IEEE VIS 2017: Machine Learning, Diversity, Parties

I’ve ignored the major new topic this year so far: machine learning. Another new thing this year, though way overdue, was that we finally started to talk about diversity. And then there were the parties.

Machine Learning

Machine learning made a big showing this year, though I managed to miss most of the relevant talks and events. In addition to the best paper at VAST, there were also two workshops and a tutorial on the topic. The Visualization in Data Science workshop had an interesting panel discussing the question of when humans need to be in the loop (Hadley Wickham deftly summed it up at the end as “machines are good at some things, humans are good at other things”).

A relevant paper that I couldn’t fit anywhere else was LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks by Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander Rush. They developed a tool for exploring a class of neural networks that are good at learning sequences. It runs in the browser and they have a variety of example inputs to try it out with.

Despite my lack of coverage of the machine learning papers and events, this topic was quite noticeable this year and will undoubtedly become a common theme at the conference.

Diversity Panel

At last year’s Death of SciVis panel, the issue of diversity (or rather its lack) in the visualization field came up in the discussion. This year, the Diversity in Visualization panel with Robert S. Laramee (Organizer), Rita Borgo, Vetria Byrd, Aviva Frank, Kelly Gaither, Ronald Metoyer, and Erica Yang set out to highlight and address the issue.

The presentations covered a variety of topics, from statistics about gender diversity (or rather, its lack) to power, and a few ways to improve things. Kelly Gaither talked about how the U.S. will soon be majority minority (meaning no particular race will have a majority), but minorities still only make up 7% of STEM graduates. Jobs, however, are in STEM. That’s clearly a problem that need to be addressed. Rita Borgo also looked at numbers, comparing gender diversity in the VIS and CHI organizing committees.

Vetria Byrd talked about a conference she runs called BPViz: Broadening Participation in Visualization, the next one will be held at Purdue, June 13–14, 2018. Ron Metoyer also mentioned the Tapia Conference, which is about participation of underrepresented groups in computer science more generally. CHI also has a long-running diversity program, and VIS also did for the first time this year.

I think it was reasonable that this panel got the Best Panel award, but I also felt that there were quite a few things missing. It seemed odd that nobody brought up Grace Hopper Celebration, a conference focusing on women in computer science that took place at the same time as VIS and was 15 times its size. I also didn’t see anybody think about changing the way they work to make it easier to bring people in who aren’t usually interested in data and visualization – along the lines of Catherine D’Ignazio’s work, for example (her Information+ talk is well worth watching).


Parties at VIS are a relatively new thing, I believe they started with the Tableau Party in 2012. They’re a great addition to the social fabric of the conference – though they also make the week more exhausting.

Location scouting for them can be tricky, and last year they all ended up in the same pub. This year, IBM set a new standard with their party Sunday night in a barcade (that’s an arcade with adult beverages).

The West Coast Party and the Austrian Party took place in nice bars, . Though the Austrian Party got some people out in Dirndls and Lederhosen, which was fun.


This concludes the report from IEEE VIS 2017. It was a good conference, with many exciting talks and some interesting new directions. And, yes, some great parties.

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2 responses to “IEEE VIS 2017: Machine Learning, Diversity, Parties”

  1. Yao Ming Avatar

    There was another panel related to the VIS+ML: How do Recent Machine Learning Advances Impact the Data Visualization Research Agenda? Not sure if you have attended it. The panelists also output some interesting thoughts on the topic of VIS+ML. I’ve took some notes on their opinions in my blog ( if you are interested.

    1. Robert Kosara Avatar

      Ah, right! Thanks! I did an amazing job missing all of these this year…