AGW Anil Gypsy Wild Academic CV

Academic Curriculum Vitae

Anil Gypsy Wild

Senior quantitative UX researcher and HCI-trained statistical programmer working across AI evaluation, survey measurement, behavioral analytics, experimentation, segmentation, and product strategy.

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Research that moves from ambiguity to action.

15+ yearsApplied research across enterprise, consumer, healthcare, autonomous systems, and AI-enabled products.
Ph.D.Computer science foundation with HCI, adaptive systems, behavioral modeling, and statistics.
Quant + qualSurveys, telemetry, interviews, workflow analysis, segmentation, and experimental thinking.
AI trustTrust, distrust, reliance, interpretability, workflow fit, user control, and decision support.

Selected Experience

Applied research leadership

Independent consulting and applied research | 2024-2026

Senior quantitative UX research, product strategy, and statistical consulting

  • Designed research programs connecting product questions to measurable behavior, trust, friction, and decision quality.
  • Built survey and analysis workflows using R, Python, SQL, preference modeling, segmentation, and decision-oriented reporting.
  • Produced confidentiality-safe evidence packs, research proposals, simulation notebooks, and stakeholder-ready summaries.

AI, autonomy, and advanced product systems

Google X / Wing, Microsoft Research, and AI evaluation work

  • Led and supported research around autonomous delivery, AI-assisted reasoning, trust-sensitive workflows, and model-driven decision support.
  • Developed measurement approaches for trust, distrust, reliance, interpretability, system acceptance, and human control.
  • Translated complex technical behavior into research questions that product, design, and engineering teams could act on.

Enterprise and platform research

Docker, Cloudera, Workday, RightScale/Flexera, and startup-stage work

  • Conducted mixed-method and quantitative research for complex workflows, developer tools, cloud platforms, enterprise software, and adoption problems.
  • Built segmentation systems, onboarding research, taxonomy validation, heuristic evaluations, benchmark studies, and journey diagnostics.
  • Helped teams choose practical next steps when evidence was incomplete, noisy, or distributed across multiple data sources.

Methods

Statistical and research toolkit

Survey measurement

Scale development, benchmarking, Likert and ordinal modeling, confidence intervals, and power simulation.

Preference modeling

MaxDiff, conjoint-style studies, ranking tasks, segmentation, clustering, classification, and tradeoff analysis.

Behavioral analytics

Funnel diagnosis, cohort analysis, telemetry interpretation, prototype comparison, and decision-ready synthesis.

Research programming

R, Python, SQL, reusable notebooks, reproducible analysis, and stakeholder-facing research artifacts.

Research Record

Public and public-safe contribution areas

Google Scholar

Publication and citation index

Use Google Scholar as the public index for publications and citation history.

Open Google Scholar

August 2008 | University of Nevada, Reno

SYCOPHANT: A Context Based Generalized User Modeling Framework for Desktop Applications

Doctoral thesis on context-aware adaptive desktop applications and user modeling.

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May 2016 | ACM CHI

Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry

Peer-reviewed work using clickstreams and telemetry to construct behavioral persona systems.

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Confidential Work

Public boundaries

Some of my most substantial AI and product research is confidential client or employer work. I describe methods, problem classes, and public-safe outcomes here, but underlying artifacts, source materials, and proprietary research details are not shared without permission.