Link Archive

A graphical technique for the display of the Lyapunov exponent as a function of control parameters in one-dimensional maps is used to identify coexistence of attractors, superstable curves, highly interleaved basins of attraction in phase space, and highly interleaved regions with different oscillatory modes in parameter space. Furthermore, this technique reveals dramatic' changes of dynamic behavior due to small discontinuities at the maximum of the maps.

This is an online preview of the book “Fundamentals of Data Visualization” to be published with O’Reilly Media, Inc.

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.

The Feynman trap—ransacking data for patterns without any preconceived idea of what one is looking for—is the Achilles heel of studies based on data mining. Finding something unusual or surprising after it has already occurred is neither unusual nor surprising. Patterns are sure to be found, and are likely to be misleading, absurd, or worse.

I've always been fascinated with patterns. It doesn't really matter what kind of patterns; I've played with networks, leaves and leaf venation, branches, lightning, flocking, tracing outlines of shapes, river formation, rock sediments, landscapes, slime mold, lichens,reaction-diffusion, cellular automaton, some fractals, and a few other things. I think what I enjoy the most is how complex and intricate results you can get from a set of simple rules.

At ITP, I teach a course entitled Introduction to Computational Media. In this course, the students learn the basics of programming (variables, conditionals, loops, objects, arrays) as well as a survey of applications related to making interactive projects (images, pixels, computer vision, networking, data, 3D). The course mostly follows the material found in my intro book Learning Processing; in many ways, The Nature of Code serves as a follow-up. Once you’ve learned the basics and seen an array of applications, your next step might be to delve deeply into a particular area. For example, you could focus on computer vision (and read a book like Greg Borenstein’s Making Things See). In the most basic sense, this book is one possible next step in a world of many. It picks up exactly where Learning Processingleaves off, demonstrating more advanced programming techniques with Processing that focus on algorithms and simulation.

This web page contains a free electronic version of my (soon to be) self-published textbook Algorithms, along with other lecture notes I have written for various theoretical computer science classes at the University of Illinois, Urbana-Champaign since 1998.