SharonHsiao

EdTech, Visual Analytics, AR, Computational Technology, Sports, Sustainability #PittAlum #CSprof #HCAI

[UMAP @UMAPconf]

📢 #UMAP2027 CFPs are open!

📅 Deadlines (AoE):

📜 Workshops/Tutorials: Dec 11
📄 Paper Abstracts: Jan 22
📄 Papers: Jan 29
🎓 Doctoral Consortium: Feb 6
💪 Industry + LBR/Demos: Mar 22

📍 Chicago, June 9–12, 2027

🔗 https://t.co/HMgV9h1GSC

[Educational Data Mining 2027 @EDMConf2027]

EDM 2027 is live!

Join us in Adelaide, Australia, July 18-23, 2027, for the International Conference on Educational Data Mining, part of the Festival of Learning.

Theme: Mining for Meaning: Human Agency, Values, and Learning in the Age of AI.

https://t.co/1coWy80Bqs

[The Math Flow @TheMathFlow]

Solving a Rubik's Cube with graph theory.

[SharonHsiao @shoha99]

Check out project website, interactive demo is there too. https://t.co/IBRwGcjt86 #genAI #Tamagotchi #GoalSetting

[SharonHsiao @shoha99]

Very excited about two HT’26 papers acceptance: Beyond Exploration: AI-facilitated Information Visualization Structure for Complex Context Exploration and Decision-Making; Hyper-Personalized Virtual Companion: Generative Tamagotchi to Cultivate Personal Goals

[AA @measure_plan]

i trained a computer vision model to train me at tennis

it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball

how this works:
- input video from an iphone
- CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model
- video annotation using python + openCV

goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique

more to come!

[UMAP @UMAPconf]

📢 ACM UMAP 2027 website is live!

📍 Chicago | June 9–12, 2027

Deadlines:
• Workshops/Tutorials: Dec 11
• Full/Short: Jan 22 (abstract), Jan 29 (paper)
• DC: Feb 6
• Industry + LBR/Demos: Mar 22

🔗 https://t.co/HMgV9h1GSC

#UMAP2027

[ThreeJS Assets @threejsassets]

https://t.co/FrdfHRwII6 is live. 🎉

A 3D asset library built specifically for @threejs: every model web-optimized, drop-in ready, cleanly licensed for commercial work.

Shipping 3 things at once today: the library, our first pack, and a free browser Studio (beta). 🧵

[Yasmine Khosrowshahi @yasminekho]

Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible...

And why AI hasn't actually learned to "see" the way we do.

It completely changes how you think about Human Intelligence v/s Artificial Intelligence:

1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly.

2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it.

3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes.

4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired.

5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary.

6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it.

7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object.

8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs.

9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy.

10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments.

11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce.

12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer.

13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong.

14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly.

15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did.

16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built.

Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.

[SharonHsiao @shoha99]

#VirtualCompanion #ConversationalAI #MoreThanDigitalAvatar

[SharonHsiao @shoha99]

https://t.co/glIp1nBHn2
#PET #Personalization #GenAI #AgenticAI #GoalSetting new empirical work is under review now, stay tuned.

[SharonHsiao @shoha99]

Georgetown Commencement 2026 | Tom Brady Speech https://t.co/CM6AsOwm3i via @YouTube

[Seungone Kim @seungonekim]

🔖Do you want to get feedback in your manuscript before submitting?

Try out our CMU Paper Reviewer platform, an AI review service that provides feedback on the five most critical issues to address in your paper!

https://t.co/fZq4cmBiyS

[Diyi Yang @Diyi_Yang]

The next frontier of AI is not only more capable model; it is an AI that *humans* can meaningfully live and work with :)

With all students in my cs329x Human-Centered LLM class, we present 60+ pages of insights for developing Human-Centered LLMs (HCLLMs), from design & data sourcing to training, eval & deployment 🧵

[Tom Dörr @tom_doerr]

Turns text prompts into vector UI designs

https://t.co/Zj45ZQkDBx

[Mapping AI @mapping_ai]

Who actually shapes AI policy in the U.S.?

We mapped 1,812 entities: 745 people, 918 organizations, 2,925 relationships. Frontier Labs, AI Safety orgs, Think Tanks, Government, VCs, and more.

https://t.co/6RDB1R0qNd

[SharonHsiao @shoha99]

Very proud of my MS student to present her work in #IEEE #SusTech conference. "#Visualization Beyond Summarization" a very smart way to increase accessibility of very complex sustainability field.

[Chao Zhang 张超 @zhangchaodesign]

Shunan @grumpyChicky, one of my fantastic mentors at Adobe, will present my internship project, Narrix, at #CHI2026. Please come to her talk! See the paper link below.

📅 Session: Thu, 16 Apr | 9:00 AM–10:30 AM | P1, Room 127

[Anita Chen @amanitahalo]

i mapped all the subway rides that happen in a day!
it uses real train time tables and the mta's hourly origin-destination data. riders take the combination of scheduled trains that get them to their destination fastest.
see here: https://t.co/tiB6iMpxpa

[Tom Dörr @tom_doerr]

Animate anything with JavaScript

https://t.co/hVYFpIHZKI