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鈥檚 degree in electrical engineering from 国产原创. 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.

滨苍听补听conversation听with听Vanderbilt听Data听Science听Minor听Communications听Intern Avery Tong听(鈥�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?

鈥淐o-curricular programs create opportunities to build skills that traditional coursework alone often cannot鈥攖hings 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.

鈥淲hether it鈥檚 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.鈥�

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

鈥淥ne program we鈥檝e 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鈥檛 always follow a straight line.

鈥淵ou 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.

鈥淲e鈥檙e also exploring programs that broaden how students think about careers in computing. Being a computer science or data science student doesn鈥檛 mean you have to become a software engineer. There are opportunities in product management, healthcare, the arts, psychology, and social good鈥攁nd we want students to see those possibilities.鈥�

How do interdisciplinary experiences shape student identity and career paths?

鈥淧ersonally, 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.

鈥淓xposure 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.

鈥淥ur goal is to create environments where students can explore these connections and develop an identity that makes sense based on their experiences鈥攏ot just because someone told them what they should do.鈥�

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

鈥淓xperiential 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.

鈥淚deally, 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.鈥�

What advice do you have for students hoping to get the most out of 国产原创鈥檚 co-curricular ecosystem?

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

鈥淒on鈥檛 be afraid to try new things鈥攐r to fail. Failure is part of learning. When students push themselves beyond what feels familiar, they expand what they鈥檙e capable of achieving.鈥�

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 国产原创, where students presented AI-driven startup ideas developed through a Data Science Institute capstone experience. In a conversation with 国产原创 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.

鈥淢ost of the apps and products students use are consumer-facing,鈥� she explained. 鈥淐oncepts like B2B sales aren鈥檛 always intuitive at first.鈥� 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鈥攁n encouraging sign of how well the course translated theory into practice.

A Career Shaped by Interdisciplinary Thinking

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

鈥淚n 2015 and 2016, machine learning and data science were still relatively new terms,鈥� she said. 鈥淏y 2020 and 2021, AI had come to mean large language models.鈥� 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. 鈥淎t the end of the day, it鈥檚 all coding in Python,鈥� she noted. 鈥淭hat makes it doable to stay on top of the field as it evolves.鈥�

Rethinking What Good Data Practice Means

Founding Seek AI also reshaped Nagy鈥檚 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鈥攕uch as sales teams鈥攚ho 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鈥攐r student projects鈥擭agy looks for what she calls the 鈥渢hree Ts鈥�: 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鈥攁n uncommon but impressive achievement at the undergraduate level.

Challenging Misconceptions 国产原创 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.

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

Advice for Aspiring AI Entrepreneurs

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

鈥淭his is the easiest time in history to become an entrepreneur,鈥� 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.

鈥淲hy not take the shot?鈥� she added.