UX Research Portfolio

Featured Research Projects

Case-study overviews centered on AI-first experiences, collaborative workspaces, and transparent, proactive, adaptive agents. Each project highlights prototyping plus mixed methods (qualitative + quantitative) and shows how impact evolved through feedback-driven iteration.

AI Agents Proactive + Adaptive Transparency Collaborative Workspaces Mixed Methods Prototyping
CHI 2026 • Proactive AI Roles
Conditionally Accepted • CHI 2026 AI-First Prototyping
Proactive Agents Transparency Mixed Methods (Qual + Quant)

Exploring the Impact of Proactive Generative AI Agent Roles in Time-Sensitive Collaboration

UX research on how proactive, transparent agents shape teamwork, memory support, and flow in time-sensitive collaboration.

Methods

  • Technology probes of proactive/adaptive agent behaviors.
  • Mixed methods: logs, surveys, and qualitative interviews.

Results

  • Peer agents improved memory support but sometimes disrupted flow.
  • Facilitator agents clarified coordination with limited outcome gains.

Impact

  • Iterated transparency cues to reduce over-reliance.
  • Guidance for AI-first collaborative workspaces.
Project Details Paper PDF
CHI 2025 • OSINT Clinic
CHI 2025 Co-Design + Mixed Methods
Collaborative Agents Transparency AI-First Workflows

OSINT Clinic: Co-designing AI-Augmented Collaborative Investigations

UX research on AI-first collaborative investigations and how transparent agents shape team decision quality.

Methods

  • Co-design with rapid prototyping and feedback loops.
  • Mixed methods: interviews, surveys, telemetry.

Results

  • AI streamlined workflows and improved alignment.
  • Transparency gaps reduced trust in proactive recommendations.

Impact

  • Iterated transparency to improve trust.
  • Blueprint for AI-first collaborative workspaces.
Project Details ACM DL
CSCW 2024 • Research Studios
CSCW 2024 Prototyping + Mixed Methods
Collaborative Workspaces Adaptive Agents Transparency

OSINT Research Studios: Scaling Expert-Led Investigations

UX research on a collaborative workspace with adaptive support for expert-led investigations.

Methods

  • Design-based research with iterative prototyping.
  • Mixed methods: quality, workload, collaboration.

Results

  • Improved throughput while maintaining quality.
  • Reduced dependence on long-term expert-crowd ties.

Impact

  • Iterated workflows to reduce collaboration friction.
  • Reusable framework for AI-supported collaboration.
Project Details ACM DL
DIS 2023 • CoSINT
DIS 2023 Prototyping + Mixed Methods
Collaborative Agents Proactive Support AI-First Experiences

CoSINT: Collaborative Capture-the-Flag for Misinformation

UX research on proactive guidance and collaboration in high-stakes, time-boxed investigations.

Methods

  • Research-through-design with rapid prototyping.
  • Mixed methods: performance, trust, collaboration.

Results

  • Proactive cues and collaboration accelerated debunking.
  • Incentives sometimes conflicted with teamwork.

Impact

  • Iterated incentives based on feedback to sustain teamwork.
  • Informs AI-first collaborative experiences.
Project Details DOI