18-Year-Old Senior Is Rutgers’ Youngest 2025 Graduate

While most students his age are getting ready for college, Alex Geppert is preparing to start his doctoral degree

At the very top of Alex Geppert’s LinkedIn profile is a statement that reads like a challenge and a mission: “Technology is fast, and so am I.”

It’s not a boast – it’s a fair summary of a young scholar who, at just 18, is graduating from Rutgers with a double major in computer science and data science, a resume that includes hands-on robotics and AI research, and a fall admission to one of the top Ph.D. programs in artificial intelligence in the country.

Geppert enrolled in the School of Arts and Sciences at Rutgers-New Brunswick two years ago, after completing two associate degrees – one in mathematics, one in computer science – while still a student at Watchung Hills Regional High School. 

“I was taking classes at Raritan Valley Community College (RVCC) during afternoons and weekends,” he said. “By the time I finished high school, I had enough credits to start at Rutgers as a junior.”

An Early Fascination Turned Focus

Geppert’s path toward computer science took shape in middle school. During the early months of the COVID-19 lockdown, he stumbled across Deep Learning by Ian Goodfellow, a foundational text in artificial intelligence.

“I opened it and realized I didn’t follow any of it,” he said. “But it introduced me to the field and made me want to learn the math behind how it worked.”

He started with calculus and linear algebra on his own, then began formal coursework at RVCC. By his sophomore year of high school, he was taking advanced college-level classes like multivariable calculus and discrete mathematics. At 16, he graduated from high school having already completed the equivalent of two years of college. 

A Life Built Around Learning (and Rowing)

Geppert has always commuted to Rutgers from his home in Warren Township. “People ask why I live at home. It’s partly to save money,” he said, “but also because I have a great relationship with my parents. I spend the day on campus – classes, research, rowing – and come home to dinner and conversation.”

He didn’t expect to play sports in college, but that changed when Rutgers held its student involvement fair. Crew team members were on the lookout for tall recruits. “They spotted me, asked if I’d try out,” said Geppert, who is 6 foot 3. “I’d been a swimmer and lifeguard, so I figured I’d give it a shot.”

He made the team and stuck with it, despite the demanding schedule. Practice starts before 7 a.m., six days a week. “It’s intense,” he said. “But the guys on the team are some of my closest friends. A few of us study together, too. It’s a great support system.”

Turning Research into Real-World Tools

In his academic work, Geppert joined the Computational Brain Lab of Konstantinos Michmizos, whose research focus is on neuro-rehabilitation and neuro-inspired robotics. There, he developed an AI-based system that uses computer vision to recognize human hand movements and control a robotic hand in real time. The system relies on a deep neural network, which processes information in a way that mimics the human brain. 

“Working with Alex has been an exceptional experience,” Michmizos said. “His maturity and discipline in navigating the uncertainties of research are impressive. Interestingly, I only learned a few months ago that he is graduating at a very young age. I always considered him one of the top students in my lab, yet based on his behavior, I would have never guessed that he was so much younger than his peers. He is truly unique.”

This fall, Geppert will begin a fully funded doctoral program at the University of Wisconsin-Madison, where he’ll focus on multi-agent systems and generative AI. He’s especially interested in designing intelligent systems that can interpret information, make decisions and adapt to changing contexts – tools that can be integrated into real-world challenges rather than remain in theoretical models or simulations.

“I want to build AI that helps people reason through complex problems,” he said. “We talk a lot about what AI can do, but not enough about how we stay connected to it. That connection – understanding what the system is doing and why – is the part I care about most.”

He sees potential for AI to deepen human learning and decision making. “It’s not just about recommending a resource,” he said. “It’s about guiding someone through a concept, helping them test their understanding, and prompting the right questions at the right time.”

For Geppert, artificial intelligence isn’t a destination – it’s an infrastructure. 

“The goal isn't to make people more dependent on AI,” he said. “It’s to strengthen human thinking, not replace it.”