weinkauf/teaching/visualization10

Spring Semester 2010

• New York University
  Visualization
  G22.3033-006

• Instructor: Tino Weinkauf, weinant.nyu.edu
• Semester: Spring 2010
• Time: Thursdays 7:10 - 9:00 pm
• Location: 715/719 Broadway, 12th floor, Room 1221
• Office hours: Tuesdays 6:00 - 7:00 pm, 715 Broadway, Room 1207 (other appointment times can be coordinated via e-mail)
• Class materials: Available for download (you need your NYU Net ID and password or CIMS account information). The lecture slides are available immediately after the lecture. See also below in the schedule section.
• Mailing list: Every student should subscribe to the mailing list.

Announcements

Feb 05: First homework assignment is due on Feb 18, before the class. You can send me your Amira files by email and give me the paperwork before the class.

Jan 21: The first lecture and the software tools are online. If possible, please install the tools until next week.

Jan 17: Details about the course are online.

This page will be updated during the semester.

Overview

Large amounts of data are produced every day in a variety of domains such as engineering, medicine, natural sciences, or meteorology. Due to the ever increasing size and complexity of simulated and measured data, its analysis becomes more challenging. A successful approach to this is data visualization, i.e., the creation of images or videos which allow for a fast and intuitive identification of the most important properties inherent to the data.

See some visualizations for yourself (these videos have been created with Amira by me and my colleagues at Zuse Institute Berlin):

The course gives an overview of the most important approaches to data visualization and discusses their advantages and limits. The necessary mathematical tools will be presented along the way (these include topics in numerical mathematics and topology). A state-of-the-art visualization system will be used to examine real-world data sets coming from the fields of medicine and fluid dynamics.

Covered topics:

• Data Description and Selection
• Mapping of Data to Graphics
• Visualization of Multiparameter Data
• Volume Visualization
• Flow Visualization
• Tensor Visualization
• Information Visualization
• Topological Data Analysis

Expected Work

The course is suitable for advanced MS students and PhD students. Students will analyze, visualize, and interpret data sets from different domains using a state-of-the-art visualization system. Familiarity with basic computer graphics (or motivation to learn this fast) is desirable. Assessment will be based on several homework assignments, a paper presentation and a course project.

Structure of the Course

Every session will consist of two parts:

• Lecture part: I will present visualization algorithms and principles together with their mathematical foundations. You will be able to download my slides and videos after the session in the class material section of this website.
• Interactive part: I show you how to visualize data sets in praxis, you present and we discuss your solutions of the homework assignments, paper presentations, and so on. In particular, the following is planned for the interactive part:
  • Visualization in praxis: I show you how to use visualization tools in praxis. You will be able to reproduce my steps when you bring your laptop and install the software tools. I will provide you with the data sets used in these sessions.
  • Discussion of homework assignments: In a rather informal atmosphere, a student presents his/her solution to a homework assignment. We will discuss the proposed and alternative solutions to the problem at hand. This is to ensure that you actually learn something from the assignments. Your written solutions have to be handed in before this discussion. I will score them after this discussion and factor in possible misunderstandings or alternative approaches that came up during the discussion.
  • Paper presentation: Presentation skills are of high importance to your future career. Therefore, every student will present one research paper in a rather formal 20 minutes talk. I will compile a list of suitable papers and you can pick a paper you are most interested in. After the talk we will discuss the paper.
  • Presentation of the course project: You present your course project in a 10-15 minutes talk. This will be in the last session on 04/29/10.
  • Discussion of the two special homework assignments: We discuss the Top Scientific Visualization Research Problems and our examples of "Visualization lies". See also below.

Homework Assignments

Typical assignments consist of a small number of problems that you are supposed to solve in order to deepen your understanding of the presented algorithms and techniques. I will hand out 5 such assignments. They will not only consist of theoretical questions, but some practical work with a visualization tool will be required as well. Estimated required time to solve a homework assignment is 2 hours.

There are two special homework assignments, that are special in the sense that they do not follow the typical question-answer scheme. Another special facet about them is that I have to do them, too. ;-) Here they are:

• Visualization lies: Visualizations sometimes (willingly or unwillingly) "distort" the truth. Over the course of the semester we will keep our eyes open for such visualizations while reading newspapers, magazines, or websites. In one of the last sessions we will discuss our findings.
• Top Scientific Visualization Research Problems: Chris Johnson wrote in 2004 an article about the top research problems in scientific visualization. Everyone reads this short article and we discuss it in one of the last sessions. What problems do you consider to be most important? Which unsolved problem affects your own research the most, i.e., would solving one of these problems boost your own research?

Course Project

The course project will be a visualization of a large and complex data set. You have to choose the right tools to uncover the hidden structures in the data set. You write a webpage incorporating your visualization images and videos, where you explain your choice of visualization techniques and interpret your results.

The following data sets are available for this:

• Hurricane Isabel data set: A hurricane simulation with over 12 variables.
• Visible human: A medical volume data set.
• Your own data set: Visualize a data set from your research project. (I have to agree to your choice since I need to ensure it is of sufficient complexity and size.)

You have to decide on a data set before spring break.

Literature

• Alexandru Telea, Data Visualization: Principles and Practice, A K Peters Ltd, 2007, ISBN 978-1568813066, 502 pages
• Charles Hansen and Chris R. Johnson (eds.), The Visualization Handbook, Academic Press, 2004, ISBN: 978-0123875822, 984 pages
• Proceedings of IEEE Visualization Conferences 1990-2009
• Proceedings of EuroVis/VisSym 1999-2009

Schedule

The schedule is tentative and may be adjusted along the way.