Robert Amponsah, Ed.D.

In his role, Amponsah oversees marketing and communications for the CCC, helping define its mission and articulate its value to students, faculty, and industry partners. He also leads initiatives related to student programs and experiential learning, supporting students from their first year to career readiness.

Amponsah earned his bachelor’s degree in electrical engineering from ÎĺŇ»˛čąÝ¶ů. While initially trained as an engineer, he has since pursued systems engineering, interdisciplinary research, and applied work that connects engineering with music and acoustics. His professional experience includes work in musical acoustics and industry applications such as noise-reduction technologies.

±ő˛ÔĚý˛ąĚýconversationĚýwithĚýVanderbiltĚýDataĚýScienceĚýMinorĚýCommunicationsĚýIntern Avery TongĚý(’27), he discussed the role of co-curricular learning, interdisciplinary pathways, and career preparation in computing education.

What role does co-curricular learning play in computing education?

“Co-curricular programs create opportunities to build skills that traditional coursework alone often cannot—things like leadership, entrepreneurship, and innovation. While students gain technical knowledge in the classroom, co-curricular experiences allow them to apply that knowledge in real-world contexts.

“Whether it’s through mentorship, site visits, hackathons, or industry exposure, students can start to see how concepts like generative AI or data science apply across different fields such as manufacturing, retail, or healthcare. These experiences help students build perspective and confidence as they prepare for the workplace.”

What are some of the most exciting programs you are developing for students?

“One program we’ve launched is called Plot Twist, a speaker series that brings alumni back to campus to talk about the unexpected paths they took in their careers. The goal is to show students that success doesn’t always follow a straight line.

“You might hear from someone who studied history but went on to start an AI company. By hearing about those twists and turns, students can better understand how interdisciplinary experiences shape careers.

“We’re also exploring programs that broaden how students think about careers in computing. Being a computer science or data science student doesn’t mean you have to become a software engineer. There are opportunities in product management, healthcare, the arts, psychology, and social good—and we want students to see those possibilities.”

How do interdisciplinary experiences shape student identity and career paths?

“Personally, I started in electrical engineering, but my career evolved into systems engineering and eventually into musical acoustics. That intersection between engineering and music led me to work on noise-reduction headphones at Bose.

“Exposure to interdisciplinary spaces is critical. When students see how biology connects to computing or how the arts intersect with technology, they can discover paths that align with both their interests and competencies.

“Our goal is to create environments where students can explore these connections and develop an identity that makes sense based on their experiences—not just because someone told them what they should do.”

How do you envision co-curricular initiatives connecting with the classroom?

“Experiential learning is becoming increasingly important. There are opportunities for co-curricular programs to complement classroom instruction through guest speakers, leadership certificates, and career-focused experiences.

“Ideally, what happens outside the classroom reinforces what students are learning inside it. That kind of integration can help students better understand how their education connects to real-world applications.”

What advice do you have for students hoping to get the most out of Vanderbilt’s co-curricular ecosystem?

“Put yourself in spaces where you might feel a little uncomfortable. Those moments often drive innovation and personal growth.

“Don’t be afraid to try new things—or to fail. Failure is part of learning. When students push themselves beyond what feels familiar, they expand what they’re capable of achieving.”

Nagy now works closely with IBM on initiatives that connect industry practice with education and early-stage innovation. As part of this work, she recently attended an IBM-affiliated student pitch competition hosted at ÎĺŇ»˛čąÝ¶ů, where students presented AI-driven startup ideas developed through a Data Science Institute capstone experience. In a conversation with Vanderbilt Data Science Minor Communications Intern Rosie Feng (’26), Nagy reflected on her career path, evolving perspectives on data practice, and what she believes sets future AI leaders apart.

Learning to Think Like Builders

For Nagy, one of the most exciting aspects of the IBM pitch competition was seeing how quickly students adapted to business-oriented thinking. This area can feel unfamiliar to those with primarily technical backgrounds.

“Most of the apps and products students use are consumer-facing,” she explained. “Concepts like B2B sales aren’t always intuitive at first.” Despite that, Nagy was impressed by how effectively students absorbed new ideas around market fit, pitching, and customer needs.

Working alongside IBM Ventures, students presented mock startups in a pitch format similar to Shark Tank. According to Nagy, the Ventures team noted that many of the student companies closely resembled real startups they encounter in industry—an encouraging sign of how well the course translated theory into practice.

A Career Shaped by Interdisciplinary Thinking

Nagy’s path into AI reflects the interdisciplinary nature of the field itself. Beginning in physics, she transitioned into quantitative finance—a mathematically rigorous discipline that later converged with data science and machine learning. As these tools evolved, so did her work.

“In 2015 and 2016, machine learning and data science were still relatively new terms,” she said. “By 2020 and 2021, AI had come to mean large language models.” Today, her focus has shifted again toward AI agents, which she sees as the next major stage of development.

Despite rapid changes in terminology and applications, Nagy emphasized that the underlying technical foundation remains approachable. “At the end of the day, it’s all coding in Python,” she noted. “That makes it doable to stay on top of the field as it evolves.”

Rethinking What Good Data Practice Means

Founding Seek AI also reshaped Nagy’s perspective on data quality. While clean, accurate data is a well-known ideal, she pointed out how difficult it can be to achieve in real organizational settings.

In many businesses, data entry depends on busy employees—such as sales teams—who may not prioritize detailed documentation amid packed schedules. This can introduce inconsistencies early in the data lifecycle.

Nagy sees AI as a powerful tool for addressing this challenge. AI systems that assist with tasks such as meeting monitoring and automated data capture can reduce human error at the source, leading to cleaner, more reliable datasets over time.

What Makes a Strong AI Pitch

When evaluating startups—or student projects—Nagy looks for what she calls the “three Ts”: team, technology, and traction.

Team refers to why a group is uniquely positioned to solve a particular problem. Technology asks whether the solution is defensible, novel, or newly possible. Traction considers market size and early customer validation. During the capstone course, Nagy noted that several student teams demonstrated real traction, including engagement with actual customers—an uncommon but impressive achievement at the undergraduate level.

Challenging Misconceptions ÎĺŇ»˛čąÝ¶ů AI Accuracy

One of the most common misunderstandings Nagy encounters is the expectation that AI tools should be perfectly accurate. She finds this standard inconsistent with how human analysts are evaluated.

“In my years as a quant and data scientist, no one ever asked if I was 100 percent accurate,” she said. Instead of seeking perfection, she encourages organizations to assess AI based on benchmarks and transparency—particularly whether the system can clearly communicate the steps it took to conclude.

Advice for Aspiring AI Entrepreneurs

For students considering entrepreneurship, Nagy’s advice is simple: start now.

“This is the easiest time in history to become an entrepreneur,” she said. With accessible tools, open-source models, and rapidly expanding AI infrastructure, the barriers to entry are lower than ever. Whether or not success comes immediately, Nagy believes the experience itself is invaluable.

“Why not take the shot?” she added.