This award is presented to an outstanding educator in Middle Tennessee who has exhibited exceptional dedication to and innovation in the advancement of technology education, as identified through open nominations.

When the data science minor launched in Fall 2021, Dr. Bell took the reins as director to continue the vision of making it a home for students from all disciplines. Today, the program welcomes psychologists, historians, engineers, musicians, philosophers, teachers, and everyone in between. She continues to prioritize growth in both capacity and quality, working to create space for all who want to learn. Beyond the minor, she has developed and taught several generative AI courses at Vanderbilt, including a graduate course on designing and deploying generative AI systems for proprietary products. Since the program began, she has advised over 1,200 students and speaks with nearly one in twelve undergraduates each year. If data science or generative AI sounds relevant for your future, the DS Minor is hoping to hear from you next at our upcoming events!

Hear firsthand stories from our amazing students, gain valuable insights, and enjoy free food!

Don’t miss this chance to connect, learn, and get inspired!

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 ������ý����. 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.

����������conversation��with��Vanderbilt��Data��Science��Minor��Communications��Intern Avery Tong��(’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 ������ý����, 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 ������ý���� 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.

Registration is now open for Vanderbilt’s AI Showcase Fall 2025, an official College of Connected Computing event on Friday, December 5 at 2:00 p.m.

Reserve your seat or submit a project for a chance at $1,700 in total awards. (Nomination deadline: Monday, December 1 at noon)

Curious how your peers are applying data science across campus? We’re excited to launch a new Student Spotlight series highlighting the incredible work DS Minors are doing in research labs, internships, coursework, and beyond. Each feature offers a closer look at how students are using the skills learned in the classroom to tackle real-world problems — from building machine learning models to uncovering insights through visualization and storytelling.

If you’re interested in being featured in an upcoming Spotlight or want to nominate a classmate, to learn more and submit your information. New Spotlight stories will be shared throughout the Spring semester, so stay tuned!

Data science is not just about gaining a technical skill –
it’s about empowerment.

– Md Kamrul Hasan, Ph.D.

is an��Assistant Professor of the Practice of Computer Science at ������ý����. His research focuses on smartphone-based diagnostic techniques, particularly non-invasive, point-of-care tools. Dr. Hasan’s work explores how smartphone-captured images can be transformed into meaningful signals, with applications such as estimating blood constituent levels.

He earned his Ph.D. from Marquette University. Dr. Hasan was the recipient of the Northwestern Mutual Postdoctoral Fellowship in the Department of Computer Science at Marquette University. After completing a postdoctoral fellowship at ������ý���� Medical Center, he joined Vanderbilt.

He has received several prestigious awards, including the Arthur J. SchmittLeadership Fellowship (2018) at Marquette University, the Japanese Government Monbukagakusho (MEXT) Scholarship (2014) at Kyushu University, Japan, and the Opera Universitaria Scholarship (2007–2009) from the University of Trento, Italy.

What brought a computer science professor to teaching in the field of Data Science?

“My background spans computer science, data science, and biomedical informatics. Through my research and professional experience,��I have learned that data-driven thinking is central to nearly every discipline—from healthcare and finance to sports, education, and public policy. This realization motivated me to continue teaching Data Science, as it allows me to blend the strengths of computer science—problem-solving, algorithms, and programming—with the creativity and real-world impact of interpreting data.

“When I joined the Computer Science department at Vanderbilt, I was invited to design and develop the Data Science course DS/CS 1100 – Applied Programming and Problem Solving with Python in 2021. That opportunity allowed me to integrate all of my previous experience—analyzing data, identifying patterns, and making informed decisions—into building the course syllabus and content. Later, I also developed an online version of DS/CS 1100.

“Since launching this course at ������ý���� in 2021, I have taught students from diverse academic backgrounds and with varying levels of programming experience. I truly enjoy helping them build meaningful connections between code and the world around them. It is incredibly fulfilling to guide students from writing their first lines of Python to uncovering insights from real datasets.”

Why should a student take DS/CS 1100?

“DS 1100 is designed for Data Science minors who want to learn not only how to write code, but how to use it to answer real questions in their own fields—such as Economics, Electrical Engineering, or Mechanical Engineering. In my class:

• Students learn Python through practical, hands-on exercises rather than by memorizing syntax.
• Students work directly with the instructor during class using Jupyter Notebook. I upload a structured worksheet with datasets to Brightspace—often related to health, sports, or online shopping—so students can learn to explore, visualize, and analyze real data.
• The class is welcoming to beginners. No prior coding experience is required.
• Students leave the course with skills that are directly useful in internships, research projects, and upper-level courses. My goal is for them to say, ‘I can actually do data science,’ not just understand the definitions.”

Why do YOU think Data Science is important?

“Data Science is one of the most powerful tools of our time. We live in a world where decisions—both small and large—are increasingly guided by data. Whether it’s predicting stock market trends, assessing disease risk, improving transportation routes, or even recommending your next movie, data shapes our daily lives.

“If we can collect the right data, interpret it responsibly, and turn numbers into meaningful insights, we can make truly data-driven decisions. Data Science equips students with the ability to understand the world more deeply and to make informed, ethical choices.

“No matter what career path a student pursues—business, medicine, engineering, the social sciences, or the arts—data literacy is now essential. I believe that learning data science is not just about gaining a technical skill; it’s about empowerment.”

Interested in applying your skills towards providing feedback to students in introductory concepts and problems of data science as a grader? Or, ready to assist and guide other learners to better understand the concepts and skills of data science as a TA?

The Data Science Minor has a few positions open for Spring 2026 and is looking for excellent candidates to fill these last remaining roles. TA and grader positions are available for some of the most high-demand and compelling data science courses on campus:

• DS 1000: Introduction to Data Science
• DS/CS 1100: Applied Programming and Problem Solving with Python
• DS 2100: Statistics for Data Science
• DS 3100: Fundamentals of Data Science
• DS/CS 3262: Applied Machine Learning

As a TA or grader, you’ll have the chance to help learning individuals develop their skills in data science and provide them with constructive feedback and guidance. You’ll also work closely with leading faculty members in data science alongside a cohort of like-minded individuals.

To learn more about the two roles and common tasks of the positions and apply, visit the application link below! To be considered for the TA position, you must be enrolled in a graduate program at ������ý���� (VU). Graders may be undergraduate or graduate students at VU.

Applications are considered on a rolling basis, and��applications close on Friday, November 28th at 11:59pm. There are only a few positions available, so if you’re a highly motivated, reliable, conscientious individual with a passion for helping others learn the tools and skills of data science,��apply now! We look forward to reviewing your application!