Human-Centered Systems & Experience Design
I work with clients, users, and delivery teams to understand complex systems, make the work visible, test what should change, and carry experience intent through implementation.
My work connects generative and evaluative research, co-creation, service and workflow design, storytelling, information and data visualization, interaction architecture, technical prototyping, human–AI interaction, stakeholder alignment, and implementation.
Start with evidence before defining the interface.
I use interviews, observation, workflow studies, facilitation, co-creation, contextual inquiry, prototypes, and validation to understand what people are actually trying to accomplish and which organizational, policy, data, platform, and technical conditions shape the problem.
Research becomes useful when it changes what the team can see and decide. I synthesize evidence into clearer needs, experience principles, priorities, narratives, and testable directions that clients, users, designers, product teams, and engineers can evaluate together.
Make the system visible enough to challenge and improve.
Many difficult experiences are not screen problems. They span roles, handoffs, approvals, information movement, policy, data, automation, multiple channels, and technical constraints.
Service maps, workflows, prototypes, information structures, visualizations, working code, and end-to-end stories make those relationships concrete enough to inspect. That shared visibility helps teams remove ambiguity while decisions are still inexpensive to change.
Keep human agency visible as automation increases.
In AI-enabled systems, usability includes understanding what the system knows, where an answer came from, what remains uncertain, what should be reviewed, when automation should stop, and who remains accountable for the decision.
I treat evidence, provenance, review points, escalation paths, source visibility, model constraints, and human control as interaction and workflow concerns—not as a separate governance exercise added after the experience has been designed.
I work directly with clients, end users, business owners, technical teams, and program stakeholders to understand the problem before defining the solution.
Generative research helps reveal needs, constraints, mental models, workarounds, and opportunities. Evaluative research and prototyping test whether emerging directions actually improve the work. Workshops and co-creation create shared ownership of the evidence rather than using facilitation as a substitute for it.
I map roles, handoffs, approvals, exceptions, channels, information movement, policy, and system behavior into end-to-end service and workflow structures.
That makes it easier to see where responsibility is unclear, where people are compensating for system gaps, and where simplification, automation, or better interaction design can improve both the user experience and the operating process behind it.
Complex systems often fail because the right information is present but not organized for human understanding. I use information architecture, data visualization, interaction design, and storytelling to make state, relationships, uncertainty, exceptions, and next actions easier to understand.
The goal is not merely to present more data. It is to make the information needed for judgment visible at the moment a person has to act.
I design the human-facing system around AI capability: purpose, knowledge structure, retrieval behavior, instructions, evidence, output constraints, review gates, escalation, source visibility, and human control.
I test for drift, conflicting evidence, edge cases, uncertainty, and non-linear interaction paths so AI-supported workflows remain inspectable and useful as models, corpora, and instructions evolve.
I stay close to implementation because technical feasibility, interaction behavior, and experience intent are easier to reconcile before they become separate workstreams.
Depending on the problem, I work with wireframes, high-fidelity prototypes, HTML/CSS, Angular, live data, reusable components, and production code to test behavior and communicate decisions in a form technical teams can build from.
I facilitated Air Force stakeholders around assessment, reassessment, approvals, signatures, and mobile approvers, mapping roles and handoffs across paper, PDF, and digital systems to define workable end-to-end service processes.
Related work used Qlik dashboards, R Shiny prototypes, NLP, data visualization, and high-fidelity HTML/live-data prototypes to make complex operational behavior concrete enough for stakeholders to inspect, challenge, and refine.
On an Army geospatial application, I worked directly in Angular as a UX partner and front-end developer, using working code and browser tools to test interaction behavior while supporting an Angular modernization from v14 to v16.
That implementation proximity kept user needs, responsive behavior, interaction intent, technical constraints, and component decisions connected through delivery.
At Softwise, I led experience architecture across five financial products and co-architected a shared Angular / NativeScript application system supporting desktop web, mobile web, Android, and iOS from a largely common codebase.
I maintained reusable experience patterns in SoftwiseLibrary and worked directly in implementation; management credited the shared approach with reducing development effort by approximately 35%.
My current independent work uses custom GPTs and AI-agent workflows for knowledge retrieval, professional representation, opportunity evaluation, and decision support.
I architect structured corpora and knowledge systems around retrieval, evidence, provenance, model constraints, human review, escalation, and source visibility, treating human agency and inspectability as experience requirements.