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Meet the NoVa high schoolers whose award-winning medtech products bring surgical solutions

Ben Choi and Pravalika Gayatri Putalapattu are both finalists in the national Regeneron Science Talent Search. We asked them to break down their projects and tell us their thoughts on what's next in tech.

Ben Choi, a finalist in the Regeneron Science Talent Search. (Courtesy photo)
An “ultra-low-cost” bionic arm. Machine learning to analyze live surgery videos. Ethics in AI, and quantum computing.

Once again, DMV high schoolers are proving that the future of tech is in pretty capable hands.

Two students from NoVa, Ben Choi and Pravalika Gayatri Putalapattu, were named finalists in 2022’s Regeneron Science Talent Search, which is put on by Regeneron Pharmaceuticals and the Society for Science. The 40 finalists, selected from nearly 2,000 entries, will each receive $25,000, and participate in a weeklong competition in March. The top 10 will nab prizes from $40,000 to $250,000.

Regeneron said finalists were selected based on the scientific rigor of their projects, as well as their potential to become world leaders and scientists. Previous finalists and winners have gone on to receive Nobel Prizes and MacArthur Foundation Fellowships and found huge science-based companies.

“This year’s finalists have shown resilience and dedication in the face of so many obstacles in their educational pursuits,” Society for Science CEO Maya Ajmera said in a statement. “From the COVID pandemic to the harsh realities of climate change, these students have demonstrated their leadership and commitment to STEM innovation. As our world continues to heal and find ways forward, these students will be the key to solving global challenges not only today, but in the future.”

Meet the DMV’s own finalists for 2022, and get a look at their winning projects:

Ben Choi — Potomac School (McLean, Virginia)

Finalist Ben Choi with his project. (Courtesy photo)

Ben Choi, a student at Potomac School in McLean, created an “ultra-low-cost” bionic arm for his Regeneron project. As current neuroprosthetics require invasive brain surgery and are often costly, he wanted to create something that would be more accessible. His project, which costs about $300 to produce, by his estimates, uses non-invasive electrodes that are placed on the forehead. After collecting thousands of brainwave data points, he trained an AI-based algorithm (featuring 23,000 lines of handwritten code) that can detect what the user intends to do next, like picking up an object.

Choi built his project primarily at home using a 3D printer, but he’s also already won a few awards with it and was able to do more work on it at MIT and Stony Brook University. Though he only began working on the project a few years ago, Choi told that he’s been interested in prosthetics since he watched a documentary on the topic in elementary school.

“I was just really captivated by it at the time. It just seemed really fascinating,” Choi said. “But at the same time, I remember it was a little bit alarming to see, one, just how expensive they were, but two, that they require this really, really risky open brain surgery. So even though I pursued other things over the years, that always really stuck with me.”

After participating in Regeneron, Choi said he’s hoping to pursue a clinical trial with his project, and “see it all the way through” into something that people can use in everyday life. As he moves forward with the project and his education, he has high hopes for how AI can impact the world, with some caveats.

“A lot of the most important questions I think are in the future, it’s not going to be a matter of what we can create, but rather what we should create in that regard,” Choi said. “It’s like Frankenstein: We can’t lose sight of the implications.”

Pravalika Gayatri Putalapattu — Thomas Jefferson High School for Science and Tech (Alexandria, Virginia)

Finalist Pravalika Gayatri Putalapattu. (Courtesy photo)

Alexandria, Virginia’s Pravalika Gayatri Putalapattu began working on her project in July 2020. The Dynamic Operating Room Companion (DORC, for short) is designed to help surgeons avoid preventable mistakes in gallbladder removal surgeries. DORC uses machine learning to analyze live surgery videos, tracking the movements of surgical tools and the patterns in use — whether the tools are there, how they’re moving, and the velocity and acceleration when in use, for instance. Based on the patterns, DORC’s AI tech will flag potential errors to surgeons.

Gayatri Putalapattu (who, fun fact, is also a semi-famous Wattpad author) told that she was inspired to create the system after a death in her family from a potentially preventable surgical mistake. It also gives her a personal connection to binary code, because she noted how slight changes could have made her life very different.

“Computer science runs on binary, with ones and zeros, I find it very fascinating that I think my family sort of runs the same way,” she said, explaining that her India-born father was able to immigrate to the US, thus allowing her to pursue projects like this, while his twin stayed behind.

Gayatri Putalapattu hopes to get her system implemented in hospitals, to be used by surgeons to prevent error. But in the meantime, she hopes the competition can help her with additional data collection from surgeons, and inspire other students to keep working on their own technical projects.

She also has her sights set on one of the up-and-comers of tech: the quantum industry. While she said she’s found a love of computer science over the past few years, especially with this project, she’s always been a lover of math. But she hopes to keep on with quantum in the future — especially, as she noted, because there’s still a lot of algorithmic and other math-based work to do.

“Quantum computing is still in its infancy, but I think it has a power to completely obliterate classical computing limits, and just solve problems so much faster, and do so many more things,” Gayatri Putalapattu said.

The students will compete March 9 through 16.

See the full list of finalists

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