This is an archived website for Spring 2017

by Mike Gleicher on July 21, 2017

This is the website for an old (Spring 2017) edition of CS765.

If you are looking for the newer (Fall 2017) edition of CS765 please look here.

DC3 Grades

by Mike Gleicher on May 12, 2017

We graded DC3 and the grades were posted to Canvas. We saw many interesting designs, and a range of ideas.

Unfortunately, in order to get things graded on time, we were not able to type up our notes on each assignment. So you got no feedback other than the grade.

  1. If you want feedback because you’d like to use this as an opportunity to learn more about visualization, let us know and we’ll set up a time to do this in person.
  2. If you want to talk about your grade and understand why you didn’t get a higher score… First, I checked (after posting grades): given the current grading spreadsheet, a 1/2 letter grade raise won’t change anyone’s final grade. So, arguing about your DC3 won’t raise your final grades. (we haven’t posted final grades, but they will be posted by noon).

For #1, we had asked about a demo session. Either, I didn’t spread the word well, or there is low interest. If more people sign up at, maybe we’ll do it. I’ll look at the list later today and guage interest. If there is no announcement, assume there’s no demo session.

If you’re curious, there was really no correlation between whether someone chose to write a tool or not – there were good and (less good) tools, and good and less good sketches/visualizations. Things without working tools often made up for it with really thoughtful analysis, critique, and creative design. (There were also some over-the-top tools, which also had really creative designs and really thoughtful analysis documentation – but we didn’t give people “more than As”)

Also, if you’re curious… less good tool submissions generally just made standard plots using standard plotting packages, and usually had minimal documentation (often below the minimum requirements). There were some assignments that had very creative uses of standard designs, using multiple visualizations to address a range of questions.

And since you’re probably not curious… a challange was the range of skills that people had. Someone with good implementation skills could build a nice system – independent of their skill at designing good visualizations. Fortunately, the most impressive systems (almost always) implemented well thought-out designs, and documented their rationales well. And we tried to reward good visualization design – even if the implementation wasn’t well integrated etc. (At the extreme, some sketch designs – implemented with colored pencil and post-it notes – got As).

DC Demos

by Mike Gleicher on May 8, 2017

OK, One final post about DC3 (I hope this is the last one…)

To the 14 people who turned in “pre-submissions” – I provided “quick” feedback in the submission comments. I did not look at things closely, nor try to assess anything other than completeness.

For demos: if you haven’t scheduled a slot, your still can at:

For the demos: if it wasn’t clear, please bring a laptop. We can try to demo things on lab machines, but this is less likely to work well (unless your demo is something running on a server somewhere else that we can access over the web).

DC3 Endgame

by Mike Gleicher on May 2, 2017

This post is to detail how we’ll do the handins and grading for Design Challenge 3. That posting has a lot of details on what to turn in, so you might want to read it again.

The deadlines in the assignment posting:

  1. Monday 5/8 – Noon – if you turn things in before this, we will check it
  2. Tuesday 5/9 – Noon – official deadline for turning things on Canvas. Things turned in after this will be considered late and (possibly) penalized.
  3. Tuesday 5/9 and Wednesday 5/10 (afternoon) – we’ll do demos. If you turned your project in on time (e.g. before the demos), and something comes up in the demo, you can update your project (it will be considered late – so ask at the demo if it’s worth making the fix). If you turn in your assignment during/after the demos, it will be counted as late.
  4. Wednesday May 10th – we cannot grade things turned in after this.


We will do “demos” on Tuesday, May 8th and Wednesday May 9th.

If you made a tool, a demo is pretty much required. If you really don’t want to show off what you’ve made, then we can try to look at what you turn in by itself, but it’s unlikely we’ll be able to test it.

If you didn’t make a tool, you are still welcome to schedule a demo so you can show off what you did and we can ask questions about it. It’s not essential if what you turn in is well documented and self-explanatory, but it gives us a chance to clarify things.

We will schedule demos for 20 minute slots, with 3 people in each slot (since not everyone will take the same amount of time). We expect to spend about 5 minutes with each one, but there is flexibility (some will be faster and some slower), and everyone can “load data” in parallel at the beginning.

Sign up for a time slot at:

Please try to take one of the available slots. If you really can’t make any of them either (1) try to get by without doing a demo, or (2) send email and we can try to schedule something for Monday (5/8) or Friday (5/5).

More on Grading

The turn in process and the things we’re looking for are detailed in the assignment, but here is a more specific process that we will use. Look at the list of 7 criteria (in “How will this be graded”) and the hand-in requirement

For Tools:

  1. [Demo] A quick overview of the design – what are the basic ideas and goals (this will supplement what you write in the handin)
  2. [Demo] Demonstrations based on data of your choice (data sets provided in the repository) – for these, we expect that you’ll not only be able to show that your tool does something reasonable, but point out how it addresses some of the tasks you identify.
  3. [Demo] Demonstrations based on our data sets (data sets we’ll give you at the demo) – for these, we’ll want to see that your tool works on new data sets. Also, we’ll want to see if your tool helps find some of the things that we have “planted” in the data. (for example, we might make a data set where a person got sick, or where there is a bad assignment)
  4. [After Demo] Review of the documentation and handin – from this, we’ll assess you design documents (e.g., task analysis, design rationale). While we will probably look at your code to satisfy our curiousity, we will not penalize you if your code is ugly. We will care that your handin is complete (e.g., that there are instructions on how to run things). We probably won’t try to run people’s tools (which is why the demos are important).

For Sketches and Visualizations:

At the demo, we’ll try to get a quick overview of what you were trying to do. We’ll let you point out some of the kinds of things you see in the examples you generate. Then we’ll ask you about how it might look on other data (including some specific things like “what would it look like if a good student got sick? how easy would it be to spot?”). The idea is that it will give you a chance to let us know how the design would work in other situations, even if you don’t give us a tool that let’s us try it.

For these kinds of assignment, our review of the hand-in after the demo will be more important.

Things we’ll look for (all assignment types): see the list on the assignment, but this is more “grading specific”:

  1. Did you turn in all required parts of the assignment?
  2. Does your task analysis show that you’ve thought about what you are trying to show?
  3. Does your design address some of these tasks? Can you provide convincing examples / rationale that the design will work?
  4. Is the design creative / interesting?
  5. Does the design make good use of the principles we discussed throughout class (e.g. encoding choices, …)?
  6. Is the design communicated well? Even if the implementation is crude, or the you just have a sketch, is there enough other information so we could imagine what it will look like in practice?
  7. Does the implementation work? Does it provide outputs that fit the design? Did it work on data sets you haven’t seen before?

There are, of course, trade-offs. If you have a tool that makes even a simple visualization but does so robustly (works on the new data sets), that’s an accomplishment – we’ll be impressed that you pulled it off in a short amount of time. If you just give a sketch, you’ll have to impress us with the creativity of your design or the thoroughness of your rationale or …



The Week in Vis: Week 15 (5/1-5/5)

by Mike Gleicher on April 30, 2017

We made it to the end (sortof). So many topics to discuss, so little time, …

Last week was “guest lecture week” and hopefully, was a chance to get started on Design Challenge 3.

Please do the official course evals. They really do help me – and the department. And the department really likes to see high turnout rates (we got over 90% for the mid-semester evals). Go to the AEFIS web page and you can fill it out. As of 4/30, only 14 people have done it.

I really do look at these things. I have a lot of good feedback from the mid-semester evals (you may have noticed changes in how the class was run, and more things will get fixed next semester).

Other that the course evals (please do it!)…

  • We have the last reading (Reading 15). I understand if there’s no discussion (although lots to discuss). Also, it’s a little disconnected from the lectures.
  • We have the last Seek and Find (Seek and Find 15).
  • Design Challenge 3 is going on. Hopefully, the Checkpoint will help us understand how we’re going to get these from people. I apologize for not figuring out the final details – but we need to be able to adapt to what people are doing. Expect instructions this week.

So this week…

  • Monday (5/1) – “Lecture” (if you can call it that – lectures in the room are a challenge for everyone) on 3D. The initial part of the Reading 15  is due – but it has nothing to do with the lecture. We may take some time to talk about DC3 (to let people discuss tools with others using similar ones).
  • Tuesday (5/2) – Design Challenge 3, Milestone 2. A chance to check in to see how everyone is doing, and figure out how we’ll collect the assignments.
  • Wednesday (5/3) – “Lecture” – on whatever topics are leftover (there are tons – not sure what I’ll talk about). Some SciVis stuff will certainly come up. Maybe more 3D. Maybe some low-level programming stuff to help with DC3. Who knows.
  • Friday (5/5) – Seek and Find 15 is due, and any other posts for any of the discussions that you want to grade.
  • And another reminder to do the course eval, if you haven’t.

By the end of the week, we should have a plan for how we’ll collect all the DC3s.


The Week in Vis: Week 14 (4/24-4/28)

by Mike Gleicher on April 21, 2017

We’re almost at the end. So here’s what is happening for the rest of the semester.

Last week, we had “lectures” about scale, hopefully, we learned more than just “the room is poorly suited to lectures.” We also talked about Design Challenge 3. Hopefully, you did Reading and Assignment 13 as well as Seek and Find 13. And you turned in the last of the turn in parts of Design Challenge 2.

This week (4/24-4/28) will be guest lecture week. Both real and virtual. It’s a chance for us to consider presentations and animation, and to work on Design Challenge 3. Reading/Assignment 14 is due, as is Seek and Find 14.

  • Monday (4/24) – Guest lecture on text visualization and set visualization. “Reading” and Discussion Assignment 14 starts (initial posting due).
  • Tuesday (4/25) – Design Challenge 3 Milestone 1 is due. What we want from you is minimal. It’s mainly so we can check to make sure you’ve started, and get a sense of what people plan to do so we can plan to help you.
  • Wednesday (4/26) – In class exercise to wrap up Design Challenge 2.
  • Friday (4/28) – No class! However, Seek and Find 14 is due, as is the later parts of Assignment 14 (including watching the virtual guest lecture).

This class (like all CS classes) will be doing online evaluations through AEFIS. You will get email with instructions. Please do a course evaluation.

The following week (5/1-5/3) is the last week of the semester. We’ll have the usual (lectures and reading 15 and seek and find), and Design Challenge 3 will continue.

I will be out of town from 4/21-4/29 (I am in France for Eurographics and The Workshop of Intelligent Cinematography and Editing. I am giving a keynote talk about things I did back when I was a graduate student. Normally, I don’t travel so much during the semester.

Reading/Assignment/Discussion 15: Leftovers

by Mike Gleicher on April 16, 2017

Due: required posting by May 5th – this is a hard deadline since we need it to be done to do grading.

Canvas Discussion: LINK

It always happens, we get to the end of the semester and there are so many more topics that I want to discuss.

For the last 2 lectures, May 1 and 3, I’ll talk about 3D and Scientific Visualization. This means there won’t be a day to talk about uncertainty (which is a really important topic). So, the required reading will be about uncertainty.

Because the readings are disconnected from the lectures, I’ll let people have extra time to do them / make the required posting. (it is the end of the semester and I know how it goes). Also, there will be no expectation of discussion (since everyone will be busy with their DC3 and other classes) – but please discuss since that’s a better way for learning.

Since 3D and SciVis are interesting to a lot of people, there are optional readings. They are both big topics: a reading or two won’t make a dent. Even finding decent “getting starting” readings is hard.

I recommend that everyone watches the 3 minute video about Visualizing Mummies at the Brittish Museum (video) to motivate why you may care about traditional 3D Vis and SciVis. The video isn’t great, but you’ll get the idea. The paper is pretty cool.

The Readings – Uncertainty

Don’t worry – you don’t have to read all 6!

The first paper is short (it’s an extended abstract), but it gets at a lot of the issues (in an unexpected way).

1. Boukhelifa, N., & Duke, D. J. (2009). Uncertainty visualization: why might it fail? In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems – CHI EA ’09 (p. 4051). New York, New York, USA: ACM Press. doi:10.1145/1520340.1520616 (ACM) (PDF in Box)

In contrast, this is a thorough survey – too much for me to ask everyone to read, but it has a nice diversity.

2. Ken Brodlie, Osorio, R. A., & Lopes, A. (2012). Expanding the Frontiers of Visual Analytics and Visualization. In J. Dill, R. Earnshaw, D. Kasik, J. Vince, & P. C. Wong (Eds.), Expanding the Frontiers of Visual Analytics and Visualization (pp. 81–109). London: Springer London. doi:10.1007/978-1-4471-2804-5 (Springer) (PDF in Box)

I like this next paper because it gets at a variety of different ways to show uncertainty, and points at some of the different strategies. The evaluation aspect is less important for class.

3. MacEachren, A. M., Roth, R. E., O’Brien, J., Li, B., Swingley, D., & Gahegan, M. (2012). Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2496–2505. doi:10.1109/TVCG.2012.279  (PDF)

This one focuses on a single kind of visual technique, but goes a little deeper…

4. Wood, J., Isenberg, P., Isenberg, T., Dykes, J., Boukhelifa, N., & Slingsby, A. (2012). Sketchy Rendering for Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2749–2758. doi:10.1109/TVCG.2012.262 (web)

We wrote a paper that  deals with a very common case of uncertainty visualization, and one of the most standard visualizations.

5. Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. doi:10.1109/TVCG.2014.2346298 (web)

The statisticians have a lot to say about how we should think about uncertainty, especially in experiments. This paper gets at many of the issues (it is statisticians explaining to psychologists what they should do).

6. Cumming, G., & Finch, S. (n.d.). Inference by eye: confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–80. doi:10.1037/0003-066X.60.2.170 (pdf)

What you need to read…

Everyone must read #1 and/or #2 (I recommend both). Each person should read 3 or 4 – but don’t worry about the details of the experiments. (hopefully within each discussion group, there will be a mix). Everyone should look at 5 (but again, get the gist, don’t worry about the details of the experiments). 6 is optional.

For your initial posting, give a sense of the kinds of challenges in visualizing uncertain data. And then describe how the methods in the technique paper you read (3,4) address these. Given that this is the last week of class (and technically, class ends), our expectations are lower. If there is discussion great. But mainly, we want to know that you looked at the readings.

Stuff that is (sadly) optional…

These are topics I really wanted to get to. But there isn’t enough time.


(Note: this is optional. I find it a fascinating topic, and it’s what drew me to vis. We’ll try to cover the high points in class – or as much as we can in 1 lecture).

We’ve been avoiding 3D for most of class. We can’t do it forever. While using 3D for visualization has its problems, sometimes its important (if we’re trying to show 3D phenomena), and sometimes it can be useful.

The initial readings will give you a sense of how we see 3D. The focus is on the perception part. What cues do we use? What can we or can’t we measure visually?

We don’t really have much time in class to discuss how to make pictures that best help people see depth. I have some readings, but we won’t get to them. They are listed below in case you are interested.

Recommended Readings (required in previous years):

artists have dealt with the problem of trying to convey depth in a picture since, well, I’ll let an art historian answer that, but let’s just say a long time. Painters and illustrators have all kinds of tricks. Photographers and filmmakers use light and camera position and other things. Computer Scientists have tried to pick up some of those tricks and systematize them.

This is a chapter of the “Guild Handbook of Illustration” that helps illustrators learn to convey 3D shape in their drawings. A lot of it is about how to think about how light helps you perceive shape (and it does so with fabulous examples). When they start talking about the actual techniques (like how to use charcoal to make the pictures), it’s a little less interesting.

  • Light on Form (Chapter 4 of the Guild Handbook of Illustration) by Jessup and Mascaro. (CS protected reader) (box)

Some things that apply well to Vis:

  • Amy and Bruce Gooch. Using Non-Photorealistic Rendering to Communicate Shape. SIGGRAPH ‘99 course notes here. (this is better than the original, but seminal paper. you don’t need to read it in detail – just skim through the motivation and look at the pictures).
  • Cipriano and Gleicher. Molecular Surface Abstraction.
  • Look at the light collages web page (but it links to the initial version of the paper – if you want to read more, check below).

I really wanted to add a few things that show how to effectively use the cues in visualization. But this is just so huge and broad that I don’t know where to start. I’ll mention some of my favorites (some of these are seminal pieces, where there is lots of follow on. some of these are:

  • Lee, et al. Geometry-Dependent Lighting. IEEE Trans of Vis and Comp Graphics. (ieee official version). Note: this paper is the extended version of the original Light Collages paper.
  • SIGGRAPH 2008 Course notes “Line Drawings from 3D Models” – These are nice slides that summarize the topic very well.
  • DeCarlo, et al. Suggestive Contours for Conveying Shape. Proc. SIGGRAPH 2003. (pdf) (project). The 2003 paper is really seminal, the web page lists some of the follow-ons.
  • Linedemann and Ropinski.  About the Influence of Illumination Models on Image Comprehension in Direct Volume Rendering. IEEE Vis 2011. (page here)


(Note: this is optional. For people who have the problem, it’s a big topic. We’ll try to cover the high points in class – or as much as we can in 1 lecture).

The term “scientific visualization” is somewhat problematic. In some sense, it means what a specific branch of the visualization community likes to call scientific visualization. But, usually it involves visualization of physical (usually spatial) phenomena – that are not cartographic.

The main part of scientific visualization is the visualization of “standard” spatial data types (scalar fields, vector fields, …).

There is a huge literature on how to solve these problems, and they continue to be an active research area.

Volume visualization (3D scalar fields) is the main topic. Arguably, you need to understand it before anything else. It is the mainstay of medical imaging as well.

Some places to get started:

  • Munzner Chapter 8 – a basic survey of how to show spatial data
  • Arie Kaufman and Klaus Mueller. Overview of Volume Rendering. Chapter 7 of The Visualization Handbook (Hansen and Johnson eds), Academic Press, 2005. (available via the library, but we grabbed it and put it into the reader here, or the author has a preprint here)
  • As I mentioned above, the work on 3D Visualization of Mummies at Museums is a good way to motivate advanced volume visualization. (paper)

Initial Posting (requires a little reading): Due Monday, April 24th
Additional Postings and Discussion: A total of 4 required postings, plus I think this will lead to lots of conversation. We’ll leave the discussion open through the end of the semester (but we will probably grade around May 5).
Discussion on Cavnas: HERE

This week, is actually a mix of 3 different topics that are not directly connected to what’s going on in class. They all interconnect in a way that might make sense afterwards.

  1. Presentations – as part of grad class, I always want to help people develop presentation skills since they will be important no matter what you do.
  2. Video presentations – making a good video to convey your idea is a different medium, but one that is useful. And surprisingly hard to do well.
  3. Animation – using animation in visualization is a valuable thing, and brings up lots of questions in terms of design, perception, and implementation.
  4. Virtual Guest Lectures – since I’m not around to lecture to you, it’s a chance to let a famous person teach you about vis.

There’s limited reading this week. But you need to do the following things (preferably in this order):

  1. Read my rant about presentations (this is from an older class – you won’t get to hear me do this in person). See the details below. But after reading this, I’d like you to make a posting about how it applies to you (details below).
  2. As a positive example of presentation… Watch a video by Hans Rosling. Dr. Rosling died this past year, but he left a big mark on many fields. I’ll ask you to comment on it.
  3. Watch a video about animations in vis. This is both to get some of the ideas, as well as to see how the ideas are presented in video form. (it’s a research presentation)
  4. Watch a “virtual guest lecture” – you just have to watch one, and tell others what you saw (in case they saw the other one).

This means 4 required postings (which is a lot), but there isn’t so much reading (other than my course web post). And there’s a lot to converse about. All in the same Canvas Thread.

Part 1: Presentations

Normally, I’d give a lecture on presentations. Instead, you get to read through my notes (since we’re running out of lecture times, and the classroom is bad for lectures). Before reading my notes, here are some caveats (note: this is taken from the 2012 class):

  • The goals and standard for presentation really vary across venue/discipline. What we value in computer science (in particular the areas I work in) are quite different than in other disciplines. It’s hard for me to discuss this without value judgement (since I am bred to believe in the “CS way”), but I also plead ignorance to the practices in other area. I’d like to use this as a chance to learn about others.
  • I don’t consider myself to be a great presenter. Do as I say, not as I do. The upside of this, is that it means I think about how to be better at it.
  • A lecture is not the same as a talk, so what you see in class is quite different than what you would see in one of my talks.
  • Even within a particular style/venue/type of talk, there is a wide range of opinions on what is good talk, what the goals should be, …
  • The “right answer” depends not only on the situation, but on the person. But that will be one of the biggest lessons I hope you get. I may not speak to your specific case, but hopefully, you can see how the general lessons apply.
  • As you might guess, I have strong opinions. But you don’t have to guess at what they are, since I’ve written them down.

Given that…

My real goal is to get you to think about what might make for a good presentation, and to form your own strong opinions – even if they are different than mine.

Given that, read my posting about presentations. Yes, it’s from a 2011 class – but I think if I were updating it, it wouldn’t be much different.

For a requited posting: Comment on how this relates to you. If you’re outside of CS, how do the standards of your field differ? If you’re within CS, how does this relate to talks you’ve seen or other advice you’ve been given.

For what it is worth: I am preparing a conference keynote talk (which is why I’ll be missing class), and I am re-reading those notes in preparing.

Part 2: Video Presentations

Hans Rosling is a famous presenter – talking about social issues around the world in venues like TED, etc. He was famous for presenting data in a compelling way to make his points for a broad audience. Sadly, he died this year. But his influence is significant (both on presentating data and on the world in general).

If you haven’t seen a Rosling talk, you need to experience one. If you have seen one, you probably won’t mind watching another.

There are lots of videos of rosling presentations – here’s one I have handy, or here’s another one.

The actual point of Rosling is not his visualizations (he does use standard visualization effectively – often with animation), but rather as a way to talk about presentations.

For a required posting: Say which video you watched (give a link, especially if it’s not one of the two I gave). Say why you think he’s good (or not), but also comment on why he is often used as an example for presentation style. What can you learn from him (positive or negative)?

Part 3: Research Videos and Animation

I’ll kill two birds with one stone here: I want you to think about the role of animations in visualization, and how to present research results in video form. So, I’ll have you watch a research video about animation in visualization!

You don’t have to read the paper, but you do have to watch the video:

  • Heer, Jeffrey, and George Robertson. “Animated Transitions in Statistical Data Graphics.” IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (January 2007): 1240–47. doi:10.1109/TVCG.2007.70539.

Some of the ideas in the video have been questioned in perceptual studies, but I think the basic concepts are still worthwhile.

For a required posting: why is this a good presentation of the ideas (or not)? You can comment on the ideas, but we are curious what you think of how the ideas are presented.

If you want to know what I did as a graduate student, here is a circa 1992 video that I wrote in graduate school. On YouTube. Yes, that is a 25 year old me you’re listening to. And yes, I did it with a 1991 computer, video recorders, and limited ability to edit. Today you should make better videos since you have better technology.

Part 4: The Virtual Guest Lecture

At the end of the semester, I like to give a summary lecture. This semester, we may run out of time (too much more content to cover!). So, I thought I’d let some more famous person give you a lecture. In the past, I’ve used these videos when I was out of town (people like them)

There are two. You can pick either. Or you can watch both.

Both talks are Capstone talks. They are invited talks at the end of the conference – not regular paper talks. Both are also recorded in an odd way: you get the audio and the slides. This is not ideal. To be honest, listening to the talk and seeing the slides is a poor substitute for the actual talk. There is something about a live event too. So, please bear with the production values. It is better than nothing (I believe).

The two options are:

  1. Jarke van Wijk. VIS 2013: Capstone: Information Visualization: Challenges and Opportunities.
    We’ve actually seen a bunch of his work. I was at this talk.
  2. John Stasko. EuroVis 2014: Capstone: The Value of Visualization…and Why Interaction Matters.
    Ironically, John is talking about van Wijk’s old work (the value of visualization paper). Further irony, John is the one introducing van Wijk in the first talk.

The format of the “video” (slides + audio) makes for a different experience than seeing the talk in person. I’d like to separate the content of the talks from the delivery mechanism,. If you want to comment on the format, that’s OK – but try to separate it from discussions of the content. Trust me that, in person, they are both good speakers (with very different styles)

Required posting: since half of the class (roughly) didn’t see the talk you saw, give a brief summary of it, focusing on what you got out of it. So write “these are the main things you would have learned had you seen the talk” (which is more or less the same as “these are the main things I learned”).

If you want some food for thought: In both cases, these are senior people who have made many contributions to the visualization community. What can you see about their perspectives? What questions do the talks raise in your mind?


The Week in Vis: Week 13 (4/17-4/21)

by Mike Gleicher on April 14, 2017

This past week, we thought about how to think about multiple variables, and Design Challenge 2.

This coming week, we’ll wrap up DC2 and move on to DC3. We’ll also talk about scale: what happens in Vis when the data gets bigger and harder.

Note that on Monday, we’ll have an in-class design exercise, so being paper and something to draw with. (for those of you who like to do things on tablets, well, its easier to share paper with others in class, so we’ll prefer things on paper).

Also, on Wednesday, we’ll talk about DC3. So you should at least read the description (it’s not totally finished, but you can get the main ideas) and bring questions.

  • Monday, April 17th – An in class design exercise to introduce scalability issues, and a lecture on some of the issues in item removal and aggregation. Reading 13 (which is also Discussion/Assignment 13) is due (the first parts at least).
  • Tuesday, April 18th – The 3rd phase of Design Challenge 2 is due. The 4th phase will be an in-class exercise the following week.
  • Wednesday, April 19th – A lecture on other aspects of scalability (dimensionality reduction) and a discussion of Design Challenge 3.
  • Friday, April 21st – No class! The additional postings for Discussion/Assignment 13 and Seek and Find 13 are due.

After this week… there’s only 2 weeks left of class, and there are so many topics that we didn’t get to. And there’s Design Challenge 3.

The Week in Vis: Week 12 (4/10-4/14)

by Mike Gleicher on April 7, 2017

This past week, we talked about implementation. Which was interesting to some, and not so interesting to others. For those of you who are interested in implementation, it is important that we tried to understand what to implement first. For those of you who aren’t interested in implementation, well, hopefully it was a small enough dose that you got an appreciation for what the rest of us are doing.

The implementation points will be relevant for Design Challenge 3. It is available in draft form, including some initial data and some sample code. Design Challenge 2 is still going on, but you might want to look ahead.

Yes, I am aware we are slow in getting Design Challenge 1 graded. We are working on it.

This week…

  • Monday, April 10th – “Lecture” on Multi-variate data. Alper will be our guest lecturer. He will tell you some things useful for the latter parts of Design Challenge 2. Reading 12 is due, as is Discussion 12 (the initial posting).
  • Tuesday, April 11th – Phase 2 of Design Challenge 2 is due.
  • Wednesday, April 12th – In class, there will be some discussion of the second phase of Design Challenge 2, we’ll do an in-class design exercise. If there’s time left, Alper will help people understand how to do stuff inside the web browser (but this will be at the end, so those of you who don’t want to learn what happens inside a web browser can leave).
  • Friday, April 14th – No class! But Seek and Find 12 is due  is due. And the rest of the postings for Discussion 12.

For the rest of the semester…

  • Design Challenge 3 is coming up – you might want to look ahead at the preview.
  • We will have class on Mondays and Wednesdays, with no Friday sessions. By my count, we will have held pretty true to the twice per week schedule, and few people come to the optional Fridays when we had them. Plus, the semester ends on a Thursday, so the last Friday isn’t really an option, and the other Fridays I’m away (and there aren’t that many).
  • We’ll continue the weekly seek and finds (in fact, all of them are posted in case you want to get ahead). There will be weekly readings and discussions for the last weeks too. Hopefully they will get posted soon as well.
  • We have a backlog of grading… but we have a plan to get caught up.