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
Making AI tools that sharpen design judgment.
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 taste in an age of generative abundance.
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 principle
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
Why this work matters now
AI makes it easy to produce variants; my question is how people learn to choose well.
Everyone is closer to the prototype.
Designers, researchers, PMs, and engineers all shape early artifacts now. Interfaces need to support shared reasoning before decisions harden.
Speed still needs reflection.
Fast cycles are useful only if teams keep room for critique, incubation, and explaining why a direction is better.
Research focus
Design, Evaluate, Situate.
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. Dimensions, variations, comparisons, and fidelity shifts become thinking surfaces.
Design lens
Human-AI for design thinking
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 DesignWeaver Moves
- 01
Externalize useful dimensions so people can see what matters.
- 02
Use variation and structured comparison to reason across alternatives.
- 03
Move from coarse ideas to committed artifact choices through staged fidelity.
Evaluate lens
Evaluation for design thinking
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 paper Evidence loop
- 01
Treat final artifact quality as one layer of evidence.
- 02
Connect traces and rationales to concrete design questions.
- 03
Measure explanation, preservation, revision, and transfer.
Situate lens
Interfaces fitted to context
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.
Situations
- 01
Let practice shape what assistance should do.
- 02
Match the interface to the medium and setting.
- 03
Make help fit the situation instead of interrupting it.
Exploring
Adjacent contexts for the same loop
These threads are places I test the same concern with judgment, agency, and evidence in context.
Situate Everyday robotics Integrating intelligence into robots so they can better assist people in everyday living and working contexts. Design Learner-centered AI Scalable support that keeps agency and the learning process visible. Evaluate Post-deployment iteration Using traces from real use to improve benefit and catch harm. Situate Community-centered tools Coordination and sensemaking for groups with shared ownership.
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 .
Selected publications
Papers worth opening first.
- Short form Workshop First author 0 citesIn Herding CATs: Making Sense of Creative Activity Traces (CHI 2026 Workshop), Apr 2026Workshop Position Paper
Why cite this?
Cite this position paper when motivating rationale-enriched telemetry: short, trace-guided windows paired with optional in-flow clarification to diagnose ambiguous moments in creative or AI-supported work.
Full citation contextUseful when- Discussing rationale-enriched interaction logging or post-deployment diagnosis in creative and AI tools.
- Designing feedback interventions at likely friction points without moving users into a separate survey flow.
Scope- The proposed interventions have not yet been validated for insight quality, analysis time, interruption cost, or downstream agent training.
- Traces and clarifications do not by themselves establish causality; trigger selection may distract users or bias later behavior.
- Friction signals and clarification schemas must be adapted to the domain and the interaction touchpoints available in a particular tool.
- CVPR
Full paper Coauthor 16 citesIn Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2025HighlightWhy cite this?
Cite HotSpot for neural implicit surface reconstruction from unoriented points when the argument concerns sufficient SDF constraints, optimization stability, topology, or surface-area regularization.
Full citation contextUseful when- Comparing losses for neural signed distance functions rather than treating the eikonal condition as sufficient.
- Discussing stable reconstruction, distance accuracy, or topology from unoriented point observations, with sparse-boundary failure modes made explicit.
Scope- The experiments target 2D and 3D reconstruction from unoriented point positions, including a 260-shape, 13-category ShapeNet subset; they do not establish superiority for every implicit-representation task.
- Sparse boundary sampling, high absorption, or an over-strong heat term can tear boundaries or collapse a signed solution toward an unsigned distance.
- Boundary weight and spatial scaling remain tuning considerations.
- CHI
Full paper First author 38 citesIn Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Apr 2025Why cite this?
Cite DesignWeaver when discussing dimensional scaffolding, novice prompt construction, or interface support for richer and more varied exploration in text-to-image product design.
Full citation contextUseful when- Designing interfaces that externalize product-design dimensions instead of relying on a blank prompt box.
- Studying how prompt scaffolds change vocabulary, iteration, visual diversity, novelty, and user expectations.
Scope- The controlled study involved 52 novices, ages 19–31, completing a chair-design task; experienced-designer, collaborative, and other-domain use remain open questions.
- Preset dimensions may constrain creativity as well as scaffold it.
- Richer prompts can raise expectations beyond what current text-to-image models deliver reliably.
- NeurIPS
Full paper Coauthor 172 citesIn Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, Dec 2021Why cite this?
Cite Physion for human-aligned intuitive-physics benchmarking, object-centric physical prediction, or generalization across diverse simulated scenario families.
Full citation contextUseful when- Evaluating whether a vision model predicts physical outcomes in ways that approach human accuracy and error patterns.
- Motivating object-centric representations, object-contact prediction, or transfer across physical scenario families.
Scope- Physion uses synthetic ThreeDWorld scenes and a binary contact-prediction task; it does not cover all real-world materials, fluid behavior, jointed multipart objects, or property ranges.
- Particle-based state models receive ground-truth 3D physical state that a vision system must otherwise infer.
- Strong within-benchmark performance does not by itself establish broad real-world physical understanding or transfer from a single scenario family.
Updates
Recent signals.
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
For students who like messy questions.
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.
Reading closely Prototyping Evaluation Analysis Communication
Start here
- Read the research-start post.
- Email s1tao@ucsd.edu with subject "UCSD Research Interest".
- Include a 1-page CV or resume, an optional portfolio link, and a 3-5 sentence note on the questions or domains you care about.
Connect
Find me around the web.
Advisor Lineage & Past Collaborations
Ph.D. Steven P. Dow HCI
Master's Steven P. Dow HCI Tzu-Mao Li Graphics
Undergrad Judith E. Fan Cognition & Intuitive Physics