What started with tinkering on an Omega computer with a cousin in rural North Carolina led to a career bridging industry and education. This CS educator spent years in programming, systems administration, and disaster recovery at IBM before realizing their true calling: explaining difficult concepts in ways that make them accessible.
Now working at Data Science for Everyone, they're developing K-12 learning progressions for data literacy—because in today's world, working with data and computational tools shouldn't be reserved for CS electives. The vision is bold: integrate data science across all subjects so every teacher can recognize data literacy opportunities in their content area, with CS educators as frontline support.
Their teaching philosophy is refreshingly human: normalize problem-solving, celebrate without surprise ("I knew you could do it"), and create spaces like the "Hidden Figures room" where students work together at their own pace. They emphasize academic risk-taking—school is the safe place to take big swings—and learning to internalize feedback as growth, not criticism.
On AI, their stance is clear: students need to understand the math, statistics, and algorithms behind the "magic." AI is a tool that elevates existing expertise—like giving a chef better pans—not a replacement for human development, lived experience, or the connections that spark true creativity. In an age where attention is monetized, students need computational literacy paired with self-reflection, relationship-building, and perseverance.
