Rigorous Math Education Helped This Teen Discover 1.5 Million Space Objects

Last updated: June 30, 2026

Education technology has built a trillion-dollar industry on one premise: make learning fun enough, and children will engage. Matteo Paz’s story is a direct challenge to that premise. The Pasadena, California teenager completed AP Calculus in 8th grade through his district’s rigorous Math Academy program, went on to study undergraduate mathematics, and used that foundation to build a machine-learning model that identified 1.5 million previously unknown objects in NASA’s space telescope archive. His peer-reviewed discovery, published in The Astronomical Journal in December 2024, won him first place and $250,000 in the 2025 Regeneron Science Talent Search, the nation’s oldest and most prestigious high school science competition. The education story here isn’t AI. It’s what happens when a child is given the mathematical rigor to make AI do something worth doing.

TL;DR

Matteo Paz, a Pasadena High School student, won $250,000 at the 2025 Regeneron Science Talent Search for discovering 1.5 million unknown objects in NASA's NEOWISE archive.
His algorithm VARnet used wavelet analysis and deep learning, tools he mastered through Pasadena Unified's accelerated Math Academy, which took him to AP Calculus BC in 8th grade.
By the time Paz joined Caltech's Summer Research Connection in summer 2023, he was studying undergraduate-level mathematics.
The peer-reviewed study published in The Astronomical Journal in December 2024 is already informing observations at the Vera C. Rubin Observatory.
The bigger lesson: mastery-based, rigorous math education produced a student who could design real science tools, not a student who could score points on an app.

A Pasadena teenager’s discovery of 1.5 million unknown space objects sparked global headlines. But the most important part of his story isn’t the discovery. It’s the education that made it possible.

Common questions

What is Math Academy and can my child access it?

Math Academy is a mastery-based accelerated math curriculum developed by Pasadena Unified School District and now available to families at mathacademy.com. It advances students at their own mastery pace, skips concepts already understood, and gates progress on demonstrated understanding. There is no gamification layer. Progress requires genuine competence, not accumulated points.

My child struggles with math. Does a story about a high achiever apply to them?

Yes, and the connection is neuroplasticity. Brain-imaging studies by Shaywitz at Yale and Temple at Stanford show that intensive, appropriate practice physically rewires the developing brain. Children who struggle with math are not children who cannot learn it. They are children who have not yet had the right kind of practice at the right level of challenge. A screener is a starting point, not a diagnosis. If your child might need formal accommodations such as an IEP or 504 plan, or you suspect a vision, hearing, or other medical cause, a professional evaluation is the route to those supports.

How do I know if a math program is building real skills or managing engagement?

Ask one question: can my child solve a problem in this subject that they have never seen demonstrated before? Genuine mastery transfers to new contexts. Programs that advance students via hints, points, and repetition without difficulty-gating often do not. Programs that gate progress on demonstrated understanding build something a child can take to genuinely novel problems.

Should my child focus on AI tools rather than math fundamentals?

Paz’s story answers this directly. VARnet required understanding wavelet analysis and Fourier transforms well enough to design a system that applied them correctly to novel data. AI tools amplify what a person already knows. A child with genuine mathematical mastery uses AI to do things that matter. A child who skipped the fundamentals to focus on the tools has less to amplify. The fundamentals are not the alternative to AI fluency. They are its prerequisite.

A Pasadena teen completed AP Calculus in 8th grade and went on to find 1.5 million space objects. The education story isn't the AI. It's what rigorous math mastery built. Tweet Now Follow Learning Success

What Matteo Paz Actually Built

By summer 2023, when Paz joined Caltech researcher Davy Kirkpatrick’s lab through the university’s Summer Research Connection program, he had already been accelerated years past his peers in mathematics. Math Academy, a Pasadena Unified School District program, had taken him through AP Calculus BC in 8th grade and into undergraduate-level coursework. That mathematical depth gave him the tools Kirkpatrick’s lab needed.

Paz developed VARnet, an algorithm applying wavelet analysis and deep learning to analyze time-series light data from NASA’s NEOWISE space telescope archive. The telescope’s database had been examined before, but Paz’s model caught patterns that conventional approaches had missed. VARnet flagged 1.5 million potential variable objects, including previously unidentified quasars, binary stars, and supernovae. The findings were published in The Astronomical Journal in December 2024, and the catalog is already informing observations at the Vera C. Rubin Observatory.

In April 2025, the Regeneron Science Talent Search awarded Paz its top prize of $250,000. The competition cited VARnet’s applicability beyond astronomy. Paz told Smithsonian Magazine the model works on “anything else that comes in a temporal format,” including stock market data and environmental signals.

Author Quote "

anything else that comes in a temporal format

What the coverage gets wrong

Most stories on Paz's discovery center on AI as the breakthrough, framing VARnet as the protagonist. But the machine learning model is the output of Paz's mathematical education, not its replacement. The educational story is that a mastery-based accelerated math program gave a teenager the mathematical vocabulary to design and interpret a novel algorithm. Coverage that leads with AI obscures the more replicable, more useful lesson: rigorous, mastery-focused math education produces students who can make AI do something genuinely important.

The Education Story the Coverage Missed

The popular version of this story is an AI story. Coverage from Smithsonian to the Daily Galaxy framed it as a teen’s AI discovering space objects, centering the machine learning model as the breakthrough. But VARnet didn’t teach itself wavelet analysis. Paz did. And he learned it because Math Academy gave him rigorous mathematical training, not a points system.

That distinction matters for how parents think about learning technology. Ed-tech platforms have spent two decades optimizing for engagement: points, streaks, mini-games, and adaptive hints designed to keep children on-screen long enough to absorb content. The research on this approach is unambiguous. Extrinsic reward systems produce shallow learning that evaporates when the rewards stop. Genuine mastery, built through appropriately challenging, mastery-gated practice, transfers to contexts no app ever demonstrated. Math Academy takes the opposite bet. No gamification layer. Mastery gates. Hard mathematics, accelerated for students ready to handle it.

The result is a student who could apply Fourier transforms and wavelet analysis to an actual NASA dataset at 16. Neuroplasticity research (Shaywitz at Yale, Temple at Stanford) shows that intensive, appropriate practice physically rewires the developing brain. Math Academy is exactly that kind of practice. What reads as a prodigy story is a story about what rigorous, mastery-based learning builds in a brain given the right tools and time. For parents asking what math program to pursue, the operative question isn’t which one their child finds most engaging. It’s which one builds competence they can take to a problem they’ve never seen before. Those are not the same thing. The core skills of math are worth understanding before choosing a program.

Key Takeaways:

Math First, AI Second: Paz's VARnet required genuine mastery of wavelet analysis and Fourier transforms — mathematical tools built through rigorous accelerated education, not gamified apps.

Mastery Rewires the Brain: Neuroplasticity research (Shaywitz, Yale; Temple, Stanford) shows intensive appropriate practice physically changes the developing brain. Math Academy is exactly that kind of practice.

The Question to Ask Every Program: Not whether your child is engaged, but whether they gain skills transferable to problems they have never seen demonstrated. Mastery passes that test.

What This Means for Your Child

The gap between gamified learning and mastery-based learning isn’t a pedagogy debate. It shows up in what a child can eventually do. Paz’s VARnet required understanding Fourier transforms well enough to design a system that used them correctly on novel data. A child who has accumulated points in a math app has none of those tools. A child who has worked through actual mathematics, struggled with it, and genuinely mastered it, does.

For parents, the practical question is what the learning program your child uses actually builds. Engagement metrics and time-on-platform tell you nothing useful. The right question is whether a child who finishes the program can bring those skills to bear on a problem they’ve never encountered before. That’s mastery. That’s what opens doors.

Math Academy is a district program in Pasadena, but its curriculum is available online and similar mastery-based programs exist. The broader principle: when you advocate for your child’s math education, ask whether the program builds genuine competence or manages engagement. The difference, compounded over years, is the distance between a child who uses tools and a child who builds them.

Every parent of a child who struggles with math has heard the same advice: find an app they like, make it fun, reduce the pressure. Matteo Paz’s story is what the opposite looks like. Rigorous practice, genuine mastery, real tools, and a teenager who ended up building something NASA couldn’t. The enemy isn’t hard math. It’s the gamification-first approach that decided engagement metrics were a suitable substitute for mathematical competence, and the children shortchanged as a result. If you want to understand where your child is in their math journey today, start with a free AI analysis of your child’s learning profile.