理工科升学规划
The core of STEM admissions is a legible evidence chain — proof that a student's work grew, in an unbroken line, from the fundamentals to a deep, customized project that is genuinely their own. We plan that arc the way we coach a derivation: rationally, in order, each step earning the next — now with AI to help a student surface real interests and build the activities that show them. Honest data, curated summers, and essay coaching sit underneath.
The strongest STEM application is not a pile of achievements — it is a traceable line. Foundations lead to harder work; harder work leads to a customized project the student genuinely chased. An admissions reader should be able to follow that line from beginning to end and see, at a glance, that the growth is real and the interest is the student's own. Building that chain — deliberately, in order — is the core of what we do.
We plan it the way we coach a derivation: rationally, step by step, each step earning the next. Where is the student now, what is the honest next move, and the one after that — so that by senior year the record reads as a single argument rather than a scramble of unrelated lines. A student who can show that their achievement is 有迹可循 — that it has a visible, followable origin — is far more convincing than one with a taller but disconnected list.
Data keeps us honest about the target. Every school begins as a row in its Common Data Set — the standardized, audited filing, not the brochure — so the list is built on real score bands and admission factors, not rumor. But that is one supporting tool, not the thesis: the numbers only say what is competitive; the evidence chain is what makes a student interesting.
We start from each school's Common Data Set — admit rate, score bands, the admission-factor matrix, financial-aid reality — and translate it into what it actually means for one student's profile. The Yale walkthrough on our Data & Insights page is the method, shown in full.
A reach / match / safety list corrected for test-policy bias and TOEFL gating, weighted toward programs where a student's STEM strength — and the evidence chain behind it — is read as a positive rather than a coin flip.
From a curated database of elite STEM summer programs — Harvard SSP, MIT MITES & Beaver Works, Stanford, UPenn ESAP, JHU EEI, CMU SAMS, and more — matched to grade, deadline, language requirement, and intent.
Working only from each school's official supplemental prompts, we coach the argument and the voice — never a template, never a translation-tool cadence that readers discount on sight.
A junior-to-senior-year timeline — testing windows, early vs. regular deadlines, CSS Profile, the F-1 interview — so nothing that matters is discovered a week too late.
Planning here is AI-assisted at every stage — not to replace a student's judgment, but to multiply their curiosity. We use current AI tools to help a student find the questions worth chasing, turn them into a real, buildable plan, and keep the work moving. It is a force multiplier on the student's own effort, and we are explicit about where it stops.
Our differentiator is original, statistical analysis of the 2024–25 Common Data Set universe — 135 institutions, read line by line. Four findings shape almost every list we build.
Test-required schools see a submission rate ~1.7× higher than test-optional ones (57% vs. 34%). A test-optional school's published band reflects only the strong third who chose to submit — so the true admit floor sits 30–60 points below the printed 25th percentile.
At STEM-leaning schools the Math 75th percentile runs 40–50 points above EBRW. For applicants from a Chinese math curriculum who score 780–800 reliably, that asymmetry is a structural advantage — strongest where math is a signal, not a saturated baseline.
Despite the folklore of "you need a 100," the median hard TOEFL minimum across 100 schools is 80. The binding constraint is real but lower and more school-specific than most families assume — we map it tier by tier.
A section-by-section walkthrough of a Common Data Set, using Yale as the worked example — admit math, the C7 factor matrix, the C9 score profile, and the financial-aid section that quietly matters most.
A curated, verified directory of roughly fifty elite STEM summer programs — drawn from a database of 412 programs and re-checked for current dates, deadlines, grade eligibility, and language requirements. A sample of the flagship engineering and science offerings:
| School | Program | Deadline | Focus |
|---|---|---|---|
| Harvard | Secondary School Program (SSP) | Feb 11 | Engineering science, CS & math for credit |
| MIT | MITES Summer · Beaver Works (BWSI) | Feb 1 · Mar 31 | Intensive research; robotics, AI, aerospace |
| Yale | YYGS — Innovations in Science & Tech | Jan 14 | Interdisciplinary STEM |
| Stanford | Pre-Collegiate · Biomedical Engineering | Mar 13 | Medical technology & neural interfaces |
| UPenn | Engineering Summer Academy (ESAP) | Mar 1 | Biotech, nanotech, computer graphics |
| JHU | Explore Engineering Innovation (EEI) | Mar 14 | First-year engineering, simulated |
| CMU | Summer Academy for Math & Science (SAMS) | March | STEM project learning & research |
| Princeton | Algorithmic Thinking (PACT) | Rolling | Theoretical CS & algorithms |
Essays are one of the main things we do — the one place a STEM file argues in prose instead of numbers. We keep a full method for it: the archetypes behind almost every supplemental prompt, a step-by-step process, and an internal database of official prompts for 100+ institutions, refreshed each cycle. The full Essay Guide lays it out in detail.
We coach essays the way we coach a derivation: find the argument, then set it down cleanly in the student's own voice. We maintain a reference of the official supplemental prompts across 131 institutions — word limits, prompt counts, the question behind the question — and work strictly from those.
We do not hand a student a model essay to imitate, and we do not republish anyone else's article corpus. Admissions readers see thousands of template-cadenced, translation-tool essays a year and discount them on sight — so we never make one.
Outcomes are presented as anonymized archetypes — field, method, and venue only. We publish no names, no scores, and no application records.
A visual results gallery is in preparation. Until then, outcomes are described in text only, fully anonymized.
The numbers tell you what is competitive. The application tells a reader whether you are interesting.
— The Tian2 EditorsA free editorial showcase. We share method and analysis openly; there is no storefront here.