 - 05:47 UnknownUser  
JPEGjpeg image003.jpeg manage 37.3 K 2011-09-23 - 05:47 UnknownUser  
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Topic revision: r8 - 2011-10-03 - 08:33:37 - Main.tanyabw
 
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