I am here to talk about the science behind visualization. I am Prof. Tamara Munzner from the University of British Columbia. Ask Me Anything!
Bibliographic record
Abstract
Hello world! Tamara Munzner here. I’ve been doing computer-based visualization for almost 25 years, starting as technical staff at the NSF-funded Geometry Center, continuing as a grad student in the Stanford graphics group with Pat Hanrahan, and then as a professor at UBC since 2002. I have worked in a broad range of application domains including genomics, evolutionary biology, fisheries management, energy and sustainability, geometric topology, large-scale system administration, web log analysis, computer networking, computational linguistics, data mining, and journalism. Yet more details on my web site in general or my bio page in particular. Let’s talk about the science behind visualization! I’m particularly excited to talk about the ideas covered my book, Visualization Analysis and Design. Since it’s done at long last. Or any of the visualization research papers, videos, or software at on my lab web site. Or anything about the visual representation of data, broadly construed. And hey, it’s an AMA, so anything else is fair game too. Including books, especially science fiction and fantasy, since reading too much is a vice of mine. As you can see from my reading lists: books read in reverse chronological order and books read ordered by author, with commentary. Proof: https://twitter.com/tamaramunzner/status/636466649541902336 Update 1: forgot to say that the official start time for me answering is noon Pacific time which is 3pm Eastern. That’s soon! Update 2: Answers have started. Typety-type-type. Update 3: 3pm Pacific, taking the teeniest of breaks for a snack and cup of tea. Must hold body and soul and neurons together. I’ll be back! Update 4: 3:15pm Pacific, back to the keyboard. A runny Brie on rosemary bread toast and an acceptable Cream Early Grey have saved the day. Might need to move on to the big guns of Lapsang Suchong or a hefty Assam soon if the questions continue at this rate! Update 5: 6:30pm Pacific. Not dead yet - still answering! Although admittedly my posting rate slowing down, despite my fresh cup of Halmari Assam… Update 6: 10pm Pacific. Declaring victory, or at least throwing in the towel. I’ve completely run out of time, I’ve mostly run out of neurons, and I think dinner sounds like a fine idea right about now. Wow, this has been an amazingly fun day! Many thanks to everybody below for your thoughtful questions, and also thanks to @frostickle in particular for both talking me into this and for shepherding me through it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.152 | 0.096 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".