Won best paper award at NeurIPS Cooperative AI Workshop (2021) for research on Theory-Based Reinforcement Learning; led ML recommendation systems at Pinterest scaling to 100M daily impressions
MIT BS 2017-2021, MEng 2021-2022. GitHub joined 2018. If started MIT at 18 in 2017, born ~1999. Met co-founder Matt Stallone at MIT.
Education: MIT (Bachelor's in Computer Science and Brain & Cognitive Science; Master of Engineering in Computer Science), 2017-2022
Current position (2026)
Founder at Tweeks.io
Where the conditions came from
Three sources, read side by side
Each is placed on a −1 to 3 scale from documented evidence, and the three are never added together. A combined total would rank Jason Madeano against other people. Held apart, they explain why this path ran differently from another one—which is the only comparison this project supports.
The marble itself
What they brought
+2Tailwind
-10+1+2+3
What capability, drive, or early skill is documented in the person rather than their surroundings?
MIT BS in CS and Brain & Cognitive Science plus MEng. IBM-Watson Undergraduate Research and Innovation Scholar. Won best paper at NeurIPS Cooperative AI Workshop 2021. 2 years in MIT UROP program. Strong research ability with early publications.
Where it was dropped
What they were handed
+1Tailwind
-10+1+2+3
What money, family standing, network, or permission was already in place before the work began?
No specific family background information found. MIT admission and UROP participation suggest strong academic preparation but no evidence of family wealth or domain connections.
The shape of the track
What surrounded them
+2Tailwind
-10+1+2+3
What place, timing, institution, or peer group made the next step available?
MIT provided elite institutional access and UROP research opportunities. NeurIPS publication validated research quality. Pinterest offered industry ML experience at scale (100M daily impressions). Met co-founder Matt Stallone at MIT.
A −1 is a documented headwind, not a missing value; a 0 means the sources record nothing notable either way. Annotation confidence: Medium. These are analyst readings of what the sources record, not measurements of merit, talent, or effort. The twenty-two scored dimensions remain available inside the deeper research detail.
What moved through the conditions
Perseverance and luck stay visible—not scored.
Documented perseverance2 years in MIT UROP program.
This records repeated behaviour or recovery described by sources; it is not a grit or merit score.
Encounter luckMet co-founder Matt Stallone at MIT.
This is an unchosen opening or condition in the record, not an estimate of how much luck caused the outcome.
Describes the starting position, not what the person later made of it.
Cohort percentile: 64
02 Built or converted leverage
10/25 multiplying-capacity score
Strongest observed levers: Complementary team, Capital safety, Domain proximity.
Measures what was present, not whether it was inherited, earned, self-built, external, or mixed.
Cohort percentile: 68
03 Compounding trajectory
2 documented steps
The timeline below shows the sequence of work and transitions around the selected early milestone. It is evidence of a path, not proof that every step was necessary.
Milestone at age 24
04 Observed career standing
T3 · Domain-recognized
Notable and widely recognized within the domain. The tier summarizes documented career recognition through the data cutoff—not Jason Madeano's worth or future potential.
Each non-zero lever gets a best-supported origin, evidence signals, and confidence. Unresolved is the honest default when the biography cannot distinguish self-built, advantage-enabled, earned, external, or mixed.
Complementary team1/2
Advantage-enabledmedium confidence
One or more documented starting advantages plausibly enabled this lever.
A structural wave is external to the person, even when their position improved access to it.
Frontier geography (1/2)
Concentration intensity1/3
Unresolvedlow confidence
No current annotation distinguishes self-built, enabled, or earned origins for this lever.
No decisive linked signal
This is a bounded inference from the current annotations—not a claim about private effort, merit, or the percentage of Jason Madeano's outcome attributable to any origin.
Luck is not a leftover score.
Structural luck, Encounter luck, Event luck, Outcome variance can change every arrow in the path. This successful-only dataset cannot observe the near-identical paths that did not break through, so luck stays visible and unscored.
Within Founders / operators, Jason Madeano's starting-advantage total is at the 64th percentile. Separately, their built or converted leverage total is at the 68th percentile. Other T3 profiles average 5.3 / 24 starting advantage and 11.0 / 25 leverage. Similar scores appear in other tiers, so these figures describe positioning—not a cause.
Trajectory
· age 18
MIT (Bachelor's in Computer Science and Brain & Cognitive Science; Master of Engineering in Computer Science), 2017-2022
· age 24
Founded Tweeks.io
YC Winter 2025
Primary leverage engine
Technical depth
Scarce technical / intellectual depth
Secondary engine
Timing/platform wave
Built/converted leverage
10 / 25
evidence: Low
Built or converted leverage
Multiplying capacity documented later in the path. Measures what was present, not whether it was inherited, earned, self-built, external, or mixed.
Started serious reps before 20
0/1
Prior reps
1/3
Scarce skill depth
1/3
Native distribution
1/3
Elite ecosystem network
1/3
Complementary team
1/2
Structural wave / timing
1/3
Concentration intensity
1/3
Capital safety
1/2
Domain proximity
1/2
Starting-advantage scores (0–2 each)
Access or conditions documented near the beginning of the path. Zero means "no clear evidence in reviewed sources," not "advantage was absent."