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Course Organization & Format Visualization II MSIM 842, CS 795/895

Course Organization & Format Visualization II MSIM 842, CS 795/895. Instructor: Jessica Crouch. Visualization. “A picture is worth a thousand words” Visual communication can be very effective very efficient if done well Visual communication can be worthless if done poorly.

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Course Organization & Format Visualization II MSIM 842, CS 795/895

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  1. Course Organization & FormatVisualization IIMSIM 842, CS 795/895 Instructor: Jessica Crouch

  2. Visualization “A picture is worth a thousand words” Visual communication can be very effective very efficient if done well Visual communication can be worthless if done poorly

  3. Some visualizations are more appealing and informative than others

  4. Course Content • The question: • How can we design algorithms that produce informative, intuitive, graphical representations of data? • No single answer exists (obviously) • Best design depends on • Type of information • What the user needs to learn from the visualization • Efficiency considerations

  5. Assumption / Pre-Requisite • Everyone here knows a bit about graphics and has some experience with graphics programming (OpenGL) • What is the difference between graphics and visualization?

  6. Assumption / Pre-Requisite • We’ll focus on designing visualizations and we’ll assume you have enough background in graphics to implement a design. • Background knowledge of graphics is important because issues of computational efficiency must be considered. • We could dream up NP-Complete algorithms that would produce lovely visualizations but would be useless from a practical standpoint. • If a visualization needs to be interactive, understanding how the graphics pipeline works is especially vital.

  7. Types of Information • How should this course be organized? • Could categorize according to application area: • Medical • Networks • Oceanographic • Astronomical • Transportation • Structural engineering • Fluid mechanics • Etc.

  8. Types of Information • Often it is more useful to categorize based on fundamental characteristics of the data • Dimensionality • Inherent spatial coordinates? • Time varying? • Scalar, vector, tensor properties? • Sparse vs. dense • Discrete vs. continuous

  9. Types of Information • Visualization algorithms and techniques generalize across application areas • Ex: Scalar fields can be visualized using color mapping • Works for temperature distribution over surface of the earth, for distribution of radiation over a slice of the brain, and for lots of other applications

  10. Course Organization • Visualization topics are roughly grouped by data type (with a couple exceptions) • Syllabus & tentative schedule are online • Assigned readings are online too • We’re reading research papers rather than a textbook • Most classes will consist of a presentation and discussion of 2 research papers

  11. Course Format • This is a Ph.D. level course • No higher level courses in visualization are offered at ODU • Objective is to familiarize you with up-to-date visualization research • Focus will be on currentresearch • Will provide an opportunity for you to • Learn what is currently going on in the field of visualization • Practice presenting research (even if someone else’s…) • You need this for conferences, defense, jobs, etc. • Critically evaluating research work • You’ll have to review other people’s papers and grant proposals, and you’ll need to know what to expect when other people review your work

  12. Paper presentations • Each student will present two papers during the semester • First task: peruse the papers online and prioritize them according to which ones you would prefer to present • Email me (jrcrouch@cs.odu.edu) your preferences by Sunday night. In your email represent each paper using the letter labels given on the schedule. • On Monday I will make assignments satisfying as many preferences as possible and post the presentation schedule online. • First two presenters then have 9 days to prepare.

  13. Course Format • Look at the website: www.cs.odu.edu/~jrcrouch/courses/msim842-s07 Write down username, password.

  14. How to prepare your presentations • Read the paper • Read background material to clarify anything in the paper that you don’t fully understand • Look up some of the references from the end of the paper • Use textbooks and other sources as necessary • Prepare a PowerPoint or pdf presentation and lecture to explain the paper • Send me your presentation file by 5pm Tuesday before your Wed. presentation.

  15. Presentation organization Follow this general outline when preparing your presentation: • Problem • Motivation • Approach • Evaluation • Conclusion • Questions

  16. Problem • What problem does this work solve? • Describe how the problem developed • If possible, tell the problem’s “story” • Ex., “New MRI technology was developed that reports medically significant tensor data. Previous MRI data was scalar valued, so new methods for communicating the structure of the tensor field in the brain needed to be developed. This paper describes a method for visualizing two dimensional tensors over the brain volume.” • Give appropriate definitions of any new terms or acronyms • This part should be interesting and attention grabbing (while remaining relevant)

  17. Motivation • Why is the problem important? • Examples of applications that would benefit from a solution • Examples of people who might be able to use a good solution • Would a good solution • Save lives? • Save money? • Help scientists make new discoveries? • Improve education? • Tell us why should we care about this work.

  18. Approach • Teach us how this visualization method works • Provide background information wherever necessary • Go slow • Don’t skip steps • Be explicit, give examples • Include illustrations from the paper and other sources • This is the longest, most detailed part of the presentation

  19. Evaluation • How is the performance of this visualization measured? • If a validation experiment was performed, explain how it worked: inputs, outputs, comparisons • Summarize the results of the evaluation • Put in context, given the what competing visualization methods might do

  20. Conclusion • In what situations would this method be ideal? • In what situations would this method perform poorly? • Are the visualizations effective from a human perception standpoint? • Is the method efficient from a computation standpoint? • Is additional work needed? If so, what? • This is the critical thinking part.

  21. Questions • Bring 3-5 questions for the class • Ask us questions that require us to thoroughly understand the paper in order to answer. • Ask us questions that make us consider the work critically and stimulate discussion.

  22. When you are not presenting • Read the papers carefully before class • Understand the basic objective and methods used • Jot down questions regarding anything that is unclear • Make note of what you think the main strengths and weaknesses of the work are • Participate in class discussion of paper • Provide an honest and constructive evaluation of your peers’ presentations • See form on course website • Fill out and give to me (or email me) after class • I will provide anonymous feedback to presenters

  23. Respond to feedback • No one will present two papers on the same day. • Please respond to recommendations you receive on your first presentation when you prepare your second presentation.

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