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Veer Garg, a freshman at Springfield High School in Illinois, was selected as one of the top 300 junior innovators in the 2026 Thermo Fisher Junior Innovators Challenge. WAND-TV reported the recognition on Oct. 8, 2026. The project behind it is a machine-learning model that classifies chest X-rays as showing pneumonia or not. Garg built it as a science-fair project, and the figures about the project and the competition come from WAND-TV’s account rather than from independent verification.
What Garg was recognized for
Garg was named one of the top 300 junior innovators in the 2026 Thermo Fisher Junior Innovators Challenge, according to WAND-TV’s Oct. 8, 2026 “STEAM Minute” segment. The report presents the recognition as the result of a science-fair path: local fair, then state fair, then national selection. Garg’s project is the reason he entered that path, and it is the part of the story with the most technical content.
The chest X-ray project
Garg started the work in eighth grade for a science fair. He combined an interest in coding with an interest in science, and he trained machine-learning models to tell chest X-ray images showing pneumonia apart from images without it. WAND-TV reports that the models were trained on 2,400 images and reached more than 90% accuracy.
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That accuracy figure needs context. The report does not say where the images came from, how they were split between training and testing, or whether anyone outside the project checked the results. A high accuracy number on a fixed dataset shows that a model fits that data well; it does not show that the model would perform reliably on patients in a hospital, across different X-ray equipment, or in populations unlike the training set. The project should therefore be read as a student proof of concept, not as a diagnostic tool.
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Garg’s own description points in the same direction. He told WAND-TV, “I was really interested in Python, and I knew I would like to do a school science project relating to Python,” which explains why the project is built around a programming language rather than a medical device. He also described a future app: “I’m creating an app that will be available to health care specialists all over the world.” That is a stated aim. The report does not describe a released app, a regulatory submission, or a clinical trial.
How the selection process was described
WAND-TV describes a funnel that begins with local science fairs and narrows at each stage. The figures below are the ones the station reported. They have not been checked against competition records, so treat them as the station’s account.
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| Stage or detail | Figure reported by WAND-TV | Status of the figure |
|---|---|---|
| Projects in the nationwide starting pool | Roughly 60,000 local science-fair projects | Reported by WAND-TV; not independently confirmed |
| Projects showcased at the Illinois state fair | About 700 | Reported by WAND-TV; not independently confirmed |
| Illinois state-fair performers invited to the national challenge | Top 10% of state-fair performers | Reported by WAND-TV; not independently confirmed |
| Illinois projects selected nationally | About 70 | Reported by WAND-TV; not independently confirmed |
| Projects selected nationwide | About 2,000 | Reported by WAND-TV; not independently confirmed |
| Illinois students selected in the top group | Nine, with Garg as the only one from central Illinois | Reported by WAND-TV; not independently confirmed |
| Garg’s project model | 2,400 chest X-ray images; more than 90% accuracy | Reported by WAND-TV; methodology and independent validation not stated |
Note the difference between the two kinds of figure. The selection counts describe a competition structure, and they are useful for understanding how selective the stages were. The accuracy figure describes one model on one dataset, and it cannot be read as evidence of clinical performance.
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- Clinical readiness. Nothing in the report indicates the model has been tested on patient data or approved for use in diagnosis.
- Data provenance. The report does not say whether the 2,400 images were public, donated, or obtained through a medical institution.
- Evaluation method. The report does not explain how training and evaluation data were separated, so the >90% figure could reflect overlap or a narrow test set.
- Full competition roster. The 300-selection roster and the Illinois count were not published alongside the story in a form that could be checked here.
Garg’s stated next steps
Garg told WAND-TV he wants to extend the model to classify other lung diseases, and that his next project concerns myopia progression. Both are research intentions. Neither has been reported as underway with results. His longer-term ambition, expressed in the same interview, is a medical career: “I want to be a doctor because I would like to help as many people as I can and would like to make a positive impact on the world.”
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The bottom line on this story
Veer Garg’s recognition is a real, reported achievement: a Springfield High School freshman selected from a large pool of junior projects for a machine-learning project on chest X-rays. The accuracy and selection figures come from WAND-TV and should be cited as its reporting. The project is best understood as an early student experiment in medical image classification. Whether the model would hold up beyond its original dataset is a question this report does not answer.
For a broader view of the competition itself, the Thermo Fisher Scientific website is the primary source for the challenge’s rules and history, and the WAND-TV story is the primary source for the details of Garg’s selection: WAND-TV, “STEAM Minute: Springfield freshman named top 300 junior innovator” (Oct. 8, 2026).
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