Learning Design  Technology Strategy
for Data-Informed, AI-Enabled Education Workforce Development

I partner with colleges, universities, government, and mission-driven organizations to design and scale digital initiatives that improve student success and workforce readiness.

• 20+ years managing complex, cross-functional learning and
  technology programs
• Doctoral research translating AI-enabled adaptive learning into
  practice
• Experience spanning higher education, government, and edtech

Professional Experience

Scalable Digital Learning Transformation for Student Success

Led cross-functional initiatives to scale high-quality online learning aligned with institutional student success goals.

• Managed a portfolio of 12 course transformations across undergraduate, graduate, and doctoral programs
• Directed a 10-person digital learning operations team supporting program directors and faculty
• Designed and implemented new workflows, permissions, and quality assurance for the course content management system
• Partnered with academic leadership to launch accelerated graduate programs using Rapid Prototype Development

Impact: Established scalable course delivery processes, strengthened faculty capacity, and improved alignment between learning design, program outcomes, and student success metrics.

Enterprise Workforce Software Training for a Distributed Federal Environment

Managed the design and rollout of a large-scale digital training program supporting a technology transition for a distributed workforce.

• Led a team of instructional designers developing accessible, scenario-based online training
• Supported adoption of new operating systems, productivity tools, and collaboration technologies for more than 15,000 staff and contractors
• Used stakeholder feedback and performance data to continuously refine content and delivery

Impact: Accelerated workforce adoption of new tools and workflows while ensuring accessible, scalable training aligned with federal performance and compliance requirements.

Benchmark Datasets for AI Career Guidance in Computer Science Reskilling

Contributed to the development of three benchmark datasets built from a large-scale career-advice platform with more than 60,000 crowdsourced questions and 3.5 million learners. The initiative focused on improving AI-driven career navigation and reskilling pathways in technical fields.

• Reviewed and scored question/answer data using structured quality criteria to strengthen accuracy, clarity, and relevance
• Applied mixed-methods research and qualitative analysis to identify patterns and support dataset refinement
• Provided domain expertise on career pathways, credential requirements, and common reskilling routes in computer science occupations
• Supported the deployment of high-quality data for more personalized and equitable AI guidance

Impact: Enabled more reliable benchmark datasets for evaluating AI models and improving the accuracy, personalization, and equity of career guidance at scale.