About TroponinIQ

The person behind the coach in your pocket

A physicist. A champion.
The same obsession.

Justin Harris earned a Master’s in Atomic Physics, won Mr. Michigan, and has coached IFBB pros for 25 years — proving hard science and the iron run on the same standard. TroponinIQ is what happened when he pointed that standard at the machine doing the answering, and ended up doing original AI research most software companies never touch.

R = base + Σ (wₖ·fₖ)— the equation his coaching AI is scored on, and the one he turned into a live diagnostic

One standard, three worlds

Physics, iron, and AI run the same loop: measure honestly, or you’re guessing.

A good physicist, a great prep coach, and a serious ML engineer all do the same thing — form a hypothesis, measure what actually happened, throw out what the data kills, and keep only what survives a check that could have failed. Justin has run that loop on particles, on physiques, and now on the retrieval system inside TroponinIQ. Same intolerance for hand-waving, three different substrates.

The physicist

The conservation-law thinking behind his AI work isn’t a costume. It’s his actual training. A physicist reaching for symmetry and conserved quantities to reason about a system is doing his job — not borrowing a metaphor.

  • M.S. Atomic Physics · B.S. Kinesiology (Alma College)
  • Three peer-reviewed physics papers · national seminar speaker

The coach

Every answer TroponinIQ gives is grounded in his own methodology — contest prep, offseason growth, peak week, nutrition, hormones — the material he’s used to bring pros to the stage. The knowledge base is his, page by page.

  • 2004 Mr. Michigan Overall · 2006 NPC Jr. USA’s SHW Champion · 2× All-American (football)
  • IFBB Pro Coach · 25+ years · dozens of pro athletes coached · Alma College Hall of Fame (2024)

The builder

He built a diagnostic for his own AI — rigorous enough to talk him out of a finding.

The engine that decides which piece of his knowledge answers your question is a reranker: it scores candidates with a weighted sum of signals. Tuning those weights is where most systems quietly go wrong — some weight-combinations are unidentifiable, so tuning them is noise. Justin built a tool, drawn from a Noether-type conservation lens, that computes those blind spots from live traffic before a single tuning run is wasted.

caught its own false positive

A signal pair looked entangled — a proper control showed it was an artifact, not real. The production map came back clean.

read-only · 0ms

Byte-identical to the live scoring path, and mutation-proven: break the math, a test fails.

arXiv-bound

Two research write-ups in progress — a theory paper on designed symmetries, and “Identifiability Audits for Additive Rerankers.”

Noether-type, before any physicist reaches for the pitchfork — the point isn’t a grand claim, it’s a tool that works on a real system. Which is exactly what you’d expect from someone who trained to distrust the impressive number until a control survives it.
Every claim here stays inside the lines of an independent review of his own research.

The coach in your corner is also the person doing the research.

Most AI is a wrapper around someone else’s model. This is someone’s life’s work — a physicist’s rigor, a coach’s methodology, and an engineer’s refusal to ship what he can’t stand behind, in one system you can talk to right now.