🇺🇸 Sharvi Mahajan

The design choice that defines NeuroDrive Alert is temporal. Rather than classifying a single moment as alert or drowsy, the system tracks fatigue as it accumulates, using the change over time as the predictive signal. That is a meaningfully harder machine-learning problem than snapshot classification — it requires modelling a trajectory rather than a state — and it is also the only version of the problem worth solving, because a warning that arrives simultaneously with the microsleep arrives too late.

Her entry into the competition was framed around what she could learn rather than what she had already built. "I entered the challenge to learn new skills and be exposed to innovative ideas that could significantly refine the real-world impact my project has," she wrote. "Under the guidance of a 3M mentor, I hope to bring my project to a whole new level and improve my model in order to be smoothly translated to a physical system." The gap she names — between a model that performs on recorded data and a device somebody can wear in a moving vehicle — is the one where most promising biosignal projects die.

Her command of the underlying instrument is not superficial. Asked to name her favourite invention of the last hundred years she chose the electroencephalogram, and explained why in terms a graduate student would recognise. "An EEG machine measures brainwave activity through a system of electrodes placed on the scalp. I find it absolutely amazing how an EEG can reflect even the tiniest of dynamic brainwave movement into a scan, showcasing its exceptional temporal resolution. The sheer amount of data that one can obtain from a singular EEG has the potential to shed light on the neural fingerprints of numerous neurological conditions." She singled out its non-invasiveness and time resolution as what make it suited to "tracking patterns over time, particularly in shorter time periods" — which is exactly the property her own system exploits.

Her stated favourite application of the technology is not fatigue detection at all. "My favorite thing about an EEG has to be its ability to aid significantly in the creation of Brain-Computer Interfaces," she wrote. "BCIs restore independence, mobility, and communication for individuals experiencing physical disabilities and the EEG machine's key role in the process of building a BCI truly intrigues me."

Her assigned 3M mentor is Stephanie Owen, an R&D Product Engineering Lab Manager for electronics within the company's Transportation and Electronics Business Group. The pairing addresses the precise weakness Sharvi identified in her own project: translating a trained model into hardware that works reliably in a vehicle is an electronics product-engineering problem, not a data-science one.

The competition, in its nineteenth year, drew entries from fifth through eighth graders across the United States, judged by 3M scientists and education leaders on creativity, scientific knowledge and communication effectiveness. The ten finalists work with mentors through the summer and gather on 12–13 October 2026 at the 3M Innovation Center in St. Paul, Minnesota, for live challenges and a final presentation, with $25,000 and the title of America's Top Young Scientist at stake. Sharvi's own horizon is longer: "a computational neuroscientist or founder of a tech startup," she wrote, describing a lifelong fascination with the brain and a parallel love of "building different models and testing different features or techniques to improve model performance." The quotation beside her name is from Kalpana Chawla, the first Indian-American woman in space: "The path from dreams to success does exist. May you have the vision to find it, the courage to get onto it, and the perseverance to follow it."