Inspire 2026 Faculty on AI Literacy Bootcamp & Foundations of Data Science Summer Courses
"I love interacting with the next generation of Data Science students! It’s really exciting to see students develop a passion for these topics." - Brian Wright
AI Literacy Bootcamp: From Machine Learning to Generative AI and Foundations of Data Science invite students to explore how data and artificial intelligence shape the world around us. From building and testing AI models to visualizing data and examining ethical implications, these hands-on courses introduce the tools, concepts, and real-world applications driving today’s most innovative technologies. Below, meet Brian Wright and Mai Dashan, the instructors for these brand-new Inspire courses for Summer 2026!
Meet the Instructors:
Q: Tell us about your journey into data science and how you developed your expertise in the field?
Mai: My journey into data science has been driven by a passion for understanding how we can make sense of complex information and use it to solve real-world problems. I earned my Ph.D. in Computer Science from Virginia Tech, where my research focused on visual analytics and human-machine collaboration—essentially, how people and AI systems can work together to explore and analyze large-scale scientific data. This work took me to exciting places, including a summer internship at Los Alamos National Laboratory, where I worked on high-performance computing systems and saw firsthand how data science operates at scale. After completing my Ph.D., I worked as an Assistant Professor at the University of North Florida, teaching courses from programming fundamentals to graduate-level data science, which deepened my commitment to making AI education accessible to all learners. Now, as an Assistant Professor at UVA’s School of Data Science, I continue to bridge research and education, with my research spanning visual data analytics, human-AI collaboration, and AI in education. What drives me is the belief that data science and AI shouldn’t be mysterious black boxes, and I’m particularly passionate about bringing AI education into K-12 settings and using AI to develop innovative solutions for young learners. Whether I’m working with scientists analyzing complex datasets, undergraduate students learning their first programming language, elementary teachers integrating computational thinking into math lessons, or high schoolers exploring AI concepts, my goal is always the same—to demystify data science and empower people to use these tools thoughtfully and critically.
Brian: I started out working as a federal employee for the Department of Defense doing budget and financial analysis. This earlier career led to me realizing how much I enjoyed working with numbers and helping to solve problems analytically. Through this work I was able to return to my alma mater at University of Tennessee in Knoxville where I worked on a federally funded institute that focused on building courses and using quantitative research methods to address DoD projects. While working in this institute I was completing my PhD at night and realizing that focusing on emerging data science methods and teaching is how I wanted to shape my career moving forward. This led me to accept a position at George Washington University that focused on building their Data Science programs and eventually as a faculty member and Director of Undergraduate programs at UVA’s School of Data Science.
Q: What knowledge and skills can students expect to learn in AI Literacy Bootcamp: From Machine Learning to Generative AI and Foundations of Data Science this summer?
Mai: In AI Literacy Bootcamp: From Machine Learning to Generative AI, students will gain a comprehensive understanding of artificial intelligence from both technical and ethical perspectives. Students will learn to explain the difference between AI, machine learning, and deep learning, and understand how AI systems actually learn from data. Through hands-on activities using beginner-friendly no-code platforms, they’ll build and test their own AI models, going through the complete machine learning workflow from data collection to deployment. We’ll dive deep into computer vision and natural language processing, exploring how AI sees and interprets images, recognizes patterns, and processes text in applications like facial recognition, chatbots, and translation tools. Students will develop skills to identify bias in AI systems, detect AI-generated content, and think through important ethical questions about privacy, fairness, and AI’s societal impact. By the end of the week, students will have practical AI skills, critical thinking tools to evaluate AI systems, and confidence to navigate an AI-powered future.
Brian: In Foundations of Data Science (DS 1001), students learn the foundational concepts, tools, and ways of thinking that define the data science process from start to finish. The course emphasizes the Virginia Model of Data Science, which frames learning around four key domains: Systems, Design, Analytics, and Value. Students gain practical experience setting up a computing environment, using Python and Jupyter notebooks, creating data visualizations, and exploring data through summary statistics and visual design. They also learn how to build and evaluate simple models, distinguish between features and target variables, and interpret results using evidence-based reasoning. Throughout the course, they practice “thinking like a data scientist,” approaching problems systematically, using data to inform decisions, and communicating insights clearly.
In addition to technical skills, students explore the ethical and societal dimensions of data science by examining how bias, error, and responsibility appear throughout the data pipeline. Weekly labs and case studies give students hands on opportunities to apply what they learn, culminating in a guided project where they design and execute their own data driven investigation. By the end of the summer, students can define what data science is, describe its subfields, and demonstrate the ability to analyze data, interpret models, and reflect on the real-world impact of data-driven decisions.
Q: Do students need a background in data science or AI to be successful in these classes?
Mai: Absolutely not! The AI Bootcamp is specifically designed for students with no prior experience in data science, AI, or coding. What matters most is curiosity, willingness to engage with new ideas, and interest in understanding how the technology shaping our world actually works. Whether you’re interested in technology, art, social issues, or just want to understand the AI tools you use every day, this bootcamp will give you the knowledge and skills to engage confidently with artificial intelligence.
Brian: No, these courses are designed to introduce the topics in a very approachable way using best practices in active learning to engage students throughout the sessions.
Q: What advice do you have for students interested in taking your classes this summer?
Mai: Come with an open mind and curiosity! The AI Bootcamp is designed to be interactive and collaborative, so be ready to participate in discussions, ask questions, and work with peers. Don’t worry if you’ve never coded or if AI seems intimidating—that’s exactly who this bootcamp is for. Before bootcamp starts, I would encourage you to notice AI in your daily life: Which apps do you use that might involve AI? When you interact with Snapchat filters, or music recommendations, think about how those systems might work. Bring your observations and questions to the bootcamp—real-world examples make the best learning opportunities. During the week, challenge yourself to think critically. AI is a powerful tool, but it’s not magic, and it’s not always right. Part of AI literacy is understanding both what AI can do and what it shouldn’t do.
Brian: Students can certainly reach out to Mai or myself if they want more information but I would say the course can give students a real advantage as they move on to college, as this content is not often found in high schools and provides nice groundwork for students interested in pursuing a data science career. The course is fast paced but very approachable, students should be willing to challenge themselves.
Q: What are you most looking forward to about Inspire this summer?
Mai: I’m most excited about working with curious, engaged high school students who are ready to explore AI at this pivotal moment in technology. We’re living through a major transformation with generative AI and other advances, and young people will be the ones shaping how these tools are used responsibly and creatively. I look forward to seeing students’ “aha moments” when they realize AI isn’t mysterious magic—it’s a set of learnable concepts they can understand, build with, and critique. I’m looking forward to empowering students to be informed, critical participants in an AI-driven world rather than passive consumers of technology. When students leave the bootcamp equipped to ask tough questions about AI systems, and imagine better ways to build technology, that’s when I know we’ve succeeded in preparing the next generation of thoughtful technology leaders and informed citizens.
Brian: I love interacting with the next generation of data science students! It’s really exciting to see students develop a passion for these topics.