During the opening keynote presentation, Carnegie Mellon’s Priya Narasimhan shared that she didn’t set out to build a global sports technology company. But one night in 2008 at a Pittsburgh Penguins game, watching from the nosebleed section, she realized how much of the action she was missing—angles, stats, insights that could make the experience richer. That moment led to a research project with her computer science students and ultimately to the launch of her startup YinzCam.

YinzCam started as an experiment to deliver real-time replays and stats directly to phones and has grown into a platform serving more than 160 professional teams around the world. Today, it powers apps, websites, AR features, and loyalty programs, continuing with its goal to keep sports fans connected beyond game day.

For Narasimhan, the fan’s perspective has always been the starting point. “At its core, the company is about personalization—turning data into experiences that deepen loyalty and engagement.”

And while the data now says 12 percent of fans actually experience games in a stadium, the bottom line is still the same: from broadcasts to merchandise, technology isn’t the end goal; the fan is. As Saj Cherian, chief of staff and head of Fanatics Ventures, explained, “If you’re in the consumer business, your relationship to your customer, the sports fan, is so important.” Trust isn’t just a box to check, he added,  “Trust … is a brand asset.” Something that takes years to build and moments to lose. 

“When you get into these numbers, you get into these analytics, it can get really impersonal pretty quickly,” said Julie Souza, global head of sports at Amazon Web Services. “I get people coming at me all the time, saying the numbers have ruined sports. If that’s what it’s coming across as—if it is alienating—we are doing it wrong.”

The takeaway? Sports is a business, they said, but emphasized its heart is people. Profits only follow when fans feel seen, respected, and genuinely part of the game.

But it isn’t just the technology that matters—it’s the people who build it. Behind every new tool or application are researchers, engineers, and students shaping how AI is applied to sports. In the Data Science & Analytics Across Sporting Ecosystems panel, speakers turned the spotlight on the next generation, offering practical advice on how students can prepare for a rapidly evolving work and technology landscape.

The advice from the panel was clear: Don’t limit yourself. “There are only so many teams that you can work for,” said Konstantinos Pelechrinis, professor of informatics & network systems at the University of Pittsburgh and a data analytics consultant for the Dallas Mavericks, “but there are so many other companies that work with these teams that can give you a foot in the door doing pretty interesting things with AI.” Instead of focusing narrowly on a league or franchise, he said students should think about the wider ecosystem of industries, data platforms, startups, and vendors that are shaping the field.

Equally important is skill-building. “I would focus on building skills rather than focusing on a really specific field,” Ozan Adiguzel, principal AI engineer at Finster AI, advised, stressing that coding, system design, and statistics are foundational across industries. 

While tools like Python, TypeScript, and even emerging languages such as Rust or Golang open doors, understanding the mathematics behind algorithms matters just as much. 

“Having a good foundation in math and statistics is important,” added Jorge Ortiz, associate professor of electrical and computer engineering at Rutgers University and CV/AI lead for the New York Yankees. “There's still verification and making sure that your code is working correctly.” 

Students who build strong skills in math, statistics, and coding — and demonstrate them through class projects or public portfolios — will find opportunities not only in sports but across industries that are eager for computer scientists.

The lightning talks from Columbia Engineering researchers highlighted cutting-edge research at the intersection of sports, health, technology, and human behavior. Computer Science Assistant Professor Yunzhu Li showed how hybrid modeling—combining physics-based equations with machine learning—can help robots and AI systems adapt to the unpredictable dynamics of sports. Parth Gami, a 5th-year biomedical engineering PhD student, introduced a noninvasive ultrasound method for tracking cardiovascular health in athletes, with potential for wearable fitness applications. Mechanical Engineering Professor Sunil Agrawal shared how his lab uses robotics to study and train human movement, from everyday actions to competitive sports, with promising results for improving performance and preventing injury. And finally, Assistant Professor Lydia Chilton’s talk on digital twins showed how large language models can simulate and anticipate human behavior at scale—whether in shopping, education, or event planning—with sports as the next frontier.

“As much as we are advancing algorithms and data models, the essence of this work is human,” said Eddie Mandhry, managing director at Columbia–Dream Sports AI Innovation Center. “Whether it’s athletes pushing their limits, fans connecting with the game, or researchers and engineers designing new tools, AI in sports is ultimately about enhancing human potential and experience.”

Lead Photo Caption: Keynote speaker Priya Narasimhan, professor at Carnegie Mellon and CEO at YinzCam, talks with Vishal Misra, vice dean of computing and AI at Columbia Engineering

Lead Photo Credit: Eileen Barroso

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