Hi, I'm Marc Woo.

Product designer shaping the next generation of search and discovery.

I specialize in search and complex navigation, helping millions of users find exactly what they need.

AI-Integrated Discovery Strategy

Dick's Sporting Goods﹒2025 – Present

Defined the strategic vision for shifting the core discovery loop from reactive keyword matching to anticipatory intent prediction. This 0-to-1 roadmap was selected for presentation to Investor Relations as a key driver for future revenue growth.

Context-Aware UI Construction

Synthesizing high-level intent into dynamic layouts.

Designed a system that "reads the room," using implicit signals (location, time, history) to reconstruct the UI in real-time. The interface proactively surfaces relevant filters and content, reducing the friction of manual search.

LLM-Powered Decision Support

Aggregating fragmented metadata into a cohesive summary.

Leveraged AI to synthesize thousands of data points (Expert Tips, Inventory, Reviews) into clear, actionable highlights. This progressive refinement loop builds decision confidence by simplifying complex product data.

Search and Navigation Architecture

Dick's Sporting Goods﹒2022 – Present

Re-architected the foundational logic for product discovery, transforming a fragmented legacy schema into a single, adaptive system. This infrastructure reduced technical debt while enabling scalable, intent-based retrieval for millions of customer queries while reducing code weight.

Mobile Menu Optimization

Cut exit rates 30% by minimizing decision fatigue.

Overhauled mobile wayfinding by auditing interaction costs and simplifying hierarchy. The new architecture reduced cognitive load and streamlined the taxonomy management process.

Semantic Search Experience

Translating complex ML signals into human-readable results

Designed the visual framework for Vector Search, translating complex ML predictions into transparent, trustworthy results—even for vague customer queries.

Global Navigation Architecture

Eliminating layout shifts (CLS) to prioritize discoverability.

Optimized the global header to solve layout instability. Compressed navigation height by 35% without sacrificing utility, prioritizing immediate content visibility to build trust within the first millisecond of interaction.

Browse Ecosystem and Decision Support

Dick's Sporting Goods﹒2022 – Present

Restructured the core browse framework to solve for "choice paralysis." Developed a scalable information architecture that organizes complex inventory into comparable patterns, accelerating the customer journey from exploration to conversion.

Responsive Relevance Loops

Validating user intent with immediate visual feedback (18% filter engagement lift).

This "emotional detail" confirms to the user that the system is listening, drastically reducing abandonment during deep browsing sessions.

Compare Tool

Reducing information asymmetry to accelerate high-consideration purchases

Led the 0-to-1 design of a side-by-side comparison tool, cited by customers as a critical feature for building confidence. By normalizing complex specs into scannable patterns, we reduced the need for users to "tab switch" to external sites for validation, keeping the discovery loop contained within the ecosystem.

Generative Engine Optimization

Synthesizing fragmented metadata into decision-driving narratives.

Optimized content hierarchies for Generative Engine Optimization (GEO). Ensures product data is structured for AI retrieval while simultaneously providing high-value, in-flow education for customers.

Geospatial Health Mapping for IoT System

Novosselov Research Group﹒2021 – 2022

Designed the companion interface for AeroSpec, a portable IoT sensor system that transforms raw environmental telemetry into intuitive, actionable health signals. This architecture allows users to discover hyper-local safety trends through high-fidelity geospatial visualizations.

Longitudinal Validation

De-risking engineering investment through rigorous consumer science.

Validated the data model via 54 surveys and 3 diary studies before code commitment. This ensured the interface solved real-world respiratory needs rather than just displaying available telemetry.

Privacy-First Spatial Indexing

Abstracting individual data points into community-level safety signals.

Architected a hexagonal grid system leveraging Uber’s H3 spatial index to solve the "utility vs. privacy" trade-off. This implementation enables high-resolution community mapping without compromising the precise location of individual contributors.

Interactive Discovery Logic

Prioritizing immediate safety signals over raw data

Utilized progressive disclosure to prevent data overwhelm. The interface highlights critical "Safe/Unsafe" signals first, revealing granular pollutant metrics only upon explicit user interaction.