More output does not mean more taste.
When generation is cheap, the scarce work is structuring the design space: what dimensions matter, what to vary, what to preserve, and how to justify a direction.
Design judgment + human-centered AI
I build and study interfaces for an age of generative abundance: tools that help people explore alternatives, notice consequential differences, explain tradeoffs, and build taste over time.
Cognitive Science Ph.D. student at UC San Diego Advised by Prof. Steven P. Dow in ProtoLab.
Thesis thread
Scaffolding for Taste asks a simple question: when AI can make ten plausible options in a minute, how do people learn to notice the difference that matters? I study interfaces that support reflective practice: making a move, seeing what changed, comparing alternatives, and building better reasons for choosing one direction over another.
Design help should not just make more artifacts. It should make the next move easier to see, and the next judgment easier to explain.
Don Norman's Design Lab talk helped sharpen this framing: the next generation of designers needs tools that cultivate taste, not just tools that generate options.I am testing this frame across creative tools, learning environments, mixed reality, everyday robotics, and community or civic design settings: places where tools can help people notice viable options, weigh tradeoffs, and choose the next move.
Why now
AI makes it easy to produce variants; my question is how people learn to choose well.
When generation is cheap, the scarce work is structuring the design space: what dimensions matter, what to vary, what to preserve, and how to justify a direction.
Designers, researchers, PMs, and engineers all shape early artifacts now. Interfaces need to support shared reasoning before decisions harden.
Fast cycles are useful only if teams keep room for critique, incubation, and explaining why a direction is better.
Research focus
I keep testing the same loop: how to make alternatives comparable, let evidence sharpen the next question, and fit assistance to the situation where judgment happens.
Showing the Design motion sketch. Vary the alternatives, compare them, then refine.
I study interfaces that support design thinking beyond artifact generation: dimensional scaffolds that make the design space visible, variation mechanisms that expose alternatives, structured comparison that helps people reason across options, and staged fidelity that lets ideas mature from rough intent to defensible direction.
See DesignWeaverExternalize useful dimensions so people can see what matters.
Use variation and structured comparison to reason across alternatives.
Move from coarse ideas to committed artifact choices through staged fidelity.
I evaluate AI tools with evidence between controlled psychology tasks and outcome-only benchmarks: process traces, artifact changes, rationales, preservation and explanation behavior, and learning probes that show what users carry beyond the interface.
Read CHI 2026 workshop paperTreat final artifact quality as one layer of evidence.
Connect traces and rationales to concrete design questions.
Measure explanation, preservation, revision, and transfer.
I use situated work for settings where judgment depends on practice and medium: studio critique, mixed reality, everyday robotics, and civic or urban projects each change what help should do, when it should appear, and when it should stay out of the way.
Let practice shape what assistance should do.
Match the interface to the medium and setting.
Make help fit the situation instead of interrupting it.
These threads are places I test the same concern with judgment, agency, and evidence in context.
Motion note Inspired by motion craft from Stripe's design team and Katie Dill at Stripe Sessions. Interaction rules grounded by Jeffrey Heer and collaborators on animated transitions and CMU's Data Interaction Group .
Live field The visible Dot Orbit layer uses Paper Shaders by Paper; the restraint also follows the founder's design walkthrough.
Selected publications
This paper helps when you can see what someone did in an AI tool but still cannot tell what that action meant to them.
A proposal for trace-guided micro-episodes and a utility-for-rationale pattern that turns useful recovery controls into chances to learn why something happened.
Its limitIt is a position paper and research agenda, not an empirical validation of the interventions or their downstream effects.
I checked this against the paper and its official record on 2026-07-13.
Read the full evidence and citation context
This paper helps when an implicit-surface argument needs more than the usual eikonal condition—especially around convergence, stability, topology, or surface area.
A heat-loss objective with convergence and stability analysis, plus 2D and 3D reconstruction evidence and a natural surface-area penalty.
Its limitThe reported gains belong to the evaluated reconstruction settings; sparse boundaries and parameter choices can still produce failure modes.
I checked this against the paper and its official record on 2026-07-13.
Read the full evidence and citation context
This paper helps when a blank prompt box is asking novices to supply design vocabulary they have not had a chance to learn.
A palette that externalizes product-design dimensions, grounded in expert practice and evaluated with 52 novice designers.
Its limitThe study used one chair-design task with novices, and richer prompts did not reliably improve satisfaction or expectation alignment.
I checked this against the paper and its official record on 2026-07-13.
Read the full evidence and citation context
This paper helps when you need a reproducible benchmark for asking whether a model predicts physical outcomes in human-like ways.
Eight scenario families, matched human and model evaluation, and released data and code for systematic comparison.
Its limitSynthetic scenes and binary contact prediction do not establish broad real-world physical understanding.
I checked this against the paper and its official record on 2026-07-13.
Read the full evidence and citation contextUpdates
Pinned Met the great Don Norman and even got a selfie + a “To Sirui” signed “Yellow Book”!
Our CHI 2026 workshop position paper, What Happened and Why? Trace-Guided Micro-Episodes with Elicited User Explanations for Product Iteration, was accepted to Herding CATs - easily one of the best workshop names ever! The whole ProtoLab is going to Spain! Might be a UCSD party!
HotSpot got selected as a CVPR 25 Highlight!
Research opportunities
I like working with curious, motivated, and kind undergraduate and master's students, especially people who have a question they cannot stop poking at.
You do not need to show up as a polished researcher. It helps if you like reading carefully, making small prototypes, testing claims, looking honestly at evidence, and writing clearly about what changed.
The best fit is someone with real stake in a domain or problem, plus enough patience to turn that interest into a concrete study.
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